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A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots

A Generic Trajectory Planning Method for Constrained All-Whe...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ren Xin Hongji Liu Yingbing Chen Jie Cheng Sheng Wang Jun Ma Ming Liu 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 Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient performance due to time-consuming mode switches. This inefficiency arises from wheel rotation constraints and inter-wheel cooperation requirements. The direct application of a holonomic moving strategy can lead to significant slip angles or even structural failure. Additionally, the limited steering range of AWS wheeled robots exacerbates non-linearity characteristics, thereby complicating control processes. To address these challenges, we developed a novel planning method termed Constrained AWS (C-AWS), which integrates second-order discrete search with predictive control techniques. Experimental results demonstrate that our method adeptly generates feasible and smooth trajectories for C-AWS while adhering to steering angle constraints. Code and video can be found at https://***/Rex-sys-hk/AWSPlanning.

关键词： Translation Trajectory planning Wheels Transportation Kinematics Real-time systems Trajectory Mobile robots Robots Predictive control

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Improving autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Improving Autonomous Driving Safety with POP: A Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sheng Wang Yingbing Chen Jie Cheng Xiaodong Mei Ren Xin Yongkang Song Ming Liu Robotics and Autonomous Systems Division of Emerging Interdisciplinary Areas (EMIA) Under Interdisciplinary Programs Office (IPO) The Hong Kong University of Science and Technology Hong Kong SAR China Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Computer Science The Hong Kong University of Science and Technology Hong Kong SAR China Lotus Technology Ltd China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. POP first employs SLL to help the model learn to reconstruct history representations, and then utilizes feature distillation as the fine-tuning task to transfer knowledge from the teacher model, which has been pre-trained with complete observations, to the student model, which has only few observations. POP achieves comparable results to topperforming methods in open-loop experiments and outperforms the baseline method in closed-loop simulations, including safety metrics. Qualitative results illustrate the superiority of POP in providing reasonable and safe trajectory predictions. Demo videos and code are available at https://***/POP/.

关键词： Accuracy Roads Self-supervised learning Predictive models Trajectory History Task analysis

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Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training

Every Dataset Counts: Scaling up Monocular 3D Object Detecti...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Fulong Ma Xiaoyang Yan Guoyang Zhao Xiaojie Xu Yuxuan Liu Jun Ma Ming Liu The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China 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 Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Monocular 3D object detection is essential for autonomous driving. However, current monocular 3D detection algorithms rely on expensive 3D labels from LiDAR scans, making it difficult to use in new datasets and unfamiliar environments. This study explores training a monocular 3D object detection model using a mix of 3D and 2D datasets. The proposed framework includes a robust monocular 3D model that can adapt to different camera settings, a selective-training strategy to handle varying class annotations in datasets, and a pseudo 3D training method using 2D labels to improve detection ability in scenes with only 2D labels (as shown in Fig. 1). By utilizing this framework, we can train models on a combination of 3D and 2D datasets to improve generalization and performance on new datasets with only 2D labels. Extensive experiments on KITTI, nuScenes, ONCE, Cityscapes, and BDD100K datasets showcase the scalability of our proposed approach. Here is our project page: https://***/view/fmaafmono3d.

关键词： Training Solid modeling Adaptation models Three-dimensional displays Sensitivity Laser radar Scalability Object detection Cameras Robot sensing systems

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Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections

Curriculum Proximal Policy Optimization with Stage-Decaying ...

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International Conference on Intelligent Transportation

作者： Zengqi Peng Xiao Zhou Yubin Wang Lei Zheng Ming Liu Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou 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 Futian Shenzhen China

Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in such scenarios, this paper proposes a curriculum proximal policy optimization (CPPO) framework with stage-decaying clipping. By adjusting the clipping parameter during different stages of training through proximal policy optimization (PPO), the vehicle can first rapidly search for an approximate optimal policy or its neighborhood with a large parameter, and then converges to the optimal policy with a small one. Particularly, the stage-based curriculum learning technology is incorporated into the proposed framework to improve the generalization performance and further accelerate the training process. Moreover, the reward function is specially designed in view of different curriculum settings. A series of comparative experiments are conducted in intersection-crossing scenarios with bi-lane carriageways to verify the effectiveness of the proposed CPPO method. The results show that the proposed approach demonstrates better adaptiveness to different dynamic and complex environments, as well as faster training speed over baseline methods.

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Incremental Bayesian Learning for Fail-Operational Control in autonomous Driving

Incremental Bayesian Learning for Fail-Operational Control i...

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European Control Conference (ECC)

作者： Lei Zheng Rui Yang Zengqi Peng Wei Yan Michael Yu Wang Jun Ma The Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China School of Engineering Great Bay University Dongguan China 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

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety concerns and affect the task efficiency of the ego vehicle (EV), especially with model uncertainties stemming from environmental disturbances. This paper presents a real-time fail-operational controller that ensures the asymptotic convergence of an uncertain EV to a safe state, while preserving task efficiency in dynamic environments. An incremental Bayesian learning approach is developed to facilitate online learning and inference of changing environmental disturbances. Leveraging disturbance quantification and constraint transformation, we develop a stochastic fail-operational barrier based on the control barrier function (CBF). With this development, the uncertain EV is able to converge asymptotically from an unsafe state to a defined safe state with probabilistic stability. Subsequently, the stochastic fail-operational barrier is integrated into an efficient fail-operational controller based on quadratic programming (QP). This controller is tailored for the EV operating under control constraints in the presence of environmental disturbances, with both safety and efficiency objectives taken into consideration. We validate the proposed framework in connected cruise control (CCC) tasks, where SVs perform aggressive driving maneuvers. The simulation results demonstrate that our method empowers the EV to swiftly return to a safe state while upholding task efficiency in real time, even under time-varying environmental disturbances.

关键词： Uncertainty Simulation Stochastic processes Real-time systems Stability analysis Bayes methods Safety

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Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion

Accurate Prior-centric Monocular Positioning with Offline Li...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jinhao He Huaiyang Huang Shuyang Zhang Jianhao Jiao Chengju Liu Ming Liu Thrust of Robotics and Autonomous Systems The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Baidu Autonomous Driving Technology Department (ADT) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Computer Science University College London London UK School of Electronics and Information Engineering Tongji University Shanghai China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.

关键词： Location awareness Visualization Laser radar Robot vision systems Cameras Sensor systems Robustness

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Tracking the Dynamic Neural Connectivity via Conjugate Gradient Optimization*

Tracking the Dynamic Neural Connectivity via Conjugate Gradi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mingdong Li Shuhang Chen Zhijia Zhao Yiwen Wang Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Mechanical and Electrical Engineering Guangzhou University Department of Electronic and Computer Engineering The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology

Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural *** Relevance— Not directly related.

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Robust Optimization of a Stator Tooth Chamfering IPMSM for Electric Power Steering Applications

Robust Optimization of a Stator Tooth Chamfering IPMSM for E...

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International Conference on Electrical Machines, ICEM

作者： Liyang Liu Yiming Ma Ruichi Wang Zequan Li Lu Sun 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 School of Mechanical Electrical and Manufacturing Loughborough University Loughborough UK School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

The impact of manufacturing errors on motor performance is a crucial issue in the mass production of electric motor products. However, robust indexes are complicated to consider in the design process due to the enormous computational complexity. This paper presents a surrogate- assisted robust optimization method that integrates sensitivity analysis with Pareto front infill criteria. The approach enables efficient sampling in the regions where the optimal solution is most probable, thereby ensuring the local generalization capability of the surrogate model. As a result, robust optimization of probabilistic manufacturing error variables is effectively accomplished. An interior permanent-magnet synchronous machine (IPMSM) employing the stator tooth chamfering technique to mitigate cogging torque is examined for electric power steering applications. To verify the effectiveness of this approach, a limited series of prototypes were fabricated and rigorously tested, confirming the practicality of the proposed method.

关键词： Forging Torque Prototypes Stators Probabilistic logic Power systems Manufacturing Torque measurement Synchronous machines Optimization

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Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

Robotic Control Optimization Through Kernel Selection in Saf...

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IEEE International Conference on robotics and Biomimetics

作者： Lihao Zheng Hongxuan Wang Xiaocong Li Jun Ma Prahlad Vadakkepat School of Data Science The Chinese University of Hong Kong Research Intern at the Singapore Institute of Manufacturing Technology A*STAR Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research (A*STAR) Singapore Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.

关键词： Additives Trajectory tracking Optimization methods Control systems Bayes methods Kernel Robots Tuning Standards Quadrotors

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Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning

Chance-Aware Lane Change with High-Level Model Predictive Co...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yubin Wang Yulin Li Zengqi Peng Hakim Ghazzai Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia 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 Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability.

关键词： Costs Reinforcement learning Numerical simulation Vehicle dynamics Task analysis robotics and automation Predictive control

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