Expanded Douglas–Peucker Polygonal Approximation and Opposite Angle-Based Exact Cell Decomposition for Path Planning with Curvilinear Obstacles

Abstract

The Expanded Douglas–Peucker (EDP) polygonal approximation algorithm and its application method for the Opposite Angle-Based Exact Cell Decomposition (OAECD) are proposed for the mobile robot path-planning problem with curvilinear obstacles. The performance of the proposed algorithm is compared with the existing Douglas–Peucker (DP) polygonal approximation and vertical cell decomposition algorithm. The experimental results show that the path generated by the OAECD algorithm with EDP approximation appears much more natural and efficient than the path generated by the vertical cell decomposition algorithm with DP approximation.

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

“…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. Map 4 in Figure 10 d seems to be an environment in which it is easy to verify the optimality and the planning time for the path-planning algorithm, and the cell decomposition algorithm, which increases the computation cost as the angle of obstacle increases, is an unfavorable environment [ 29 ]. Map 5 in Figure 10 e seems to be an environment in which it is also easy to verify the optimality and planning time of the path-planning algorithm; for the same reason as Map 4, the cell decomposition algorithm is an unfavorable environment.…”

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

“…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. Map 4 in Figure 10 (d) seems to be an environment in which it is easy to verify the optimality and the planning time for the pathplanning algorithm, and the Cell Decomposition algorithm, which increases the computation cost as the angle of obstacle increases, is an unfavorable environment [29]. Map 5 in Figure 10 (e) seems to be an environment in which it is also easy to verify the optimality and planning time of the pathplanning algorithm; for the same reason as Map 4, the cell decomposition algorithm is an unfavorable environment.…”

Special Feature on Advanced Mobile Robotics

“…Path planning is a challenging issue in robotic tasks. Jung et al [29] proposed a new path planning method to handle curvilinear obstacles. Zeng et al [30] presented reinforcement learning with subgoal graphs, leading to near-optimal subgoal sequences as motion-planning policies.…”