Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots
원문 초록
An important capability of autonomous multi-robot systems is to prevent collision among the individual robots. One approach to this problem is to plan conflict-free trajectories and let each of the robots follow its pre-planned trajectory. A widely used practical method for multi-robot trajectory planning is prioritized planning, which has been shown to be effective in practice, but is in general incomplete. Formal analysis of instances that are provably solvable by prioritized planning is still missing. Moreover, prioritized planning is a centralized algorithm, which may be in many situations undesirable. In this paper we a) propose a revised version of prioritized planning and characterize the class of instances that are provably solvable by the algorithm and b) propose an asynchronous decentralized variant of prioritized planning, which maintains the desirable properties of the centralized version and in the same time exploits the distributed computational power of the individual robots, which in most situations allows to find the joint trajectories faster. The experimental evaluation performed on real-world indoor maps shows that a) the revised version of prioritized planning reliably solves a wide class of instances on which both classical prioritized planning and popular reactive technique ORCA fail and b) the asynchronous decentralized algorithm provides solution faster than the previously proposed synchronized decentralized algorithm.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Epona: Autoregressive Diffusion World Model for Autonomous Driving
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning
CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
토론
이 논문과 연결된 포럼 글이 아직 없습니다.