Author name cluster

Tin Lun Lam

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50 papers

2 author rows

ICRA Conference 2025 Conference Paper

Configuration-Adaptive Visual Relative Localization for Spherical Modular Self-Reconfigurable Robots

Yuming Liu
Qiu Zheng
Yuxiao Tu
Yuan Gao 0024
Guanqi Liang
Tin Lun Lam

Spherical Modular Self-reconfigurable Robots (SMSRs) have been popular in recent years. Their Self-reconfigurable nature allows them to adapt to different en-vironments and tasks, and achieve what a single module could not achieve. To collaborate with each other, relative localization between each module and assembly is crucial. Existing relative localization methods either have low accuracy, which is unsuit-able for short-distance collaborations, or are designed for fixed-shape robots, whose visual features remain static over time. This paper proposes the first visual relative localization method for SMSRs. We first detect and identify individual modules of SMSRs, and adopt visual tracking to improve the detection and identification robustness. Using an optimization-based method, tracking result is then fused with odometry to estimate the relative pose between assemblies. To deal with the non-convexity of the optimization problem, we adopt semi-definite relaxation to transform it into a convex form. The proposed method is validated and analysed in real-world experiments. The overall localization performance and the performance under time-varying configuration are evaluated. The result shows that the relative position estimation accuracy reaches 2%, and the orientation estimation accuracy reaches 6. 64°, and that our method surpasses the state-of-the-art methods.

ICRA Conference 2025 Conference Paper

Enhancing Connection Strength in Freeform Modular Reconfigurable Robots Through Holey Sphere and Gripper Mechanisms

Peiqi Wang
Guanqi Liang
Da Zhao
Tin Lun Lam

Freeform modular self-reconfigurable robot (MSRR) systems overcome traditional docking limitations, enabling rapid and continuous connections between modules in any direction. Recent advancements in freeform MSRR technology have significantly enhanced connectivity and mobility. However, limitations in connector strength and operational efficiency in existing designs restrict performance. This paper proposes a rigid freeform connector and a rigid magnetic track design to improve the connection and motion performance of the SnailBot. Each SnailBot is equipped with a multi-channel rope-driven gripper, a metal spherical shell with densely distributed circular holes on the back, and a rigid chain design conforming to the spherical surface. This combination allows each SnailBot to move precisely along the surface of a peer, facilitated by the ferromagnetic spherical shell and magnetic track. The integration of the gripper and spherical shell hole array provides robust inter-module connections in any position and orientation. The effectiveness of these designs has been validated through a series of experiments and analyses, demonstrating improved connection and motion performance in the SnailBot dual-mode connector system and expanding its potential applications and functional capabilities.

IROS Conference 2025 Conference Paper

Human-Robot Cooperative Heavy Payload Manipulation based on Whole-Body Model Predictive Control

Ning Wang
Shuo Liu
Tin Lun Lam
Tianwei Zhang 0002

Human-robot collaborative manipulation with mobile, multiple manipulators is crucial for expanding robotic applications, requiring precise handling of coupled force-position constraints between partners. Current systems, however, exhibit end-effector oscillations and instability during dynamic interactions. To overcome these limitations, this work develops a collaborative framework integrating a collaborative controller and a whole-body controller. The collaborative controller employs the object’s center-of-mass dynamics model with real-time contact forces and motion states to predict trajectories while coordinating with an attitude stabilization controller to adjust the desired end-effector poses. The whole-body controller utilizes model predictive control to generate coordinated motions that strictly follow pose commands from the collaborative controller, ensuring stable transportation. Simulation and physical experiments validate the proposed framework’s effectiveness in real-world scenarios.

IROS Conference 2025 Conference Paper

MODUR: A Modular Dual-reconfigurable Robot

Jie Gu
Tin Lun Lam
Chunxu Tian
Zhihao Xia
Yongheng Xing
Dan Zhang 0006

Modular Self-Reconfigurable Robot (MSRR) systems are a class of robots capable of forming higher-level robotic systems by altering the topological relationships between modules, offering enhanced adaptability and robustness in various environments. This paper presents a novel MSRR called MODUR, featuring dual-level reconfiguration capabilities designed to integrate reconfigurable mechanisms into MSRR. Specifically, MODUR can perform high-level self-reconfiguration among modules to create different configurations, while each module is also able to change its shape to execute basic motions. The design of MODUR primarily includes a compact connector and scissor linkage groups that provide actuation, forming a parallel mechanism capable of achieving both connector motion decoupling and adjacent position migration capabilities. Furthermore, the workspace, considering the interdependent connectors, is comprehensively analyzed, laying a theoretical foundation for the design of the module’s basic motion. Finally, the motion of MODUR is validated through a series of experiments.

NeurIPS Conference 2025 Conference Paper

PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching

WANG Yun
Junjie Hu
Qiaole Dong
Yongjian Zhang
Yanwei Fu
Tin Lun Lam
Dapeng Wu

Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users. Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner. Previous methods attempt to address this by aggregating spatio-temporal information but face a fundamental trade-off: limited temporal modeling provides only modest gains, whereas capturing long-range dependencies significantly increases computational cost. To address this limitation, we introduce a memory buffer for modeling long-range spatio-temporal consistency while achieving efficient dynamic stereo matching. Inspired by the two-stage decision-making process in humans, we propose a Pick-and-Play Memory (PPM) construction module for dynamic Stereo matching, dubbed as PPMStereo. PPM consists of a pick process that identifies the most relevant frames and a play process that weights the selected frames adaptively for spatio-temporal aggregation. This two-stage collaborative process maintains a compact yet highly informative memory buffer while achieving temporally consistent information aggregation. Extensive experiments validate the effectiveness of PPMStereo, demonstrating state-of-the-art performance in both accuracy and temporal consistency. Codes are available at \textcolor{blue}{https: //github. com/cocowy1/PPMStereo}.

ICRA Conference 2025 Conference Paper

Topology-Based Visual Active Room Segmentation

Chenyu Bao
Junjie Hu 0003
Qiu Zheng
Tin Lun Lam

Room segmentation plays a significant role in scene understanding, semantic mapping, and scene coverage for robots navigating in real-world indoor environments. However, most previous works take a passive segmentation that requires a complete and uncluttered grid map as input, often resulting in lower segmentation accuracy and cannot be deployed in unknown environments. In this paper, we propose an active room segmentation framework that can enable a robot to incrementally and autonomously perform room segmentation in cluttered indoor environments. Our framework consists of three key components: i) a door extraction module where a visual semantic feature, specifically, door, is extracted to better identify rooms in cluttered environments, ii) a within-room exploration module that detects frontiers within the currently exploring room, and iii) a topological module that represents connectivity between rooms and determines next room for exploration. We show through experiments that the proposed method depicts two distinct advantages against existing methods in segmentation accuracy and autonomy. The code is available at https://github.com/FreeformRobotics/Active_room_segmentation.

ICRA Conference 2025 Conference Paper

Transferring Visual Knowledge: Semi-Supervised Instance Segmentation for Object Navigation Across Varying Height Viewpoints

Qiu Zheng
Junjie Hu 0003
Yuming Liu
Zengfeng Zeng
Fan Wang
Tin Lun Lam

The object navigation task requires robots to understand the semantic regularities in their environments. However, existing modular object navigation frameworks rely on instance segmentation models trained at fixed camera height viewpoints, limiting generalization performance and increasing labeling costs for new height viewpoints. To tackle this issue, we propose a semi-supervised method that transfers knowledge from a source height to a target height, minimizing the need for additional labels. Our approach introduces three key innovations: i) a projection policy to enhance the teacher model's detection capabilities at the target height, ii) a dynamic weight mechanism that emphasizes high-confidence pseudo-labels to reduce overfitting, and iii) a prototype contrast transferring method to transfer knowl-edge effectively. Experiments on the Habitat- Matterport 3D (HM3D) dataset show our method outperforms state-of-the-art semi-supervised techniques, improving both segmentation accuracy and navigation performance. The code is available at: https://github.com/FreeformRobotics/TransferKnowledge.

IROS Conference 2024 Conference Paper

Energy Sharing Mechanism for Freeform Robots Utilizing Conductive Spherical Sliding Surfaces

Xinzhuo Li
Yuxiao Tu
Guanqi Liang
Di Wu 0069
Tin Lun Lam

Energy sharing among modular robots enables sustainable operation of the system by maintaining energy balance among the modules. In this paper, we propose a novel energy sharing mechanism for FreeSN, a modular self-reconfigurable robot consisting of node and strut modules. Utilizing the feature that our modules are connected in a face-to-face manner, our method successfully establishes an energy sharing channel at almost any point on a sphere by placing transmission intermediaries at the interfacing face between modules, which is facilitated by the combination of brush contact and shell decomposition. Such mechanism also allows the utilization of the node module’s inner space for extra energy storage. A prototype of this energy sharing system has been implemented on FreeSN and rigorously tested. Our findings indicate that energy sharing is reliably established between modules; for strut modules positioned randomly on a node module’s surface, the probability of forming a valid connection is 56. 6%. With orientation adjustment, a connection is achievable at nearly any position on the sphere, barring a few exceptional points. As a result, the operational endurance of the strut modules, which provide all the driving forces in the system, is markedly enhanced. This technique also holds potential for broader application across other freeform robotic platforms that incorporate conductive spherical surfaces for sliding connections.

ICRA Conference 2024 Conference Paper

Meta-Reinforcement Learning Based Cooperative Surface Inspection of 3D Uncertain Structures using Multi-robot Systems

Junfeng Chen
Yuan Gao 0024
Junjie Hu 0003
Fuqin Deng
Tin Lun Lam

This paper presents a decentralized cooperative motion planning approach for surface inspection of 3D structures which includes uncertainties like size, number, shape, position, using multi-robot systems (MRS). Given that most of existing works mainly focus on surface inspection of single and fully known 3D structures, our motivation is two-fold: first, 3D structures separately distributed in 3D environments are complex, therefore the use of MRS intuitively can facilitate an inspection by fully taking advantage of sensors with different capabilities. Second, performing the aforementioned tasks when considering uncertainties is a complicated and time-consuming process because we need to explore, figure out the size and shape of 3D structures and then plan surface-inspection path. To overcome these challenges, we present a meta-learning approach that provides a decentralized planner for each robot to improve the exploration and surface inspection capabilities. The experimental results demonstrate our method can outperform other methods by approximately 10. 5%-27% on success rate and 70%-75% on inspection speed.

EAAI Journal 2024 Journal Article

Text-guided Graph Temporal Modeling for few-shot video classification

Fuqin Deng
Jiaming Zhong
Nannan Li
Lanhui Fu
Bingchun Jiang
Yi Ningbo
Feng Qi
He Xin

Large-scale pre-trained models and graph neural networks have recently demonstrated remarkable success in few-shot video classification tasks. However, they generally suffer from two key limitations: i) the temporal relations between adjacent frames tends to be ambiguous due to the lack of explicit temporal modeling. ii) the absence of multi-modal semantic knowledge in query videos results in inaccurate prototypes construction and an inability to achieve multi-modal temporal alignment metrics. To address these issues, we develop a Text-guided Graph Temporal Modeling (TgGTM) method that consists of two crucial components: a text-guided feature refinement module and a learnable Query text-token contrastive objective. Specifically, the former leverages the Temporal masking layer to guide the model in learning temporal relationships between adjacent frames. Additionally, it utilizes multi-modal information to refine video prototypes for comprehensive few-shot video classification. The latter addresses the feature discrepancy between multi-modal support features and single-modal query features by aligning a learnable Query text-token with corresponding base class text descriptions. Extensive experiments on four commonly used benchmarks demonstrate the effectiveness of our proposed method, which achieves mean accuracies of 54. 4%, 80. 3%, 91. 9%, and 96. 2% for 5-way 1-shot classification on SSV2-Small, HMDB51, Kinetics, and UCF101, respectively. These results are superior compared to existing state-of-the-art methods. A detailed ablation showcases the importance of learning temporal relationships between adjacent frames and obtaining Query text-token. The source code and models will be publicly available at https: //github. com/JiaMingZhong2621/TgGTM.

IROS Conference 2024 Conference Paper

Vision-Language Model-based Physical Reasoning for Robot Liquid Perception

Wenqiang Lai
Tianwei Zhang 0002
Tin Lun Lam
Yuan Gao 0024

There is a growing interest in applying large language models (LLMs) in robotic tasks, due to their remarkable reasoning ability and extensive knowledge learned from vast training corpora. Grounding LLMs in the physical world remains an open challenge as they can only process textual input. Recent advancements in large vision-language models (LVLMs) have enabled a more comprehensive understanding of the physical world by incorporating visual input, which provides richer contextual information than language alone. In this work, we proposed a novel paradigm that leveraged GPT-4V(ision), the state-of-the-art LVLM by OpenAI, to enable embodied agents to perceive liquid objects via image-based environmental feedback. Specifically, we exploited the physical understanding of GPT-4V to interpret the visual representation (e. g. , time-series plot) of non-visual feedback (e. g. , F/T sensor data), indirectly enabling multimodal perception beyond vision and language using images as proxies. We evaluated our method using 10 common household liquids with containers of various geometry and material. Without any training or fine-tuning, we demonstrated that our method can enable the robot to indirectly perceive the physical response of liquids and estimate their viscosity. We also showed that by jointly reasoning over the visual and physical attributes learned through interactions, our method could recognize liquid objects in the absence of strong visual cues (e. g. , container labels with legible text or symbols), increasing the accuracy from 69. 0%—achieved by the best-performing vision-only variant—to 86. 0%.

IROS Conference 2023 Conference Paper

FPECMV: Learning-Based Fault-Tolerant Collaborative Localization Under Limited Connectivity

Rong Ou
Guanqi Liang
Tin Lun Lam

Collaborative localization (CL) has garnered substantial attention in the field of robotics in recent years. Nonetheless, conventional CL algorithms have faced challenges when dealing with practical issues such as spurious sensor data and limited or discontinued observation and communication in real-world settings. This paper proposes a fault-tolerant practical estimated cross-covariance minimum variance update method (FPECMV) designed to tackle these challenges under limited connectivity. The proposed algorithm uses a CNN-based method to evaluate confidence, along with a fault isolation module to identify faults and manage spurious data in real time. The proposed fault isolation module utilizes relative measurement information that randomly occurs, without requiring high observation and communication prerequisites. Notably, the algorithm takes into account correlations among agents to maintain consistency in localization filters and attain accurate localization despite constraints posed by limited connectivity. To evaluate the performance of the proposed algorithm, experiments were conducted in a collaborative multi-robot environment with spurious sensor data and limited connectivity, using both the BULLET simulation and physical mobile robots. The experimental results indicate that the overall localization performance of the proposed algorithm is improved by 21. 0% compared to the state of the art. The experiment results demonstrate the effectiveness of our algorithm in localizing group agents in challenging and intricate scenarios with limited connectivity and spurious sensor data.

IROS Conference 2022 Conference Paper

AB-Mapper: Attention and BicNet based Multi-agent Path Planning for Dynamic Environment

Huifeng Guan
Yuan Gao 0024
Min Zhao
Yong Yang
Fuqin Deng
Tin Lun Lam

Multi-agent path finding in dynamic environments is of great academic and practical value for multi-robot systems in the real world. To improve the effectiveness and efficiency of the learning process during path planning in dynamic environments, we introduce an algorithm called Attention and BicNet based Multi-agent path planning with effective reinforcement (AB-Mapper) under the actor-critic reinforcement learning framework. In this framework, on one hand, we design an actor-network that can utilize the BicNet with communication function to achieve the intra-team coordination. On the other hand, we propose a critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by considering the behaviours of its surrounding agents. Compared with the SOTA method Mapper in crowded environments with dynamic obstacles, our AB-Mapper is more effective (90. 27±0. 06% vs. 61. 65±13. 90% in terms of mean success rate) in solving the general multi-agent path finding problem.

ICRA Conference 2022 Conference Paper

Abnormal Occupancy Grid Map Recognition using Attention Network

Fuqin Deng
Hua Feng
Mingjian Liang
Qi Feng
Ningbo Yi
Yong Yang
Yuan Gao 0024
Junfeng Chen

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural network with novel attention mechanism modules. We propose an effective channel and spatial Residual Squeeze-and-Excitation (csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiments with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested a few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96. 23% for abnormal occupancy grid map recognition.

ICRA Conference 2022 Conference Paper

Energy Sharing Mechanism for a Freeform Robotic System - FreeBOT

Guanqi Liang
Yuxiao Tu
Lijun Zong
Junfeng Chen
Tin Lun Lam

Energy sharing in modular self-reconfigurable robots ensures the energy balance of the modules, thus allowing the system to work sustainably. This paper proposes an energy sharing mechanism for a novel modular self-reconfigurable robot that allows free connections among modules, termed as FreeBOT, such that each FreeBOT can share energy with peers through surface contact. Corresponding energy sharing rules are proposed to achieve an energy sharing network structure without invalid components. As alternative choices, several types of networks subjected to the above requirements are provided, which also maximize the number of FreeBOTs joining to share energy. We implement and test the prototype of the energy sharing mechanism on FreeBOT. The experimental results show that the mechanism can effectively achieve energy sharing among FreeBOTs.

IROS Conference 2022 Conference Paper

Fast and Comfortable Interactive Robot-to-Human Object Handover

Chongxi Meng
Tianwei Zhang 0002
Tin Lun Lam

Transferring tools and objects to human hands is an important ability of collaborative robots. Most of the existing approaches focus on handover affordance, however, the comfort of receiving objects with human hands is often neglected. In this paper, we use advanced deep learning models to pre-generate handover target configurations that are convenient for human grasping based on the characteristics of the objects and tools, and then the robot grasps and passes the objects to the human. Experimental results on a mobile collaborative robot show that our proposed framework can robustly and efficiently deliver different shapes and types of objects to a human hand of any pose within the robot's field of view in a target pose that is convenient for grasping and can quickly deliver objects to a new target location even after the human hand moves to a new position.

ICRA Conference 2022 Conference Paper

FreeSN: A Freeform Strut-node Structured Modular Self-reconfigurable Robot - Design and Implementation

Yuxiao Tu
Guanqi Liang
Tin Lun Lam

This paper proposes a novel freeform strut-node structured modular self-reconfigurable robot (MSRR) called FreeSN, consisting of strut and node modules. A node module is mainly a low-carbon steel spherical shell. A strut module contains two freeform connectors, which provide strong magnetic connections and flexible spherical motions. The FreeSN system shares the benefits of freeform connection and strut-node structures. The freeform connection brings good adaptability to the environment. The triangle substructures inside the system configuration significantly improve the structural stability. The parallel execution of module motions can superpose the module capabilities and makes the system more scalable. The modules can combine these robot features by selecting the system configuration and better fit different circumstances and tasks. Four demonstrations, including assembly, obstacle crossing, transportation, and object manipulation, are designed to show the capabilities of the FreeSN system in different aspects. The results show the great performance and versatility of this MSRR system.

ICRA Conference 2022 Conference Paper

SnailBot: A Continuously Dockable Modular Self-reconfigurable Robot Using Rocker-bogie Suspension

Da Zhao
Tin Lun Lam

This paper proposes a novel modular self-assembling, self-reconfiguring robot with the 3D continuous dock called “SnailBot”. SnailBot mainly consists of a spherical ferromagnetic shell and a six-wheel rocker chassis with embedded magnets. Unlike many other existing modular self-reconfigurable robots with fixed docking locations, SnailBot uses the 3D continuous dock to attach to its peers regardless of alignment. This freeform docking mechanism can greatly improve the efficiency of self-reconfiguration and reduce docking failures because there is nearly no constraint in the location of the connector. Compared with the existing freeform MSRR, SnailBot can form a more structurally stable connection to its peers without loss of connection efficiency. Owing to the excellent obstacle crossing ability of the rocker-bogie suspension, the robot can freely crawl on other modules in the form of a sliding sphere. Experiments demonstrate the basic actions of a single module and some applications of SnailBots, such as a manipulator.

IROS Conference 2022 Conference Paper

Speed up of Wave-Driven Unmanned Surface Vehicle Using Passively Transformable Two-segment Foils

Lyucheng Xie
Hongzheng Cui
Tin Lun Lam

For wave-driven unmanned surface vehicles (WUSVs), utilizing oscillating foils is the most straightforward and common wave energy conversion mechanism. Improving the thrust of the oscillating foil to increase its speed can help WUSVs improve their maneuverability and shorten the completion of ocean missions. This paper proposes a novel transformable two-segment foil, improving the wave energy-converting efficiency to provide more average thrust in every wave cycle. We estimate their working effectiveness numerically with a simple model to verify that the design enhances foils' thrust force. The thrust enhancement was further confirmed by computational fluid dynamic (CFD) simulations, and we estimated the suitable values of parameters of the foils in several different common sea conditions in coastal waters by CFD simulations. We design and make two wave gliders with traditional and transformable two-segment foils and finish the speed enhancement experiments. The speed enhancement is verified, and transformable two-segment foils can increase the speed of WUSVs by 10% in similar sea conditions in experiments.

IROS Conference 2021 Conference Paper

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

Tianwei Zhang 0002
Huayan Zhang
Xiaofei Li 0001
Junfeng Chen
Tin Lun Lam
Sethu Vijayakumar

Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can be efficiently obtained by direction of sound arrival (DoA) estimation, but the depth estimation is difficult. Therefore, in this paper, we propose a novel audio-visual fusion approach that fuses sound source direction into the RGB-D image and thus removes the effect of dynamic obstacles on the multi-robot SLAM system. Experimental results of multirobot SLAM in different dynamic environments show that the proposed method uses very small computational resources to obtain very stable self-localization results.

IROS Conference 2021 Conference Paper

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Liguang Zhou
Jun Cen
Xingchao Wang
Zhenglong Sun 0001
Tin Lun Lam
Yangsheng Xu

Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/FreeformRobotics/BORM.

IROS Conference 2021 Conference Paper

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

Fuqin Deng
Hua Feng
Mingjian Liang
Hongmin Wang
Yong Yang
Yuan Gao 0024
Junfeng Chen
Junjie Hu 0003

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2. 6% in global mAcc and +0. 8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.

ICRA Conference 2021 Conference Paper

Graph Convolutional Network based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array

Yuxiao Tu
Guanqi Liang
Tin Lun Lam

Modular self-reconfigurable robotic (MSRR) systems are potentially more robust and more adaptive than conventional systems. Following our previous work where we proposed a freeform MSRR module called FreeBOT, this paper presents a novel configuration detection system for FreeBOT using a magnetic sensor array. A FreeBOT module can be connected by up to 11 modules, and the proposed configuration detection system can locate a variable number of connection points accurately in real-time. By equipping FreeBOT with 24 magnetic sensors, the magnetic field density produced by magnets and steel spherical shells can be monitored. The connectable area is split into 199 non-uniform regions, including 84 uniform regions. Using a Graph Convolutional Network (GCN) based algorithm, the connection points can be located accurately under ferromagnetic environments. The system can locate a variable number of connection points for such a region division with only single connection point training data. Finally, the localization algorithm can run faster than 40 Hz on FreeBOT. With the real-time configuration detection system, the FreeBOT system has the potential to reconfigure automatically and accurately.

ICRA Conference 2021 Conference Paper

Long-Range Hand Gesture Recognition via Attention-based SSD Network

Liguang Zhou
Chenping Du
Zhenglong Sun 0001
Tin Lun Lam
Yangsheng Xu

Hand gesture recognition plays an essential role in the human-robot interaction (HRI) field. Most previous research only studies hand gesture recognition in a short distance, which cannot be applied for interaction with mobile robots like unmanned aerial vehicles (UAVs) at a longer and safer distance. Therefore, we investigate the challenging long-range hand gesture recognition problem for the interaction between humans and UAVs. To this end, we propose a novel attention-based single shot multibox detector (SSD) model that incorporates both spatial and channel attention for hand gesture recognition. We notably extend the recognition distance from 1 meter to 7 meters through the proposed model without sacrificing speed. Besides, we present a long-range hand gesture (LRHG) dataset collected by the USB camera mounted on mobile robots. The hand gestures are collected at discrete distance levels from 1 meter to 7 meters, where most of the hand gestures are small and at low resolution. Experiments with the self-built LRHG dataset show our methods reach the surprising performance-boosting over the state-of-the-art method like the SSD network on both short-range (1 meter) and long-range (up to 7 meters) hand gesture recognition tasks.

IROS Conference 2021 Conference Paper

Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition

Bo Miao
Liguang Zhou
Ajmal Mian
Tin Lun Lam
Yangsheng Xu

Accurate perception of the surrounding scene is helpful for robots to make reasonable judgments and behaviours. Therefore, developing effective scene representation and recognition methods are of significant importance in robotics. Currently, a large body of research focuses on developing novel auxiliary features and networks to improve indoor scene recognition ability. However, few of them focus on directly constructing object features and relations for indoor scene recognition. In this paper, we analyze the weaknesses of current methods and propose an Object-to-Scene (OTS) method, which extracts object features and learns object relations to recognize indoor scenes. The proposed OTS first extracts object features based on the segmentation network and the proposed object feature aggregation module (OFAM). Afterwards, the object relations are calculated and the scene representation is constructed based on the proposed object attention module (OAM) and global relation aggregation module (GRAM). The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network. Moreover, OTS outperforms the state-of-the-art methods by more than 2% on indoor scene recognition without using any additional streams. Code is publicly available at: https://github.com/FreeformRobotics/OTS.

IROS Conference 2021 Conference Paper

PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time

Huayan Zhang
Tianwei Zhang 0002
Tin Lun Lam
Sethu Vijayakumar

Dynamic environments that include unstructured moving objects pose a hard problem for Simultaneous Localization and Mapping (SLAM) performance. The motion of rigid objects can be typically tracked by exploiting their texture and geometric features. However, humans moving in the scene are often one of the most important, interactive targets – they are very hard to track and reconstruct robustly due to non-rigid shapes. In this work, we present a fast, learning-based human object detector to isolate the dynamic human objects and realise a real-time dense background reconstruction framework. We go further by estimating and reconstructing the human pose and shape. The final output environment maps not only provide the dense static backgrounds but also contain the dynamic human meshes and their trajectories. Our Dynamic SLAM system runs at around 26 frames per second (fps) on GPUs, while additionally turning on accurate human pose estimation can be executed at up to 10 fps.

ICRA Conference 2021 Conference Paper

Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation

Xiaoyu Zhang
Lei Yan 0011
Tin Lun Lam
Sethu Vijayakumar

This paper introduces a novel task-space decomposed motion planning framework for multi-robot simultaneous locomotion and manipulation. When several manipulators hold an object, closed-chain kinematic constraints are formed, and it will make the motion planning problems challenging by inducing lower-dimensional singularities. Unfortunately, the constrained manifold will be even more complicated when the manipulators are equipped with mobile bases. We address the problem by introducing a dual-resolution motion planning framework which utilizes a convex task region decomposition method, with each resolution tuned to efficient computation for their respective roles. Concretely, this dual-resolution approach enables a global planner to explore the low-dimensional decomposed task-space regions toward the goal, then a local planner computes a path in high-dimensional constrained configuration space. We demonstrate the proposed method in several simulations, where the robot team transports the object toward the goal in the obstacle-rich environments.

ICRA Conference 2021 Conference Paper

Thrust Enhancement of Wave-driven Unmanned Surface Vehicle by using Asymmetric Foil

Yan Gao
Lyucheng Xie
Tin Lun Lam

In the Wave-driven unmanned surface vehicles (WUSVs), oscillating-foils are the most straightforward and widely used wave energy conversion mechanism. In this paper, a kind of novel asymmetric foil is proposed, which improves the wave energy-converting efficiency to provide a more significant thrust in every wave cycle. We break down the movement of the foils in the wave and build the corresponding kinetic model to analyze their working effectiveness numerically. Through computational fluid dynamic (CFD) simulations, we determine the optimal values of critical parameters of the foils, which are suitable for a wide range of wave conditions. The thrust enhancement of the asymmetric foil is verified in both CFD simulations and hydrodynamic experiments, and the result shows a similar enhancement trend. Comparing with the traditional symmetric foil, our asymmetric foil can provide at least 13. 75% more thrust to the WUSVs.

ICRA Conference 2021 Conference Paper

Versatile Locomotion by Integrating Ankle, Hip, Stepping, and Height Variation Strategies

Jiatao Ding
Songyan Xin
Tin Lun Lam
Sethu Vijayakumar

Stable walking in real-world environments is a challenging task for humanoid robots, especially when considering the dynamic disturbances, e. g. , caused by external perturbations that may be encountered during locomotion. The varying nature of disturbance necessitates high adaptability. In this paper, we propose an enhanced Nonlinear Model Predictive Control (NMPC) approach for robust and adaptable walking – we term it versatile locomotion, by limiting both the Center of Pressure (CoP) and Divergent Component of Motion (DCM) movements. Due to utilization of the Nonlinear Inverted Pendulum plus Flywheel model, the robot is endowed with the capabilities of CoP manipulation (if equipped with finitesized feet), step location adjustment, upper body rotation, and vertical height variation. Considering the feasibility constraints, especially the usage of relaxed CoP constraints, the NMPC scheme is established as a Quadratically Constrained Quadratic Programming problem, which is solved efficiently by Sequential Quadratic Programming with enhanced solvability. Simulation experiments demonstrate the effectiveness of our method to recruit optimal hybrid strategies in order to realize versatile locomotion, for the robot with finite-sized or point feet.

ICRA Conference 2020 Conference Paper

A Novel Solar Tracker Driven by Waves: From Idea to Implementation

Ruoyu Xu
Hengli Liu
Chongfeng Liu
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.

IROS Conference 2020 Conference Paper

A Two-stage Automatic Latching System for The USVs Charging in Disturbed Berth

Kaiwen Xue
Chongfeng Liu
Hengli Liu
Ruoyu Xu
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

Automatic latching for charging in a disturbed environment for Unmanned Surface Vehicle (USVs) is always a challenging problem. In this paper, we propose a two-stage automatic latching system for USVs charging in berth. In Stage I, a vision-guided algorithm is developed to calculate an optimal latching position for charging. In Stage II, a novel latching mechanism is designed to compensate the movement misalignments from the water disturbance. A set of experiments have been conducted in real-world environments. The results show the latching success rate has been improved from 40% to 73. 3% in the best cases with our proposed system. Furthermore, the vision-guided algorithm provides a methodology to optimize the design radius of the latching mechanism with respect to different disturbance levels accordingly. Outdoor experiments have validated the efficiency of our proposed automatic latching system. The proposed system improves the autonomy intelligence of the USVs and provides great benefits for practical applications.

IROS Conference 2020 Conference Paper

An Obstacle-crossing Strategy Based on the Fast Self-reconfiguration for Modular Sphere Robots

Haobo Luo
Ming Li
Guangqi Liang
Huihuan Qian
Tin Lun Lam

This paper introduces an obstacle-crossing strategy, and the self-reconfiguration algorithm for a new class of modular robots called the rolling sphere, which can fit obstacles represented by cubes of different sizes due to the chain connection of multiple spheres. For the self-reconfiguration of the rolling spheres, a large gradient is obtained by classifying its action types and hierarchically minimizing the distance between the initial configuration and the final configuration. The most direct use of this large gradient is the fast crossing of various obstacles, by jointing multiple self-reconfigurations according to the OctoMap of the obstacles. It is verified in simulation that the self-reconfiguration takes full advantage of the parallel movement of multiple modules to reduce the total time steps, and the obstacle-crossing strategy can adapt to a variety of obstacles.

ICRA Conference 2020 Conference Paper

CCRobot-III: a Split-type Wire-driven Cable Climbing Robot for Cable-stayed Bridge Inspection *

Ning Ding 0003
Zhenliang Zheng
Junlin Song
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

This paper presents a novel Cable Climbing Robot CCRobot-III, which is the third version designed for bridge cable inspection tasks, aiming at surpassing previous versions in terms of climbing speed and payload capacity. Benefiting from Split-type Wire-driven design, CCRobot-III can climb along a 90-110mm diameter bridge cable in inchworm-like gait at a speed of up to 12m/min, and carrying more than 40kg payload at the same time. CCRobot-III consists of a climbing precursor and a main-body frame. The two parts are connected and driven by steel wires. The climbing precursor, acting as a mobile anchor, moves quickly on a bridge cable. The mainbody frame, acting as a mobile winch, carries payload and pulls itself to a certain position with steel wires. Both parts have one or two pairs of palm-based gripper, which is the key component for providing strong adhesion to support the robot climbing. Experimental results have shown that CCRobotIII possesses outstanding climbing performance, high payload capacity, and good adaptability to complex conditions of cable surface. Moreover, it has potential engineering applications on the cable-stayed bridge for fieldwork.

IROS Conference 2020 Conference Paper

FreeBOT: A Freeform Modular Self-reconfigurable Robot with Arbitrary Connection Point - Design and Implementation

Guanqi Liang
Haobo Luo
Ming Li
Huihuan Qian
Tin Lun Lam

This paper proposes a novel modular selfreconfigurable robot (MSRR) "FreeBOT", which can be connected freely at any point on other robots. FreeBOT is mainly composed of two parts: a spherical ferromagnetic shell and an internal magnet. The connection between the modules is genderless and instant, since the internal magnet can freely attract other FreeBOT spherical ferromagnetic shells, and not need to be precisely aligned with the specified connector. This connection method has fewer physical constraints, so the FreeBOT system can be extended to more configurations to meet more functional requirements. FreeBOT can accomplish multiple tasks although it only has two motors: module independent movement, connector management and system reconfiguration. FreeBOT can move independently on the plane, and even climb on ferromagnetic walls; a group of FreeBOTs can traverse complex terrain. Numerous experiments have been conducted to test its function, which shows that the FreeBOT system has great potential to realize a freeform robotic system.

IROS Conference 2020 Conference Paper

OceanVoy: A Hybrid Energy Planning System for Autonomous Sailboat

Qinbo Sun
Weimin Qi
Hengli Liu
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

Towards long range and high endurance sailing, energy is of utmost importance. Moreover, benefiting from the dominance of the sailboat itself, it is energy-saving and environment-friendly. Thus, the sailboat with energy planning problem is meaningful. However, until now, the sailboat energy optimization problem has rarely been considered. In this paper, we focus on the energy consumption optimization of an autonomous sailboat. It has been formulated as a Nonlinear Programming problem (NLP). We deal with it with a hybrid control scheme, in which pseudo-spectral (PS) optimal control method is used in heading control, and a model-free framework guided by Extreme Seeking Control (ESC) is used in sail control. The optimal path is generated with the optimal input motor torques in time series. As a result, both simulation and experiments have validated motion planning and energy planning performance. Notably, about 7% of energy is saved on average. Our proposed method can make sailboats sailing longer and sustainable.

IROS Conference 2020 Conference Paper

Robot-to-Robot Relative Pose Estimation based on Semidefinite Relaxation Optimization

Ming Li
Guanqi Liang
Haobo Luo
Huihuan Qian
Tin Lun Lam

In this paper, the 2D robot-to-robot relative pose (position and orientation) estimation problem based on ego-motion and noisy distance measurements is considered. We address this problem using an optimization-based method, which does not require complicated numerical analysis while yields no inferior relative localization (RL) results compared to existing approaches. In particular, we start from a state-of-the-art method named square distances weighted least square (SD-WLS), and reformulate it as a non-convex quadratically constrained quadratic programming (QCQP) problem. To handle its non-convex nature, a semidefinite programming (SDP) relaxation optimization-based method is proposed, and we prove that the relaxation is tight when measurements are free from noise or just corrupted by small noise. Further, to obtain the optimal solution of the relative pose estimation problem in the sense of maximum likelihood estimation (MLE), a theoretically optimal WLS method is developed to refine the estimate from the SDP optimization. Comprehensive simulations and well-designed experiments are presented for validating the tightness of the SDP relaxation, and the effectiveness of the proposed algorithm is highlighted by comparing it to the existing approaches.

IROS Conference 2014 Conference Paper

Design and implementation of a low-cost and lightweight inflatable robot finger

Ronghuai Qi
Tin Lun Lam
Yangsheng Xu

In this paper, mechanical design and implementation of a low-cost and lightweight inflatable robot finger are proposed. The proposed soft inflatable robot finger is different from traditional designs. It uses a common and low cost inflatable material and can be easily and massively manufactured. The proposed soft inflatable finger only weighs 0. 8 grams, but can well realize swift movement which is actuated by low pressure air. Numerous analyses and experiments have been conducted for key parameters selection of the mechanical design. The performances of the proposed finger including flexing and extending have also been evaluated, and results are satisfactory.

ICRA Conference 2014 Conference Paper

Kinematic modeling and control of a multi-joint soft inflatable robot arm with cable-driven mechanism

Ronghuai Qi
Tin Lun Lam
Yangsheng Xu

In this paper, the kinematic modeling and control for a multi-joint inflatable robot arm with cable-driven mechanism are proposed. The soft inflatable robot arm is capable of imitating human arms to realize remote interaction. The weight of the arm is only about 50 grams, and collision safe. To solve the challenge problems of kinematics of the soft inflatable arm, new approaches are proposed, including redundant rigid arm and soft inflatable joint models. The approaches have a good advantage of applying to muti-joint arms. As our knowledge it is the first time to solve the kinematics of muti-joint soft inflatable arm in Three-Dimensional coordinate space. Numerous experiments have been conducted, including movement space and positioning accuracy. The workspace and velocity are close to an adult's arm movement space and normal motion speed.

ICRA Conference 2014 Conference Paper

Longitudinal wheel-slip control for four wheel independent steering and drive vehicles

Tin Lun Lam
Huihuan Qian
Yangsheng Xu

In this paper, a longitudinal wheel-slip controller for four wheel independent steering and drive (4WISD) vehicles is proposed to suppress longitudinal wheel slip in varying road conditions. Different from conventional methods that consider single driving source and zero steering angle, the proposed controller considers all independent traction sources from each driving wheel and omnidirectional steering command so as to eliminate slip detection errors in 4WISD vehicles. The proposed controller requires low cost sensing equipment, including merely wheel speed sensor and accelerometer, which makes the system practical to be utilized. The proposed wheel-slip controller can be applied to vehicles with arbitrary quantity of driving wheels and different steering configurations such as traditional two-front-wheel steering and two-rear-wheel steering. Numerical simulation results are presented to demonstrate the efficiency of the proposed longitudinal wheel-slip controller.

ICRA Conference 2014 Conference Paper

Mechanical design and implementation of a soft inflatable robot arm for safe human-robot interaction

Ronghuai Qi
Tin Lun Lam
Yangsheng Xu

In this paper, a novel soft inflatable arm is proposed for telepresence robots. It is capable of imitating human arms to realize remote interaction. The new proposed arm using a very common and low cost inflatable material, and it is very light, which weight is only about 50 grams, but can well realize agile movement by driving three tiny cables installed in shoulder joint and elbow joint, respectively. Meanwhile, the proposed cable driven mechanism also allows connecting numbers of joints easily. The soft inflatable can work just by pumping air with very low pressure (7. 32 ± 3. 45 kPa), and allows human directly and safely contact without any external sensors. Moreover, to solve the challenge problems of soft joint deformation, the kinematic modeling of the joint with deformation compensation is also developed. Experimental results show that the soft inflatable arm can agilely move for remote interaction. The workspace and velocity are also close to an adult's arm movement space and normal motion speed.

ICRA Conference 2013 Conference Paper

Traction/braking force distribution algorithm for omni-directional all-wheel-independent-drive vehicles

Tin Lun Lam
Jingyu Yan 0001
Huihuan Qian
Yangsheng Xu

In this paper, a traction/braking force distribution algorithm for omni-directional all-wheel-independent-drive vehicles is proposed as a tool to enhance driving stability. In the proposed algorithm, the amount of the traction or braking force on each driving wheel can be determined so as to generate a desired tangential force, yaw moment and centripetal force independently. The algorithm considers omni-directional steering command and is capable of handling both traction and braking force commands. The algorithm is applicable on vehicles with at least three independent driving wheels. Simulations have been conducted to illustrate the use of the proposed force distribution method in enhancing vehicles stability.

IROS Conference 2012 Conference Paper

Collision avoidance of industrial robot arms using an invisible sensitive skin

Tin Lun Lam
Hoi Wut Yip
Huihuan Qian
Yangsheng Xu

Collision avoidance of industrial robot arms in varying environment is a challenging task which has been a tough problem for decades. It often requires a large number of sensors and high computational power. Moreover, since the sensors are often mounted on the surface of robot arms, they may affect the appearance of the robot arms and may be vulnerable to damage. This video presents a cost-effective invisible sensitive skin that can cover a large area without utilizing a large number of sensors and it is built inside the robot arm. By using only 5 contactless capacitive sensors and specially designed antennas, collision avoidance of a 6-DOF industrial robot arm is attained.

ICRA Conference 2012 Conference Paper

Direct yaw moment control for four wheel independent steering and drive vehicles based on centripetal force detection

Tin Lun Lam
Huihuan Qian
Yangsheng Xu

In this paper, a deterministic yaw moment controller for four wheel independent steering and drive vehicles is proposed to enhance driving stability and controllability. Different to conventional methods that track a desired yaw rate, the proposed controller stabilizes a vehicle by additionally tracking the heading angle of a vehicle which is more efficient and robust. The heading angle of a vehicle is obtained by a novel method which is based on centripetal force detection. It eliminates the prerequisite knowledge of the characteristics between wheels and road surface which are time varying and difficult to be measured in real time. The proposed system only requires low cost sensing equipment such as wheel speed sensor and accelerometer that makes the system practical to be utilized. The proposed heading angle detection method can be generally applied to any kind of vehicle. The deterministic yaw moment controller is also applicable to any type of four wheel independent drive vehicles.

ICRA Conference 2011 Conference Paper

A flexible tree climbing robot: Treebot - design and implementation

Tin Lun Lam
Yangsheng Xu

This paper proposed a novel tree climbing robot "Treebot" that has high maneuverability on an irregular tree environment and surpasses the state of the art tree climbing robots. Treebot's body is a novel continuum maneuver structure that has high degrees of freedom and superior extension ability. Treebot also equips with a pair of omni-directional tree grippers that enable Treebot to adhere on a wide variety of trees with a wide range of gripping curvature. By combining these two novel designs, Treebot is able to reach many places on trees including branches. Treebot can maneuver on a complex tree environment, but only five actuators are used in the mechanism. As a result, Treebot can keep in compact size and lightweight. Although Treebot weighs only 600 grams, it has payload capability of 1. 75 kg which is nearly three times of its own weight. On top of that, the special design of the gripper permits zero energy consumption in static gripping. Numerous experiments have been conducted on real trees. Experimental results reveal that Treebot has excellent climbing performance on a wide variety of trees.

IROS Conference 2011 Conference Paper

Mechanical design of a tree gripper for miniature tree-climbing robots

Tin Lun Lam
Yangsheng Xu

In this paper, an novel tree gripping mechanism has been proposed for miniature tree-climbing robots. It is capable of attaching on a wide variety of trees with a wide range of gripping curvature. In addition, it is lightweight and simple in control. The gripper is simple in control as it is actuated by one actuator only. In addition, the omni-directional gripping ability also simplifies the use of the gripper as there is no extra actuator needed for orientation control. The special mechanism and optimized settings make the gripper able to attach on different sizes of tree tightly. The mechanism also allows zero energy consumption in static gripping. Numerous on-tree experiments of the proposed mechanism have been conducted and the results are satisfied.

ICRA Conference 2011 Conference Paper

Treebot: Autonomous tree climbing by tactile sensing

Tin Lun Lam
Yangsheng Xu

This paper proposed an autonomous tree climbing algorithm for a novel tree climbing robot named Treebot. Making a robot realize an environment and climb on a tree autonomously is a challenging task as the shape of tree is complex and irregular. To our best knowledge, this is the first paper dealing with the autonomous climbing problem in an unknown tree environment. The proposed method is aimed to use minimal sensing resources to achieve autonomous climbing. It reconstructs the shape of tree by using tactile sensors and guides the robot to climb along an optimal path. Numerous experiments have been carried out and the results are satisfactory.

IROS Conference 2010 Conference Paper

Energy management for four-wheel independent driving vehicle

Huihuan Qian
Guoqing Xu
Jingyu Yan 0001
Tin Lun Lam
Yangsheng Xu
Kun Xu

The promising electric vehicle (EV) technology is a direction to tackle the global non-renewable energy problem. However, the efficiency to use the electric energy still needs deliberate research. Traditional EV has no choice to manage its energy flow, because it has only one traction motor. With the robotic research in 4 wheel independent drive (4WID), the driving task of the single traction motor can be shared by 4 independent in-wheel motors. By exploring the motor efficiency map, we propose the energy management strategy based on optimal driving torque distribution (ODTD). The total input power of the 4 motors can be minimized while the driving performance is still maintained, and electric energy consumption can be reduced compared with traditional single motor driving EV. Simulation results validate the proposed strategy. The energy management strategy can also be applied to multi-driving-wheel mobile robots.

IROS Conference 2010 Conference Paper

Linear-time path and motion planning algorithm for a tree climbing robot - TreeBot

Tin Lun Lam
Guoqing Xu
Huihuan Qian
Yangsheng Xu

This paper proposes a path and motion planning algorithm for a tree climbing problem. This problem is challenging as the shape of tree is complex and irregular. To our best knowledge, this is the first paper dealing with the path planning problem on natural tree environment. Different from conventional motion planning approach that requires constructing a complex configuration space, this paper divides the planning problem into two parts, i. e. , path and motion planning problem so as to reduce the dimension of the problem. An intuitive method to represent a climbing space is proposed that highly simplifies the path planning problem. With the use of a dynamic programming algorithm, an optimal path to reach a target position can be acquired in linear time. In addition, an efficient motion planning algorithm for a tree climbing robot named TreeBot is developed to make TreeBot follow the planned path.

ICRA Conference 2009 Conference Paper

Omni-directional steer-by-wire interface for four wheel independent steering vehicle

Tin Lun Lam
Huihuan Qian
Yangsheng Xu
Guoqing Xu

In this paper, an omni-directional steer-by-wire interface for four wheel independent steering vehicle is presented. The proposed steering interface is an extension of a traditional steering interface that provides three steering inputs. By combination of which, driver can control the vehicle in traditional way or omni-directionally without any mode switching operation. The reservation of the conventional steering behavior makes driver easy to adapt the novel steering interface. The force feedback controller is designed to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. Hardware-in-the-loop simulations are conducted to verify the hardware prototype and examine the proposed algorithms.

ICRA Conference 2009 Conference Paper

Traction force distribution on omni-directional four wheel independent drive electric vehicle

Tin Lun Lam
Yangsheng Xu
Guoqing Xu

This paper proposes an optimal traction force distribution for omni-directional four wheel independent steering (4WIS) and four wheel independent drive (4WID) vehicle. The proposed force distribution algorithm is aimed to enhance the vehicle stability with minimum cost. The algorithm avoids the use of any feedback information of vehicle motion such as linear velocity as this information is difficult to measure accurately and the price of the measuring equipment is very high. As a result, the implementation cost can be reduced and at the same time avoid improper force distribution due to the inaccurate measured information. Moreover, the proposed algorithm does not involve any parameter tuning. It makes the algorithm easy to implement. The proposed algorithm can also be applied to any steering types of 4WID vehicle such as typical two wheel steering (2WS) as 4WIS is the general case of any steering configuration. Simulation results reveal that the performance of the proposed force distribution is superior to the uniform force distribution which is commonly used in 4WID vehicle.