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Jingping Wang

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5 papers

2 author rows

IROS Conference 2024 Conference Paper

Multi-Fov-Constrained Trajectory Planning for Multirotor Safe Landing

Dong Wang
Jingping Wang
Suqin He
Jinxin Huang
Bangyan Zhang
Yinian Mao
Guoquan Huang 0003
Chao Xu 0001

In recent years, multirotors have become more and more widely used, such as in aerial photography and delivery. Ensuring a safe landing in emergencies is the most basic requirement, and it is important to make full use of all the sensors of the multirotor. To improve the safety of UAV landing in unknown unstructured scenes, this paper proposes a multi-FOV-constrained trajectory planning algorithm. Due to the discontinuity of multi-FOV constraints and the nonlinearity of UAV dynamics, the entire trajectory planning problem is a nonlinear optimization problem with non-convex constraints. To address this problem, our algorithm contains two stages, a multi-fov-constrained path search algorithm and a safe landing trajectory optimization algorithm. The multi-fov-constrained path search algorithm is used to generate a safe initial path that satisfies the FOV constraint. Then, the safe landing trajectory optimization algorithm generates a safe trajectory, which considers FOV constraints, dynamics, smoothness, and obstacle avoidance. We conducted simulation experiments and real-world experiments to verify the robustness and effectiveness of our algorithm.

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IROS Conference 2023 Conference Paper

Continuous Implicit SDF Based Any-Shape Robot Trajectory Optimization

Tingrui Zhang
Jingping Wang
Chao Xu 0001
Alan Gao
Fei Gao 0011

Optimization-based trajectory generation methods are widely used in whole-body planning for robots. However, existing work either oversimplifies the robot's geometry and environment representation, resulting in a conservative trajectory or suffers from a huge overhead in maintaining additional information such as the Signed Distance Field (SDF). To bridge the gap, we consider the robot as an implicit function, with its surface boundary represented by the zero-level set of its SDF. We further employ another implicit function to lazily compute the signed distance to the swept volume generated by the robot and its trajectory. The computation is efficient by exploiting continuity in space-time, and the implicit function guarantees continuous collision evaluation even for nonconvex robots with complex surfaces. We also propose a trajectory optimization pipeline applicable to the implicit SDF. Simulation and real-world experiments validate the high performance of our approach for arbitrarily shaped robot trajectory optimization.

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EAAI Journal 2023 Journal Article

CVT on-line error measurement hybrid-driven by domain knowledge and Stacking Model

Jingping Wang
Ying Shi
Rui Zhang
Zhonghua Wu
Hao Ye
Shenwei Li

The performance of Capacitive Voltage Transformer (CVT) degrades over time, making measurement error monitoring a research hotspot in the field of smart grid. At present, these are several challenges such as complex data features, a lack of criteria for selecting optimal measurement models, and low precision. CVT measurement errors can be classified into ideal error and additional one. The former is typically evaluated via mutual information and redundancy within the topology-level transformer group. Considering that a single model cannot process the time series, strong randomness and nonlinearity of the additional error, the Stacking model is selected. Based on the principle of heterogeneity and high-quality, correlation coefficient and feature contribution degree, Random Forest, eXtreme Gradient Boosting, Ridge Regression, K Nearest Neighbors, Support Vector Regression, and Long Short-Term Memory are chosen as base learners through correlation and feature contribution analysis; while extra-trees with strong generalization and robustness is chosen as the meta learner. To improve the measurement precision, the attention-like mechanism is used to scale time and accuracy weights. Finally, according to the power domain knowledge, a linear superposition model is developed to fuse the ideal and additional errors, and thus realize online error measurement for CVTs. The experimental results indicate that the improved Stacking model outperforms mainstream measurement models by an average reduction of 59. 47%, and 52. 58% in the root mean squared error and the mean absolute error with the best R 2 closest to 1. It not only effectively improves the accuracy but also meets speed requirement for online error measurement.

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IROS Conference 2023 Conference Paper

Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments

Changjia Ma
Zhichao Han 0002
Tingrui Zhang
Jingping Wang
Long Xu 0002
Chengyang Li
Chao Xu 0001
Fei Gao 0011

Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments. In this system, path finding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leveraged to escape from infeasible initial value's local minima. Then spatial-temporal optimization is employed to generate a safe, smooth and dynamically feasible trajectory. During optimization, the trajectory is discretized by fixed time steps. Penalty is imposed on the signed distance between agents to realize collision avoidance, and differential flatness cooperated with limitation on front steer angle satisfies the non-holonomic constraints. With trajectories broadcast to the wireless network, agents are able to check and prevent potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.

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ICRA Conference 2023 Conference Paper

Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

Jingping Wang
Long Xu 0002
Haoran Fu
Zehui Meng
Chao Xu 0001
Yanjun Cao
Ximin Lyu
Fei Gao 0011

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.

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