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Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-robot systems

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arXiv 2023年

作者： Huang, Jihao Zeng, Jun Chi, Xuemin Sreenath, Koushil Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Hybrid Robotics Group The Department of Mechanical Engineering UC Berkeley United States

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this paper, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot’s states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

Fast Path Planning for Autonomous Vehicle Parking with Safety-Guarantee using Hamilton-Jacobi Reachability

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arXiv 2023年

作者： Chi, Xuemin Zeng, Jun Huang, Jihao Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Hybrid Robotics Group the Department of Mechanical Engineering UC Berkeley United States

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicle dynamics is computed by the HJ analysis and it intersects with a safe set to get a safe reachable set. The safe set is defined by constraints of positive signed distances for obstacles in the environment and computed by solving QP optimization problems offline. For states inside the intersection set, i.e., the safe reachable set, the computed backward reachable tube ensures they are reachable subjected to system dynamics and input bounds, and the safe set guarantees they satisfy parking safety with respect to obstacles in different shapes. For online computation, randomized states are sampled from the safe reachable set, and used as heuristic guide points to be considered in the bidirectional A* search. The bidirectional A* search is paralleled for each randomized state from the safe reachable set. We show that the proposed two-level planning algorithm is able to solve different parking scenarios effectively and computationally fast for typical parking requests. We validate our algorithm through simulations in large-scale randomized parking scenarios and demonstrate it to be able to outperform other state-of-the-art parking planning algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

Global optimization of ORC process parameters based on SSL artificial neural network

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Global optimization of ORC process parameters based on SSL a...

Chinese Automation Congress (CAC)

作者： Jiabin Qin Junjie Zhou Xialai Wu Ling Lin Jian Yi Runze Lin Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou university Huzhou China State Key Laboratory of Industrial Control Technology Zhejiang university Hangzhou China

The Organic Rankine Cycle (ORC) is a commendable technology for waste heat recovery. However, the optimization problem of ORC operating parameters based on its mechanism model is not easy to be solved for obtaining the global optimal solution, and it is time-consuming. To deal with this problem, a global optimization method of ORC process parameters is proposed to maximize the net output power of the system. Utilizing the SSL Artificial Neural Network (ANN) is utilized as a surrogate model in the optimization, based on this, the parametric optimization problem can be converted into a Mixed-Integer Linear Optimization Problem (MILP). The optimization results show that, compared with the process parameter optimization method based on the ORC mechanism model, the solution time of the optimization is greatly reduced, and the obtained solution is globally optimal.

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FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation

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FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving...

International Conference on Software Engineering (ICSE)

作者： Yang Sun Christopher M. Poskitt Kun Wang Jun Sun School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose Fixdrive, a framework that analyses driving records from near-misses or law violations to generate AV driving strategy repairs that reduce the chance of such incidents occurring again. These repairs are captured in μDrive, a high-level domain-specific language for specifying driving behaviours in response to event-based triggers. Implemented for the state-of-the-art autonomous driving system Apollo, Fixdrive identifies and visualises critical moments from driving records, then uses a Multimodal Large Language Model (MLLM) with zero-shot learning to generate μDrive programs. We tested Fixdrive on various benchmark scenarios, and found that the generated repairs improved the AV's performance with respect to following traffic laws, avoiding collisions, and successfully reaching destinations. Furthermore, the direct costs of repairing an AV—15 minutes of offline analysis and $0.08 per violation-are reasonable in practice.

关键词： Visualization Runtime Large language models Zero shot learning Maintenance engineering Trajectory Software reliability Driver behavior Autonomous vehicles Vehicles

Advancing Virtual Reality Interaction: A Ring-Shaped controller and Pose Tracking

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Advancing Virtual Reality Interaction: A Ring-Shaped Control...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhuqing Zhang Dongxuan Li Jiayao Ma Yijia He Pan Ji Rong Xiong Hongdong Li Yue Wang State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Tencent Holdings Tencent XR Vision Labs Shenzhen China College of Engineering and Computer Science Australian National University Canberra

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Ensuring robust tracking of controllers’ movement is critical for human-robot interaction in virtual reality (VR) scenarios. This paper proposes a robust tracking algorithm based on a novel wearable ring-shaped controller equipped with an inertial measurement unit (IMU) and a light-emitting diode (LED). This novel controller design allows users to free up their hands for more immersive experiences. To track the controller’s motion accurately and robustly, we resort to various forms of visual measurements, including 6 DoF and 5 DoF pose measurements from hand gesture detection, as well as 3 DoF position measurement and 2 DoF image measurement derived from the LED. We theoretically analyze the performances of these observation models and propose an optimal observation model combination scheme. Moreover, the necessity and rationale of online estimating system gravity are illustrated. The effectiveness of our tracking method is validated through extensive experiments.

关键词： Solid modeling Analytical models Visualization Measurement units Tracking Virtual reality Inertial navigation

Decentralized Zeno-Free Event-Triggered control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks

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arXiv 2024年

作者： Zhang, Dandan Jin, Xin Su, Hongye The State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution;while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness, which is verified by the robust global attitude stabilization of flexible combined ro

关键词： Networked control systems

FIXDRIVE: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation

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arXiv 2025年

作者： Sun, Yang Poskitt, Christopher M. Wang, Kun Sun, Jun School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV’s planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FIXDRIVE, a framework that analyses driving records from near-misses or law violations to generate AV driving strategy repairs that reduce the chance of such incidents occurring again. These repairs are captured in µDrive, a high-level domain-specific language for specifying driving behaviours in response to event-based triggers. Implemented for the state-of-the-art autonomous driving system Apollo, FIXDRIVE identifies and visualises critical moments from driving records, then uses a Multimodal Large Language Model (MLLM) with zero-shot learning to generate µDrive programs. We tested FIXDRIVE on various benchmark scenarios, and found that the generated repairs improved the AV’s performance with respect to following traffic laws, avoiding collisions, and successfully reaching destinations. Furthermore, the direct costs of repairing an AV—15 minutes of offline analysis and $0.08 per violation—are reasonable in practice. Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

Safe Dynamic Motion Generation in Configuration Space Using Differentiable Distance Fields

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arXiv 2024年

作者： Chi, Xuemin Li, Yiming Huang, Jihao Dai, Bolun Liu, Zhitao Calinon, Sylvain State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Idiap Research Institute Martigny1920 Switzerland Lausanne1015 Switzerland Fauna Robotics New YorkNY United States

Generating collision-free motions in dynamic environments is a challenging problem for high-dimensional robotics, particularly under real-time constraints. control Barrier Functions (CBFs), widely utilized in safety-critical control, have shown significant potential for motion generation. However, for high-dimensional robot manipulators, existing QP formulations and CBF-based methods rely on positional information, overlooking higher-order derivatives such as velocities. This limitation may lead to reduced success rates, decreased performance, and inadequate safety constraints. To address this, we construct time-varying CBFs (TVCBFs) that consider velocity conditions for obstacles. Our approach leverages recent developments on distance fields for articulated manipulators, a differentiable representation that enables the mapping of objects’ position and velocity into the robot’s joint space, offering a comprehensive understanding of the system’s interactions. This allows the manipulator to be treated as a point-mass system thus simplifying motion generation tasks. Additionally, we introduce a time-varying control Lyapunov function (TVCLF) to enable whole-body contact motions. Our approach integrates the TVCBF, TVCLF, and manipulator physical constraints within a unified QP framework. We validate our method through simulations and comparisons with state-of-the-art approaches, demonstrating its effectiveness on a 7-axis Franka robot in real-world experiments. © 2024, CC BY.

关键词： Lyapunov functions

Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

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Simulated Ising Annealing Algorithm with Gaussian Augmented ...

第42届中国控制会议

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyCollege of Control Science and Engineering Zhejiang University School of Automation Science and Engineering South China University of Technology School of Mechanical Engineering and Automation Harbin Institude of Technology

A numerical technique called Simulated Ising Annealing(SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization(QUBO) problem in combinatorial optimization,which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo(GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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