Optimal 3D Path Planning for a 9 DOF Robot Manipulator with Collision Avoidance

Abstract

This paper describes development of an optimal 3D path planner with collision avoidance for a 9 DOF robot manipulator. The application of the robot manipulator will be on an unmanned oil platform where it will be used for inspection. Most of the time the robot manipulator will follow a pre-programmed collision-free path specified by an operator. Situations where it is desirable to move the end effector from the current position to a new position without specifying the path in advance might occur. To make this possible a 3D path planner with collision avoidance is needed. The path planner presented in this paper is based on the well known Probabilistic Roadmap method (PRM). One of the main challenges using the PRM is to make a roadmap covering the entire collision free Configuration space, Cfree, and connect it into one connected component. It is shown by empirical testing that using a combination of the Bridge Sampling technique and a simple Random sampling technique gives best Coverage of the Cfree space and highest Connectivity in the roadmap for the given environment. An algorithm that increases the Connectivity and sometimes provide Maximal Connection is also described. A backup procedure that can be executed on-line if a query fails is also presented. The backup procedure is slow, but it increases the chances of succeeding a query if the goal is in a difficult area. It is also investigated if the coverage and connectivity can be further improved by using the potential field planner when connecting the waypoints. Empirical testing showed that the improvements of Coverage and Connectivity were limited and the sampling and query time increased. The query time for a roadmap containing 400 nodes and one containing 1000 nodes was compared. It turned out that a large roadmap did not necessarily affect the query time negative because it made it easier to connect the start and goal nodes. Three existing path smoothing algorithms and a new algorithm, called Deterministic Shortcut, were implemented and tested. Empirical testing showed that the Deterministic Shortcut algorithm outperformed the others when it came to path smoothing versus time.