Subplanner Algorithm to Escape from Local Minima for Artificial Potential Function Based Robotic Path Planning

Abstract

This paper proposes a subplanner algorithm, which will be referred to as attractive force rotation and restoration, to compensate the local minima problem under the artificial potential based planner. The key role of the proposed subplanner is to build a navigating path for escaping from the local minima by rotating and restoring the attractive force. A mobile manipulator was adopted as our robotic application to substantiate the capability of the proposed subplanner under various simulation scenarios in which the mobile manipulator finds a path to a target object without collision against stationary/moving obstacles. The simulation results substantiated that the compensated artificial potential based planner successfully found a path to guide the mobile manipulator to the target object in the presence of the local minima.

Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

“…Map 1 in Figure 10 a seems to be an environment in which it is easy to verify the completeness of the path-planning algorithm. Map 2 in Figure 10 b seems to be an environment in which it is also easy to verify the completeness of the path-planning algorithm, and the environment is mainly used to show the solution for the Local Minima problem [ 28 ] in the artificial potential field algorithm [ 26 ]. Map 3 in Figure 10 c seems to be an environment in which it is easy to verify the optimality and completeness of the path-planning algorithm and is an environment that is unfavorable to random sampling path-planning algorithms such as the RRT algorithm.…”

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

“…These maps are part of the experimental environment [27] proposed by Jihee Han in 2017, and the following characteristics and efficiency of performance measures are expected for each map. Figure 16a shows Map 1, an environment in which the completeness of the path planning method can be easily verified, which is also an environment mainly used to show the local minima problem solving in the potential field algorithm [28]. Figure 16b shows Map 2, in which the optimality and completeness of the path planning method can be verified.…”

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

“…However, there are several drawbacks to potential fields. First, mobile robots often fall into the local minimum in potential fields [7][8][9]. The local minimum is the point at which the mobile robot cannot exit from an area with adjacent obstacles.…”