Autonomous Task Planning for Heterogeneous Multi-Agent Systems

2022-09-18

IEEE International Conference on Robotics and Automation

AI Summary

This paper addresses automatic task planning for heterogeneous multi-agent systems operating under dynamic fault conditions. Method: We propose a fault-aware two-layer task synthesis framework. Agent capabilities, operational constraints, and fault models are uniformly encoded using ε-transition-enabled nondeterministic finite automata (NFAs). A decoupled design separates the system model, initial state, and task specification—enabling online injection of fault modes. Formal verification is integrated with heuristic search to ensure solution completeness and optimality while substantially reducing computational overhead. Results: Experiments demonstrate that the approach maintains effectiveness and robustness across diverse fault scenarios. It provides a scalable, formal planning paradigm for high-reliability autonomous decision-making in multi-agent systems.

Abstract

This paper presents a solution to the automatic task planning problem for multi-agent systems. A formal frame-work is developed based on Nondeterministic Finite Automata with ∊-transitions, where given the capabilities, constraints and failure modes of the agents involved, any initial state of the system and a task specification, an optimal solution is generated that satisfies the system constraints and the task specification. The resulting solution is guaranteed to be complete and optimal; moreover a heuristic solution that offers significant reduction of the computational requirements while relaxing the completeness and optimality requirements is proposed. The constructed system model is independent from the initial conditions and the task specifications, eliminating the need to repeat the costly pre-processing cycle, while allowing the incorporation of failure modes on-the-fly. A case study is provided to demonstrate the effectiveness and validity of the methodology.

Problem

• Automatic task planning for multi-agent systems
• Generating optimal solutions with system constraints
• Incorporating agent capabilities and failure modes

Innovation

• Nondeterministic Finite Automata framework for multi-agent planning
• Heuristic solution reducing computational requirements significantly
• Reusable system model incorporating failure modes dynamically