TY - GEN
T1 - Finding Better Robot Trajectory by Linear Constrained Quadratic Programming
AU - Liu, Yizhou
AU - Zha, Fusheng
AU - Li, Mantian
AU - Guo, Wei
AU - Wang, Xin
AU - Jia, Wangqiang
AU - Caldwell, Darwin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning (SBMP) algorithms have been shown to be effective for these high-dimensional systems. But the biggest flaw of SBMP methods is that the trajectory is a combination of multiple linear paths under configuration space, which causes a lot of unnecessary acceleration and jerk. So how to optimize the solution of SBMPs efficiently is a key to improve the robot trajectory quality. In this paper, a robot trajectory optimization method based on linear constrained quadratic programming is proposed, which only need collision query, no distance or penetration calculations, and no prior knowledge of the environment. We use a series of simulation to prove the effectiveness and correctness of the methods.
AB - Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning (SBMP) algorithms have been shown to be effective for these high-dimensional systems. But the biggest flaw of SBMP methods is that the trajectory is a combination of multiple linear paths under configuration space, which causes a lot of unnecessary acceleration and jerk. So how to optimize the solution of SBMPs efficiently is a key to improve the robot trajectory quality. In this paper, a robot trajectory optimization method based on linear constrained quadratic programming is proposed, which only need collision query, no distance or penetration calculations, and no prior knowledge of the environment. We use a series of simulation to prove the effectiveness and correctness of the methods.
UR - https://www.scopus.com/pages/publications/85092671813
U2 - 10.1109/ICARM49381.2020.9195329
DO - 10.1109/ICARM49381.2020.9195329
M3 - 会议稿件
AN - SCOPUS:85092671813
T3 - ICARM 2020 - 2020 5th IEEE International Conference on Advanced Robotics and Mechatronics
SP - 252
EP - 256
BT - ICARM 2020 - 2020 5th IEEE International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2020
Y2 - 21 August 2019 through 23 August 2019
ER -