Variable Dimensional Scaling Method: A Novel Method for Path Planning and Inverse Kinematics

Abstract

Traditional methods for solving the inverse kinematics of a hyper-redundant manipulator (HRM) can only plan the path of the end-effector with a complicated solving process, where obstacle avoidance is also not considered. To solve the above problems, a novel method for solving inverse kinematics of HRM is proposed in this paper: the variable dimension scaling method (VDSM), which can solve complex inverse kinematics while avoiding obstacles. Through this method, the path of the end-effector is scaled under a certain proportion and is adjusted depending on the position of the obstacle, which has good universality. The number of link angles changed is as small as possible in the process of achieving the end-effector moving along the desired path. With the redundancy of HRM, obstacle avoidance can be implemented in any environment by the proposed method. Through simulation and experiments in different environments, the above advantages of VDSM are verified.

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

“…It can be seen from the table that most methods cannot take into account multipleobstacle avoidance conditions at the same time. It can be seen from the simulation results that method [7] and method [9] not only cannot realize real-time path planning, but also the simulation time in the static path is longer than that in the present method. Although global real-time path planning can be achieved for multiple obstacles in paper [21], the PSO algorithm is adopted in the algorithm, which takes a certain amount of time to perform iterative operations in the process of solving the key points of the path.…”

“…Path planning is a mapping from perceptual space to behavioral space and the planning method is one of the research hotspots at present. There are a variety of path planning methods commonly used, such as the potential energy method [1], heuristic search algorithm [2], Dijkstra algorithm [3], LPA* algorithm (Life Planning A*) [4], Floyd algorithm [5], PRM algorithm [6], RRT algorithm [7], unit division method [8] and intelligent algorithm [9][10][11]. However, these path planning algorithms cannot satisfy global adjustment, real-time change and multi-obstacle avoidance at the same time.…”