Manipulator control with superquadric artificial potential functions: theory and experiments

Abstract

Previous work in arti cial potentials has demonstrated the need for an obstacle avoidance p otential that closely models the obstacle, yet does not generate local minima in the workspace of the manipulator. This paper presents a potential function based on superquadrics, which closely models a large class of object shapes. This potential function also prevents the creation of local minima when it is added to spherically symmetric attractive wells. We introduce two compatible forms of the superquadric potential function: one for obstacle avoidance, and another for obstacle approach. We have implemented the avoidance and approach potentials in simulations. In these simulations the end e ector of the manipulator experiences an attractive force f r om a global spherical well, while the end e ector and each of the links experience r epulsive forces from all of the objects. We have also experimentally implemented the avoidance p otentials on the CMU DDARM II. The results demonstrate successful obstacle avoidance and approach, and exhibit an improvement over existing potential schemes.

Soft-Computing Techniques for the Trajectory Planning of Multi-Robot Manipulator Systems

“…While some previous www.intechopen.com research has been done in constructing potential fields free from local minima, other research has focused on the development of strategies to identify and move away from local minima within the planning algorithm. Following on from the original approach proposed in (Khatib, 1986), (Volpe & Khosla, 1990) developed elliptical potential functions called superquadric artificial potential field functions which, from the results, do not generate local minima when used to represent the workspace in the cartesian space, but the problem of local minima is still present when used with the Cspace representation. Later on, Rimon & Koditschek, 1992) introduced analytical potential fields in the C-space for ball or star shaped obstacles which limited its application to other obstacle arrangements.…”

Path Planning of Mobile Robot by using Modified Optimized Potential Field Method

“…In this work positions and velocities of robots, obstacles, and goals are considered in the functions of potential field [7]. GNRON problem (goals non-reachable with obstacle nearby), which is a common drawback in most potential field methods, is identified by Ge and Cui [8] and Volpe and Khosla [9]. GNRON problem can be solved using their proposed potential field method.…”

Real-Time Path Planning in Unknown Environments Using a Virtual Hill

“…In other words, the robot cannot predict local minima before experiencing the environment. An avoidance of a local minimum has been an active research topic in potential field based path planning (Chang, 1996;Cho and Kwon, 1996;Connolly et al, 1990; Connolly, 1992;Janabi-Sharifi and Vinke, 1993;Kim and Khosla, 1992; Lee and Park, 1991;McFetridge and Yousef-Ibrahim, 1998;Park and Lee, 2003; Rimom and Koditschek, 1992;Volpe and Khosla, 1990). However, the previous solutions were limited to simple formations of obstacles or available for path planning in known environments.…”