Title :
Approaches for heuristically biasing RRT growth
Author :
Urmson, Chris ; Simmons, Reid
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.
Keywords :
mobile robots; path planning; tree searching; heuristic quality function; rapidly-exploring random tree search algorithm; search guide; Convergence; Cost function; Helicopters; Mobile robots; Orbital robotics; Probability distribution; Space exploration; USA Councils;
Conference_Titel :
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7860-1
DOI :
10.1109/IROS.2003.1248805
