Work

KRRF: Kinodynamic Rapidly-Exploring Random Forest Algorithm for Multi-Goal Motion Planning

Year: 2024

Type: article

Abstract: The problem of kinodynamic multi-goal motion planning is to find a trajectory over multiple target locations with an apriori unknown sequence of visits. The objective is to minimize the cost of the trajectory planned in a cluttered environment for a robot with a kinodynamic motion model. This problem has yet to be efficiently solved as it combines two NP-hard problems, the Traveling Salesman Problem (TSP) and the kinodynamic motion planning problem. We propose a novel approximate method called Kinodynamic Rapidly-exploring Random Forest (KRRF) to find a collision-free multi-goal trajectory that satisfies the motion constraints of the robot. KRRF simultaneously grows kinodynamic trees from all targets towards all other targets while using the other trees as a heuristic to boost the growth. Once the target-to-target trajectories are planned, their cost is used to solve the TSP to find the sequence of targets. The final multi-goal trajectory satisfying kinodynamic constraints is planned by guiding the RRT-based planner along the target-to-target trajectories in the TSP sequence. Compared with existing approaches, KRRF provides shorter target-to-target trajectories and final multi-goal trajectories with 1.1--2 times lower costs while being computationally faster in most test cases. The method will be published as an open-source library.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2024.3478570

Authors: Petr Ježek, Michal Minařík, Vojtěch Vonásek, Robert Pěnička

Institution: Czech Technical University in Prague

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Grantová Agentura České Republiky

Award: Sampling-based motion planning in scenarios with narrow passages