Motion Planning With Convex Swept Volume Segments

1996 ◽  
Author(s):  
Chiusheng Wu ◽  
Roger W. Mayne
Keyword(s):  
Motion Planning ◽  
Moving Objects ◽  
Planning Process ◽  
Optimization Methods ◽  
Three Dimensions ◽  
Robot Motion ◽  
Optimization Strategy ◽  
Swept Volumes ◽  
Volume Strategy ◽  
Swept Volume

Abstract This paper presents a swept volume approach for distance-to-contact computation in motion planning. The approach is based on approximating the swept volumes of moving objects with convex segments formulated for the use of quadratic programming in distance-to-contact calculations. This swept volume strategy is designed to conservatively estimate the distance-to-contact between moving and static objects and is well suited for the planning of motions using optimization methods. It has been adapted to obtain convenient distance-to-contact gradient information which is an important factor in the application of efficient optimization algorithms to the motion planning process. The paper describes the generation of the swept volume segments and applies the process to examples of robot motion planning in three dimensions using an SQP optimization strategy.

scholarly journals Robot Motion Planning and Control for Moving Objects.

1993 ◽  
Vol 59 (567) ◽  
pp. 3397-3404
Author(s):  
Takeshi Sakaguchi ◽  
Masanori Fujita ◽  
Yasuhiro Masutani ◽  
Fumio Miyazaki
Keyword(s):  
Motion Planning ◽  
Moving Objects ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Planning And Control ◽  
And Control

scholarly journals Robot Motion Planning for Moving Objects

1993 ◽  
Vol 26 (2) ◽  
pp. 985-990
Author(s):  
T. Sakaguchi ◽  
S. Sato ◽  
F. Miyazaki
Keyword(s):  
Motion Planning ◽  
Moving Objects ◽  
Robot Motion ◽  
Robot Motion Planning

scholarly journals Robot Motion Planning Benchmarking and Optimization using Motion Planning Pipeline

2021 ◽  
Author(s):  
Shuai Liu ◽  
Pengcheng Liu
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Optimization Methods ◽  
Low Complexity ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Sampling Algorithm ◽  
Original Algorithm ◽  
Complex Scenes ◽  
Planning Algorithm

Abstract Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through Motion Planning Pipeline and Planning Request Adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.

scholarly journals Benchmarking and optimization of robot motion planning with motion planning pipeline

2021 ◽  
Author(s):  
Shuai Liu ◽  
Pengcheng Liu
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Optimization Methods ◽  
Low Complexity ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Sampling Algorithm ◽  
Original Algorithm ◽  
Complex Scenes ◽  
Planning Algorithm

AbstractAlgorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.

scholarly journals Evaluating Direct Transcription and Nonlinear Optimization Methods for Robot Motion Planning

2016 ◽  
Vol 1 (2) ◽  
pp. 946-953 ◽  
Author(s):  
Diego Pardo ◽  
Lukas Moller ◽  
Michael Neunert ◽  
Alexander W. Winkler ◽  
Jonas Buchli
Keyword(s):  
Motion Planning ◽  
Nonlinear Optimization ◽  
Optimization Methods ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Direct Transcription ◽  
Nonlinear Optimization Methods

scholarly journals Motion Planning of Ground Simulator for Space Instable Target Based on Energy Saving

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 368
Author(s):  
Xinlin Bai ◽  
Xiwen Li ◽  
Zhen Zhao ◽  
Mingyi Yang ◽  
Zhang Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Energy Saving ◽  
Planning Process ◽  
Minimum Energy ◽  
Robot Motion ◽  
Dijkstra Algorithm ◽  
Minimum Energy Consumption ◽  
Motion Simulator ◽  
Save Energy

In order to achieve the high-precision motion trajectory in ground experiment of space instable target (SIT) while reducing the energy consumption of the motion simulator, a robot motion planning method based on energy saving is proposed. Observable-based ground robot motion experiment system for SIT is designed and motion planning process is illustrated. Discrete optimization mathematical model of energy consumption of motion simulator is established. The general motion form of the robot joints in ground test is given. The optimal joint path of motion simulator based on energy consumption under discontinuous singularity configuration is solved by constructing the complete energy consumption directed path and Dijkstra algorithm. An improved method by adding the global optimization algorithm is used to decouple the coupled robot joints to obtain the minimum energy consumption path under the continuous singularity configuration of the motion simulator. Simulations are carried out to verify the proposed solution. The simulation data show that total energy saving of motion simulator joints adopting the proposed method under the condition of non-singularity configuration, joints coupled motion with continuous singularity configuration, and coexistence of non-singularity path and continuous singularity path are, respectively, 72.67%, 28.24%, and 62.23%, which proves that the proposed method can meet the requirements of ground motion simulation for SIT and effectively save energy.

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