The Experts below are selected from a list of 21681 Experts worldwide ranked by ideXlab platform

Feng Qi - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory Planning for a four wheel steering vehicle
    International Conference on Robotics and Automation, 2001
    Co-Authors: Danwei Wang, Feng Qi
    Abstract:

    This paper develops a Trajectory Planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the Trajectory Planning. A two-part Trajectory Planning algorithm consists of the steering Planning and velocity Planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.

  • ICRA - Trajectory Planning for a four-wheel-steering vehicle
    Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001
    Co-Authors: Danwei Wang, Feng Qi
    Abstract:

    This paper develops a Trajectory Planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the Trajectory Planning. A two-part Trajectory Planning algorithm consists of the steering Planning and velocity Planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.

Danwei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory Planning for a four wheel steering vehicle
    International Conference on Robotics and Automation, 2001
    Co-Authors: Danwei Wang, Feng Qi
    Abstract:

    This paper develops a Trajectory Planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the Trajectory Planning. A two-part Trajectory Planning algorithm consists of the steering Planning and velocity Planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.

  • ICRA - Trajectory Planning for a four-wheel-steering vehicle
    Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001
    Co-Authors: Danwei Wang, Feng Qi
    Abstract:

    This paper develops a Trajectory Planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the Trajectory Planning. A two-part Trajectory Planning algorithm consists of the steering Planning and velocity Planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.

Dechao Chen - One of the best experts on this subject based on the ideXlab platform.

  • A New Trajectory-Planning Beetle Swarm Optimization Algorithm for Trajectory Planning of Robot Manipulators
    IEEE Access, 2019
    Co-Authors: Lei Wang, Qing Wu, Shuai Li, Dechao Chen
    Abstract:

    Based on a heuristic optimization algorithm, this paper proposes a new algorithm named Trajectory-Planning beetle swarm optimization (TPBSO) algorithm for solving Trajectory Planning of robots, especially robot manipulators. Firstly, two specific manipulator Trajectory Planning problems are presented as the practical application of the algorithm, which are point-to-point Planning and fixed-geometric-path Planning. Then, in order to verify the effectiveness of the algorithm, this paper develops a control model and conducts numerical experiments on two Planning tasks. Moreover, it compares with existing algorithms to show the superiority of our proposed algorithm. Finally, the results of numerical comparisons show that algorithm has a relatively faster computational speed and better control performance without increasing computational complexity.

Xiaoyu Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Trajectory Planning of robot manipulators based on unit quaternion
    2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017
    Co-Authors: Yikun Gu, Xiaoyu Zhao
    Abstract:

    Trajectory Planning has been a common and important topic in robotics since its generation. As a powerful tool, it can enhance the performance of the robots in manufacturing industry. In this paper, a Trajectory Planning method based on unit quaternion was proposed. Unit quaternions were used to represent the orientations of the target as there are no more singularities. The Trajectory was Planning in Cartesian space using Paul's method and the orientations were intermediated using spherical linear interpolation. Experiments on PUMA 560, the analytical inverse kinematics solutions were figured out to generate the joint Trajectory for the execution phase. A virtual simulation platform was built up to verify and illustrate the Trajectory Planning algorithm. The data of target can be updated with time which is meaningful to the visual servo system.

  • AIM - Trajectory Planning of robot manipulators based on unit quaternion
    2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017
    Co-Authors: Yikun Gu, Xiaoyu Zhao
    Abstract:

    Trajectory Planning has been a common and important topic in robotics since its generation. As a powerful tool, it can enhance the performance of the robots in manufacturing industry. In this paper, a Trajectory Planning method based on unit quaternion was proposed. Unit quaternions were used to represent the orientations of the target as there are no more singularities. The Trajectory was Planning in Cartesian space using Paul's method and the orientations were intermediated using spherical linear interpolation. Experiments on PUMA 560, the analytical inverse kinematics solutions were figured out to generate the joint Trajectory for the execution phase. A virtual simulation platform was built up to verify and illustrate the Trajectory Planning algorithm. The data of target can be updated with time which is meaningful to the visual servo system.

Fei-yue Wang - One of the best experts on this subject based on the ideXlab platform.

  • Parking Like a Human: A Direct Trajectory Planning Solution
    IEEE Transactions on Intelligent Transportation Systems, 2017
    Co-Authors: Zhiheng Li, Li Li, Fei-yue Wang
    Abstract:

    Parking control problems remain to be fully solved for autonomous vehicles. Existing approaches usually first design a reference parking Trajectory that does not exactly match vehicle dynamic constraints and then apply certain online negative feedback control to make the vehicle roughly track this reference Trajectory. In this paper, we propose a novel Trajectory Planning method that directly links the actual parking trajectories and the steering actions to find the best parking Trajectory. Tests show that this new approach has high reliability and less computation cost. Moreover, we also discuss how to counter with Trajectory Planning errors that are caused by model uncertainty in this paper. We show that an appropriate combination of feedforward Trajectory Planning and online feedback control can solve such problems.

  • An integrated design framework for driver/passenger-oriented Trajectory Planning
    Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, 2003
    Co-Authors: Li Li, Fei-yue Wang
    Abstract:

    This paper presents an integrated Trajectory Planning framework for the front wheel steering vehicles that are equipped with digital map systems. It first addresses the importance of the driver and the passengers' requirement for automatic driving. After introducing the concept of optimal driver/passenger-oriented driving performance, several driving performance indices are introduced as following in order to provide a measurement of the automatic driving process. Based on these indices, the whole optimal Trajectory Planning problem is divided into two incorporated sub problems: the longitudinal Trajectory Planning problem and the lateral steering Trajectory generation problem. Since the driving performance indices are primarily determined by the longitudinal driving procedure of the vehicle, the desired optimal longitudinal Trajectory is formulated first. With the obtained velocity setting of the longitudinal Trajectory Planning problem, the lateral steering Trajectory generation problem is solved based on cell mapping method then.