Abstract
We consider the problem of planning an obstacle avoidance path for manipulators in cluttered environments especially with narrow passages. Compared to sampling-based planners, heuristic search-based planners are more suitable for such environments due to the consistent heuristic guidance. In order to solve the problem of search stagnation caused by inappropriate heuristic guidance, we use the Shared Multi-Heuristic A* (SMHA*) algorithm and predefine multiple inadmissible heuristics. Meanwhile, when the consistent heuristic guidance is correct and appropriate, in order to avoid the unnecessary inadmissible heuristics to increase the search burden, we improve it by adding heuristic-based stagnation detection for each extended node and the improved algorithm is called SD-SMHA*. Only when the algorithm detects that it ceases to make significant progress towards the goal, the predefined inadmissible heuristics will be introduced. Finally, multiple simulation experiments are carried out and the results show that the improved algorithm effectively improves the planning efficiency and planning success rate in different scenarios.
Highlights
In recent years, with the increasing use of manipulators, their application scenarios have become increasingly complex
This paper proposes an improved shared multi-heuristic A* search algorithm based on stagnation detection which we called SD-SMHA* algorithm
An improved shared multi-heuristic A* search algorithm based on stagnation detection which we called SD-SMHA* will be elaborated in detail
Summary
With the increasing use of manipulators, their application scenarios have become increasingly complex. One of the main drawbacks of them is that more dense trajectory states need to be described in order to reason about obstacles in complex environments and it will increase the computational cost significantly To overcome this problem, the Gaussian process motion planning family of algorithms [13]–[15] samples a few states on the initial trajectory and use Gaussian interpolation to query the trajectory at any time of interest. The Gaussian process motion planning family of algorithms [13]–[15] samples a few states on the initial trajectory and use Gaussian interpolation to query the trajectory at any time of interest It effectively speeds up the trajectory optimization. The heuristic search-based algorithm will be used to solve the problem of path planning for manipulators in cluttered environments, especially with narrow passages.
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