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Xiangke Wang

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4 papers

2 author rows

ICRA Conference 2024 Conference Paper

Optimal Containment Control of Multiple Quadrotors via Reinforcement Learning

Ming Cheng
Hao Liu
Deyuan Liu
Haibo Gu
Xiangke Wang

This paper explores the optimal containment control problem for nonlinear and underactuated quadrotors with multiple team leaders governed by nonlinear dynamics, employing the reinforcement learning. A cascade controller is formulated, comprising a position control component to ensure containment achievement and an attitude control component to govern rotational channel. The proposed optimal control protocols derived from historical data collected from quadrotor systems without requirement for exact knowledge of vehicle dynamics. The simulation illustrates the effectiveness of the proposed controller in managing a quadrotor team with multiple leaders.

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JAAMAS Journal 2023 Journal Article

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

Jie Li
Runfeng Chen
Xiangke Wang

Abstract Multi-task distributed scheduling (MTDS) remains a challenging problem for multi-agent systems used for uncertain and dynamic real-world tasks such as search-and-rescue. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the problem of non-convergence that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm through the integration of a task removal inference strategy and a deadlock avoidance mechanism. Specifically, the task removal inference strategy results in better exploration performance than the original PI, improving the suboptimal solutions caused by the heuristics for local task selection as done in PI. In addition, we design a deadlock avoidance mechanism that limits the number of times of removing the same task and isolating consecutive inclusions of the same task. Therefore, it guarantees the convergence of the MTDS algorithm. We demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of the search-and-rescue task. The results show that the proposed algorithm can obtain a lower average time cost and the highest total allocation number.

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IROS Conference 2023 Conference Paper

VDBblox: Accurate and Efficient Distance Fields for Path Planning and Mesh Reconstruction

Yinlong Bai
Zhiqiang Miao
Xiangke Wang
Yong Liu 0007
Hesheng Wang 0001
Yaonan Wang 0001

Highly accurate and efficient map in unknown and complex environments is essential for robotics navigation. Traditionally, mobile robot platforms are often computationally constrained when using multiple sensors to process large amounts of input data. In previous works, some of them have been deployed to embedded platforms in real-time. However, how to balance accuracy and efficiency while reducing the computational resources and the memory footprint is still the bottleneck. Motivated by these challenges, we proposed a mapping framework called VDBblox to incrementally build Euclidean Signed Distance Fields (ESDFs) map from Truncated Signed Distance Fields (TSDFs) mapping. We use a novel weight function to update the non-projective TSDFs, thus improving the quality of the mesh reconstruction with higher accuracy than up-to-date methods. Meanwhile, the generated ESDFs map is maintained by the least recently used (LRU) cache to dynamically handle the obstacle changes with less runtime than state-of-the-art. We show VDBblox performance in terms of accuracy and efficiency by benchmark comparison on RGB-D and LiDAR public datasets. Moreover, we demonstrate that VDBblox can be integrated into a completed quadrotor system as a sub-module. Then we validate it through online obstacle avoidance and high-quality mesh reconstruction in real-world experiments. Finally, we release our method as open-source code to the community 1 1 Code - https://github.com/yinloonga/vdbblox.

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JAAMAS Journal 2019 Journal Article

Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

Yunyun Zhao
Xiangke Wang
Lincheng Shen

Abstract In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-unmanned aerial vehicle (UAV) systems. The multi-UAV decision-making problem is modeled in the framework of distributed multi-agent partially observable Markov decision processes (MPOMDPs). Specifically, the state of the targets is represented by the joint multi-target probability distribution (JMTPD), which is estimated by a distributed information fusion strategy. In the information fusion process, the most accurate estimation is selected to propagate through the whole network in finite time. We propose a max-consensus protocol to guarantee the consistency of the JMTPD. It is proven that the max-consensus can be achieved in the connected communication graph after a limited number of iterations. Based on the consistent JMTPD, the distributed partially observable Markov decision algorithm is used to make tracking decisions. The proposed method uses the Fisher information to bid for targets in a distributed auction. The bid is based upon the reward value of the individual UAV’s POMDPs, thereby removing the need to optimize the global reward in the MPOMDPs. Finally, the cooperative decision-making approach is deployed in a simulation of a multi-target tracking problem. We compare our proposed algorithm with the centralized method and the greedy approach. The simulation results show that the proposed distributed method has a similar performance to the centralized method, and outperforms the greedy approach.

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