An articulated limb motion planner for optimized movement

摘要

Abstract Task level animation of articulated figures, such as the human body, requires the ability to generate collision‐free goal‐directed motion of individual limbs in the presence of obstacles. This paper describes a new articulated limb motion planner for goal‐directed point‐to‐point reaching motions. The produced motion avoids obstacles while optimizing an objective function. This two‐phase algorithm uses heuristic guided Monte Carlo techniques to create a consistent underlying paradigm. The first phase consists of an existing potential field based random path planner which generates a population of candidate paths. This initial population is fed into the second phase, a genetic algorithm, which iteratively refines the population as it optimizes with respect to the objective function. The refinement process works on the principle of path coherency , the idea that a family of closely related collision‐free paths lies in the vicinity of a given collision‐free path. This paper focuses on seven different optimization functions. Optimized trajectories produced by the new motion planner are compared to those generated solely by the random path planner. The presented algorithm is flexible in that a wide range of objective functions can be optimized. Applications of the algorithm include task level animation, ergonomics and robotics.