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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhenmin Huang Haichao Liu Shaojie Shen Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. Difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving task of CAVs as an optimal control problem with safety conditions enforced as hard constraints, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs with the travel efficiency improved and the hard safety constraints satisfied. Specifically, all constraints involved are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, with an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence. It is noteworthy that the travel efficiency is enhanced and the computation burden is considerably alleviated with the proposed innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons to demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Technological innovation Optimal control Safety Computational efficiency Planning Vehicle dynamics Task analysis

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Jigsaw-based Benchmarking for Learning Robotic Manipulation

arXiv

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arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023, CC BY.

关键词： Benchmarking

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Xiaobo Liu Fang Wan Sheng Ge Haokun Wang Haoran Sun Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen Guangdong China School of Design Southern University of Science and Technology Shenzhen Guangdong China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong China Department of Computer Science The University of Hong Kong Hong Kong China

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Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for autonomous Driving in Congested Traffic

Real-Time Parallel Trajectory Optimization with Spatiotempor...

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International Conference on Intelligent Transportation

作者： Lei Zheng Rui Yang Zengqi Peng Haichao Liu Michael Yu Wang Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering Monash University Clayton Australia Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective eval-uation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.

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Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

Heuristic Online Path Planning for Mobile Air Purification a...

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IEEE International Conference on robotics and Biomimetics

作者： Qingnan Zhong Kai Chen Jianghua Duan Jun Ma Robotics and Human Machine Interface Laboratory HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology China

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology.

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Analytical Model Transfer-Based Surrogate-Assisted Design Optimization for SPMVM Using Structural Similarity

Analytical Model Transfer-Based Surrogate-Assisted Design Op...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Liyang Liu Yiming Ma Ruichi Wang Zequan Li Yang Xiao Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China CSG PGC Energy Storage Research Institute China Southern Power Grid Energy Storage Co. Ltd Guangzhou China The Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough UK School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

This paper proposes an efficient surrogate-assisted design optimization method for a surface-mounted permanent-magnet vernier machine (SPMVM) based on subdomain model transfer learning. In the design optimization of SPMVM with parallel slot structure with asymmetric circumferential structure, a fictitious motor model with similar structure and identical parameters except for the parallel tooth structure is created. This fictitious motor with symmetric circumferential structure is used to build a subdomain model, so that a high-precision surrogate model can be built for optimization with the aid of analytic model data to reduce the size of the finite element dataset required for the training process, and accelerate the optimization process.

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Integrated Behavior Planning and Motion Control for autonomous Vehicles with Traffic Rules Compliance

Integrated Behavior Planning and Motion Control for Autonomo...

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IEEE International Conference on robotics and Biomimetics

作者： Haichao Liu Kai Chen Yulin Li Zhenmin Huang Jianghua Duan Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

In this article, we propose an optimization-based integrated behavior planning and motion control scheme, which is an interpretable and adaptable urban autonomous driving solution that complies with complex traffic rules while ensuring driving safety. Inherently, to ensure compliance with traffic rules, an innovative design of potential functions (PFs) is presented to characterize various traffic rules related to traffic lights, traversable and non-traversable traffic line markings, etc. These PFs are further incorporated as part of the model predictive control (MPC) formulation. In this sense, high-level behavior planning is attained implicitly along with motion control as an integrated architecture, facilitating flexible maneuvers with safety guarantees. Due to the well-designed objective function of the MPC scheme, our integrated behavior planning and motion control scheme is competent for various urban driving scenarios and able to generate versatile behaviors, such as overtaking with adaptive cruise control, turning in the intersection, and merging in and out of the roundabout. As demonstrated from a series of simulations with challenging scenarios in CARLA, it is noteworthy that the proposed framework admits real-time performance and high generalizability.

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Characteristic Analysis of Flux Switching Linear Machine for Gravity Energy Storage System

Characteristic Analysis of Flux Switching Linear Machine for...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Yao Li Zhijun Ou Yujun Zhang Yang Shen Xueyan Wang Shaoliang Zhou Xiaochao Zeng Hang Zhao CSG PGC Energy Storage Research Institute China Southern Power Grid Energy Storage Co.Ltd Guangzhou China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Energy Development Research Institute China Southern Power Grid Energy Storage Co.Ltd Guangzhou China China Power Engineering Consulting Group North China Power Engineering Co.Ltd Beijing China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

Gravity energy storage technology, which transfers energies between the electric power and the gravitational potential energy, shows outstanding potentials in balancing the energy conflict between power station and public demands. This paper introduces a gravity energy storage system that operating in the engineering shafts or abandoned mines, which uses linear permanent magnet machine as the transformer to achieve energy storage and release process. The working principle of the gravity energy storage system with linear machine is introduced. Most importantly, the paper detailed analyzes the characteristic behaviors of the linear machine from electric motor and generator parts. The influence factors that affect the electromagnetic performances of the linear machine is analysis. The driving and generating performances of the linear machine are simulated and analyzed to validate its efficiency and feasibility.

关键词： Shafts Renewable energy sources Switches Voltage Transformers Generators Power generation Energy storage Gravity Electric motors

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Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

Learning-based Control for PMSM Using Distributed Gaussian P...

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Annual Conference of Industrial Electronics Society

作者： Zhenxiao Yin Xiaobing Dai Zewen Yang Yang Shen Georges Hattab Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Chair of Information-oriented Control Technical University of Munich Munich Germany Center for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

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Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Neural-iLQR: A Learning-Aided Shooting Method for Trajectory...

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IEEE International Conference on robotics and Biomimetics

作者： Zilong Cheng Yulin Li Kai Chen Jianghua Duan Jun Ma Tong Heng Lee Department of Electrical and Computer Engineering National University of Singapore Singapore Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet an incremental machine learning method or a deep neural network is normally required to fit a substantial scale of sampling motion data. Existing works mainly focus on capturing the whole picture of the motion pattern from trial data (which is essentially noisy and hard to identify with any simple predefined structures) for complex robot systems. Instead, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to drive the optimization process towards optimal direction. Through comprehensive evaluations on two illustrative control tasks using three different kinds of lightweight network structures, the proposed method is shown to outperform the conventional iLQR significantly with fast and continuous cost convergence in the presence of inaccuracies in system models, which demonstrates the generalizability and robustness of the proposed learning-aided method.

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