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Liuao Pei

Bio: Liuao Pei is an academic researcher. The author has contributed to research in topics: Computer science. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Papers
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Journal ArticleDOI
TL;DR: This work proposes a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints, leveraging the differential property of car-like robots to simplify the trajectory planning problem.
Abstract: —As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, there is no efficient trajectory planning solution capable of spatial-temporal joint optimization due to nonholonomic dynamics, particularly in the presence of unstruc- tured environments and dynamic obstacles. To bridge the gap, we propose a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints. By leveraging the differential flatness property of car-like robots, we use flat outputs to analytically formulate all feasibility constraints to simplify the trajectory planning problem. Moreover, obstacle avoidance is achieved with full dimensional polygons to generate less conservative trajectories with safety guarantees, especially in tightly constrained spaces. We present comprehensive bench- marks with cutting-edge methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes as open-source packages with the purpose for the reference of the research community. 1

5 citations

28 Aug 2022
TL;DR: In this article , the authors propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints by leveraging the differential flatness property of car-like robots.
Abstract: As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community

Cited by
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Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable autonomous parking, which has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis model to determine the better mode under various scenarios.
Abstract: Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
12 Oct 2022
TL;DR: In this article , a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments is proposed, where 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.
Abstract: 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.
Journal ArticleDOI
TL;DR: A decentralized framework for car-like robotic swarm which is capable of real- time planning in unstructured environments and validated the robustness of the system in simulation and real-world experiments is proposed.
Abstract: —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 unstructured 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, penalty is imposed on 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 from potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.
Proceedings ArticleDOI
29 May 2023
TL;DR: Wang et al. as mentioned in this paper proposed a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments, where the whole-body trajectory was modeled as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of extra joints.
Abstract: Service robots have attracted extensive attention due to specially designed functions, such as mobile manipulators or robots with extra structures. For robots that have changing shapes, autonomous navigation in the real world presents new challenges. In this paper, we propose a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments. We model the whole-body trajectory as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of the extra joints. These constraints are converted into soft constraints, and an activation function for dense sampling is applied to avoid nonlinear mutations. In addition, we guarantee the safety of full shape by limiting the system's distance from obstacles. To comprehensively simulate a large extent of height and width changes, we designed a novel Shape-Changing Robot with a Differential Base (SCR-DB). Our global trajectory optimization gives a smooth and collision-free trajectory for SCR-DB at a low computational cost. We present vast simulations and real-world experiments to validate our performance, including coupled whole-body and independent differential-driven vehicle motion planning.
Proceedings ArticleDOI
21 Apr 2023
TL;DR: In this paper , an autonomous driving path planning algorithm based on quadratic programming (QP) is proposed to deal with the scenario that require high flexibility in obstacle avoidance, such as urban roads.
Abstract: In order to deal with the scenario that require high flexibility in obstacle avoidance, such as urban roads, this paper proposes an autonomous driving path planning algorithm based on quadratic programming (QP). The algorithm proposes an obstacle avoidance cost based on Frenet frame, which can not only satisfy the characteristics of the positive definite quadratic form of the cost function, but also add the obstacle avoidance cost as a soft constraint, and then adapts an iterative solution strategy. The candidate paths are generated by solving the QP problem, the algorithm will output the optimal path, which satisfy the collision detection. The simulation test shows that the algorithm can deal with nudge, lane change and complex obstacle avoidance scenarios.