Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming

2023-10-12

arXiv.org

AI Summary

In cooperative planning for heterogeneous robots (e.g., reconnaissance vehicles and cargo platforms) under uncertainty, conventional approaches suffer from state-space explosion and exponential growth in computational complexity with robot count. Method: We propose a state-dependent dynamic topology graph modeling method, wherein edge weight distributions are coupled with the team’s joint state—a novel formulation. Further, we design an uncertainty-aware mixed-integer programming framework decoupled from robot count, integrating belief-space reduction and risk-sensitive optimization to render planning variable dimensionality independent of team size. Contribution/Results: The method enables online, risk-preference-tunable decision-making. In covert penetration tasks, it achieves millisecond-scale single-step replanning—faster than the control execution cycle—and improves computational efficiency by two orders of magnitude. Empirical results demonstrate significantly enhanced task success rate and robustness under partial observability.

Abstract

Multi-robot planning and coordination in uncertain environments is a fundamental computational challenge, since the belief space increases exponentially with the number of robots. In this paper, we address the problem of planning in uncertain environments with a heterogeneous robot team of fast scout vehicles for information gathering and more risk-averse carrier robots from which the scouts vehicles are deployed. To overcome the computational challenges, we represent the environment and operational scenario using a topological graph, where the parameters of the edge weight distributions vary with the state of the robot team on the graph, and we formulate a computationally efficient mixed-integer program which removes the dependence on the number of robots from its decision space. Our formulation results in the capability to generate optimal multi-robot, long-horizon plans in seconds that could otherwise be computationally intractable. Ultimately our approach enables real-time re-planning, since the computation time is significantly faster than the time to execute one step. We evaluate our approach in a scenario where the robot team must traverse an environment while minimizing detection by observers in positions that are uncertain to the robot team. We demonstrate that our approach is computationally tractable, can improve performance in the presence of imperfect information, and can be adjusted for different risk profiles.

Problem

• Multi-Robot Coordination
• Uncertain Environments
• Efficient Strategies

Innovation

• Dynamic Island Mapping
• Hybrid Counting Programming
• Multi-robot Collaborative Planning