<title>Adaptive and less-complex path-planning behavior for mobile robots</title>

Abstract

ABSTRACT The objective of path planning is to find a sequence of states that a system has to visit in order to attain the goal state. Because of their real-time efficiency, potential field present a powerful heuristic to guide thissearch. However, potential field approaches can not guarantee goal attainability. They are often referred to aslocal methods and are used in conjunction with a global path planning method to ensure completeness of thepath planning algorithm. The present work introduces a novel methodology for path planning which combines thereal-time efficiency of potential field with goal-attainability characteristic of global (such as A*).The algorithm of this work is: i) free from local minima; ii) capable of considering arbitrary-shaped obstacles (nogeometric approximation is required); iii) computationally less complex than previous search methods; and iv) ableto handle obstacle avoidance and goal attainability at the same time. At the first step a new probabilistic scheme,based on absorbing Markov chains, is presented for global planning inside structured environments, such as office,etc. The potential field method is then reformulated for adaptive path planning among modeled and new obstacles.Keywords : Mobile robots, Motion planning, Potential field, Adaptive planning, Markov Chains, HarmonicFunctions, BIE