Abstract
Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length.
Highlights
Path planning is to find a route for the robots from the start position to the target position without colliding with obstacles [1,2]
Path planning with automatic collapse point generation (ACPG) and node growing algorithm (NGA) is called the improved distorted configuration space (IDCS) method, and the IDCS method with post-optimization is called the Opti-IDCS method in the following content. It was sufficiently discussed in [22] that the DC-space method could significantly decrease computation cost compared to classic path planning methods, it is still worth to evaluate the performance of the IDCS method in calculation efficiency and path length with the proposed modifications
The random-exploring rapid tree (RRT) method can provide largely unlike paths for different runs due to its inherent randomness [29], even though the path generation time for IDCS method and Opti-IDCS method vary for each running time, the generated paths are consistent when the environment with obstacles is fixed
Summary
Path planning is to find a route for the robots from the start position to the target position without colliding with obstacles [1,2]. The sampling-based methods [9,10], such as random-exploring rapid tree (RRT) algorithms [11] and probabilistic roadmap method [12], are randomized approaches with merits in providing a fast solution in high-dimensional maps and adopted as the path planning solution in many research works for robot manipulators [13]. The first problem is that the collapse points, where all the obstacles are distorted to in the DC-space, are hand-picked by users This requirement does slow down the process and could lead to improper choices of the collapse points and planning failures. The major contributions include (1) An automatic collapse point generation algorithm is proposed to calculate the locations where the obstacles are distorted, which provides an autonomous procedure for the DC-space method and avoids topology destruction due to bad choices of the collapse points.
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