A Benchmark for Optimal Multi-Modal Multi-Robot Multi-Goal Path Planning with Given Robot Assignment

2025-03-05

AI Summary

Existing priority-based and synchronization-assumption methods for coordinated multi-target path planning of heterogeneous robots in shared industrial workspaces lack optimality and completeness. Method: This paper formulates the problem as an optimal planning task in a single continuous state space, supporting multimodal motion, dynamic environments, and collaborative tasks (e.g., object handover). We propose a hybrid state-space expansion algorithm integrating RRT* and PRM*, directly respecting heterogeneous robot dynamics and mode-switching constraints—bypassing grid-based discretization. Contribution/Results: We introduce the first continuous-space benchmark suite covering diverse scales, time horizons, and collaboration complexities; all code and evaluation infrastructure are open-sourced. Experiments validate theoretical completeness, asymptotic optimality, and practical scalability across real-world robotic configurations.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In many industrial robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as quickly as possible. Such settings can be treated as multi-modal multi-robot multi-goal path planning problems, where each robot has to reach an ordered sequence of goals. Existing approaches to this type of problem solve this using prioritization or assume synchronous completion of tasks, and are thus neither optimal nor complete. We formalize this problem as a single path planning problem and introduce a benchmark encompassing a diverse range of problem instances including scenarios with various robots, planning horizons, and collaborative tasks such as handovers. Along with the benchmark, we adapt an RRT* and a PRM* planner to serve as a baseline for the planning problems. Both planners work in the composite space of all robots and introduce the required changes to work in our setting. Unlike existing approaches, our planner and formulation is not restricted to discretized 2D workspaces, supports a changing environment, and works for heterogeneous robot teams over multiple modes with different constraints, and multiple goals. Videos and code for the benchmark and the planners is available at https://vhartman.github.io/mrmg-planning/.

Problem

• Multi-robot path planning with ordered goals
• Optimal planning in dynamic, heterogeneous environments
• Benchmark for diverse multi-modal robot tasks

Innovation

• Formalizes multi-robot path planning as single problem
• Adapts RRT* and PRM* planners for composite robot space
• Supports heterogeneous robots in dynamic environments