Task Allocation Strategies for Heterogeneous Multi-Robot Teams: A Comprehensive Review and Taxonomy

Abstract

Heterogeneous multi-robot teams comprising agents with diverse capabilities, sensors, and mobility characteristics require sophisticated task allocation strategies to optimally assign mission objectives while respecting individual constraints and maximizing collective performance. This comprehensive review examines task allocation approaches for heterogeneous teams and proposes a unified taxonomy organizing the field. We systematically categorize task allocation problems by their characteristics: single-task versus multi-task robots, single-robot versus multi-robot tasks, instantaneous versus time-extended assignments, and static versus dynamic task environments. The review analyzes solution approaches spanning market-based methods where robots bid for tasks using utility functions, optimization-based techniques formulating allocation as integer programming or constraint satisfaction problems, game-theoretic approaches modeling strategic interactions among self-interested agents, and learning-based methods that improve allocation policies through experience. Particular emphasis is placed on handling heterogeneity through capability modeling, examining how different formulations represent sensing ranges, manipulation abilities, computational resources, and energy constraints. We evaluate algorithms across multiple performance dimensions: solution optimality measured against centralized benchmarks, computational scalability with team size and task count, communication efficiency in bandwidth-constrained environments, and robustness to dynamic task arrivals and agent failures. The paper examines coalition formation mechanisms where multiple robots collaborate on complex tasks requiring complementary capabilities, analyzing stability concepts and negotiation protocols. Application-specific implementations are reviewed for search and rescue missions requiring diverse sensors, environmental monitoring with heterogeneous sensing modalities, and construction tasks demanding varied manipulation capabilities. Advanced topics are analyzed including multi-objective task allocation balancing mission completion time with energy consumption, human-robot task allocation incorporating operator preferences, and learning-based allocation adapting to uncertain task requirements. The review identifies critical research gaps including formal guarantees for dynamic environments and integration with motion planning.