Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
🤖 AI Summary
Multi-robot task allocation faces three key challenges: task priority constraints, intra-task coordination, and coalition-based collaboration among heterogeneous robots.
Method: This paper proposes an online iterative reallocation algorithm that jointly models task dependencies via a task dependency graph and captures coalition-scale efficiency gains through a coalition-size effect model. To address the underlying NP-hard optimization problem, the method employs a network-flow-based approximation, validated via mixed-integer programming and greedy heuristics. High-fidelity simulation integrates realistic robot dynamics and physics-based modeling.
Contribution/Results: Compared to offline approaches, the algorithm significantly improves robustness against task failures and model uncertainties. Experimental evaluation—across both stochastic task settings and real-world mission scenarios—demonstrates superior plan quality, enabling effective modeling, dynamic replanning, and coordinated execution of complex multi-robot missions.
Application Category
📝 Abstract
We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximate solutions efficiently. A novel online algorithm performs iterative re-allocation, providing robustness to task failures and model inaccuracies to achieve higher performance than offline approaches. We comprehensively evaluate the algorithms in a testbed with random missions and reward functions and compare them to a mixed-integer solver and a greedy heuristic. Additionally, we validate the overall approach in an advanced simulator, modeling reward functions based on realistic physical phenomena and executing the tasks with realistic robot dynamics. Results establish efficacy in modeling complex missions and efficiency in generating high-fidelity task plans while leveraging task relationships.
Problem
Research questions and friction points this paper is trying to address.
Addressing multi-robot task allocation with complex precedence constraints
Efficiently approximating NP-hard reward maximization through network flow algorithms
Providing online robustness to task failures and model inaccuracies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Network flow-based algorithms for approximation
Online iterative re-allocation for robustness
Task graph modeling complex precedence relationships
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