Proceedings Article10.1109/ICMA.2019.8816541
A Motion Planning Algorithm Based on Trajectory Optimization with Workspace Goal Region
Kai Mi,Hao Peng,Jun Zheng,Yunkuan Wang,Hu Jianhua +4 more
- 01 Aug 2019
TL;DR: The results show that the proposed algorithm can quickly plan an obstacle avoidance trajectory in joint space and the quality of the trajectory is improved effectively compared to randomly specifying a goal configuration.
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Abstract: We consider a motion planning problem with workspace goal region in obstacle environments and the task is to move the end-effector into a specific goal region without posture constraints. In order to improve the quality of the planning trajectory, we present a goal region constraint algorithm based on Gaussian process motion planning. We construct a distance field for the irregular goal region in advance. The closest distance and direction from any location of the workspace to the specific goal region can be easily computed. Combined with the original method, we define a goal-region-constrained likelihood which specifies the probability that the position of end-effector is within the specific goal region and move the end-effector to a better position by numerical optimization. Finally, multiple simulation experiments are carried out and the results show that the proposed algorithm can quickly plan an obstacle avoidance trajectory in joint space and the quality of the trajectory is improved effectively compared to randomly specifying a goal configuration.
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References
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
12.7K
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki,P. Svestka,Jean-Claude Latombe,Mark H. Overmars +3 more
- 01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
•Journal Article
Rapidly-exploring random trees : a new tool for path planning
TL;DR: The Rapidly-exploring Random Tree (RRT) as discussed by the authors is a data structure designed for path planning problems with high degrees of freedom and non-holonomic constraints, including dynamics.
4.5K
Optimal and efficient path planning for partially-known environments
Anthony Stentz
- 08 May 1994
TL;DR: A new algorithm, D*, is introduced, capable of planning paths in unknown, partially known, and changing environments in an efficient, optimal, and complete manner.
STOMP: Stochastic trajectory optimization for motion planning
Mrinal Kalakrishnan,Sachin Chitta,Evangelos A. Theodorou,Peter Pastor,Stefan Schaal +4 more
- 09 May 2011
TL;DR: It is experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.
1K