HG-RRT*: Human-Guided Optimal Random Trees for Motion Planning
Document typeConference lecture
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
The paper deals with the problem of designing an RRT*-based planning algorithm that allows the user to guide the tree growth in a simple and transparent way. The key idea of the proposal is to create a planning algorithm, called HG-RRT*, that minimizes an optimization function over the configuration space where a state cost function is established. This state cost is defined as the combination of several potential fields. Each of these potential fields will attract the solution path or move it away from certain areas. The planning algorithm will try to minimize the path length, the motion effort and the variations of the cost along the path. The paper presents a description of the proposed approach as well as simulation results from a conceptual and an application example, including a thorough comparison with the TRRT planning algorithm.
Fumio Harashima Best Paper Award in Emerging Technologies, a la 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA 2015)
CitationGarcía, N., Suarez, R., Rosell, J. HG-RRT*: Human-Guided Optimal Random Trees for Motion Planning. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA 2015): Luxembourg, 8-11 September 2015". Luxembourg: Institute of Electrical and Electronics Engineers (IEEE), 2015.
- SIR - Robòtica Industrial i Servei - Ponències/Comunicacions de congressos 
- IOC - Institut d'Organització i Control de Sistemes Industrials - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos