Very Large-scale Multi-Robot Task Allocation in Challenging Environments via Robot Redistribution (2506.07293v1)

Abstract: We consider the Multi-Robot Task Allocation (MRTA) problem that aims to optimize an assignment of multiple robots to multiple tasks in challenging environments which are with densely populated obstacles and narrow passages. In such environments, conventional methods optimizing the sum-of-cost are often ineffective because the conflicts between robots incur additional costs (e.g., collision avoidance, waiting). Also, an allocation that does not incorporate the actual robot paths could cause deadlocks, which significantly degrade the collective performance of the robots. We propose a scalable MRTA method that considers the paths of the robots to avoid collisions and deadlocks which result in a fast completion of all tasks (i.e., minimizing the \textit{makespan}). To incorporate robot paths into task allocation, the proposed method constructs a roadmap using a Generalized Voronoi Diagram. The method partitions the roadmap into several components to know how to redistribute robots to achieve all tasks with less conflicts between the robots. In the redistribution process, robots are transferred to their final destinations according to a push-pop mechanism with the first-in first-out principle. From the extensive experiments, we show that our method can handle instances with hundreds of robots in dense clutter while competitors are unable to compute a solution within a time limit.

• The paper introduces MRTA-RM, integrating robot redistribution with path planning to preempt deadlocks and minimize makespan.
• It employs a Generalized Voronoi Diagram and FIFO-based push-pop mechanism to effectively manage hundreds of robots in cluttered spaces.
• Empirical results show faster computation times and lower task costs compared to traditional allocation strategies in obstacle-rich settings.

Review of Multi-Robot Task Allocation via Robot Redistribution

The paper presents a novel methodology addressing the Multi-Robot Task Allocation (MRTA) problem within environments congested with obstacles, where traditional solutions often fail to efficiently navigate robot conflicts and associated costs such as deadlocks and waiting times. The proposed solution emphasizes minimizing makespan by incorporating path planning into the task allocation process, which is a significant deviation from the conventional allocation methodologies that typically attempt to minimize the sum of task costs without considering motion paths.

Significance and Methodology

Rather than evaluating costs merely as a summation of task assignments, the paper takes a critical step forward by incorporating robot paths into the allocation process to preemptively manage potential conflicts. This is achieved through a method termed Multi-Robot Task Allocation via Robot Redistribution Mechanism (MRTA-RM). MRTA-RM constructs a roadmap using a Generalized Voronoi Diagram (GVD) and partitions this roadmap into components, which effectively allow robots to be redistributed to resource-demanding areas using a push-pop mechanism based on FIFO principles. This method specifically addresses the bottlenecks and potential deadlocks that can occur when robots encounter narrow passages or densely cluttered areas.

Through extensive experimentation, the authors demonstrate the efficacy of the MRTA-RM method by successfully handling hundreds of robots in environments where other methods fail to compute solutions within a given time limit. Notably, MRTA-RM offers congestion-aware task allocation, ensuring scalability and leveraging the roadmap’s physical realities to estimate task costs more accurately than conventional methods.

Empirical Results

The empirical results are compelling, showing that MRTA-RM can efficiently compute task allocations in scenarios with hundreds of robots and tasks. Compared to CBS-TA and ECBS-TA, MRTA-RM consistently executed faster computation times while maintaining high success rates in environments with random and separated robot/task distributions. For tasks executed, MRTA-RM showed a reduced makespan and sum of costs compared to competitors, indicating the potential to optimize existing logistics and automation workflows significantly.

Theoretical and Practical Implications

Theoretically, this research demonstrates the importance of integrating path planning and task allocation to reduce the gap between theoretical assignments and real-world execution costs. The paper highlights the necessity to incorporate motion constraints and environmental dynamics into the task allocation process, thus providing an avenue for future MRTA solutions that extend beyond static allocation procedures.

Practically, the MRTA-RM framework can be directly applied to logistics automation, where multi-robot systems must perform tasks in environments like warehouses, manufacturing floors, or service spaces. The integration of GVD-based roadmap strategies and FIFO-based robot redistribution highlights a shift towards more dynamic and adaptive task allocation approaches that simultaneously optimize robot movement and task completion.

Future Research Directions

This research opens up numerous avenues for future investigation, notably the real-time adaptive methods that can further enhance the synchronicity between robot movements and task assignments in even more dynamic environments. Moreover, extending the MRTA-RM framework to accommodate robot kinodynamic constraints and integrating real-world data-driven predictions could yield even more robust and scalable MRTA solutions.

In conclusion, the paper presents a significant stride in the field of multi-robot systems, outlining a framework that balances computational efficiency with practical applicability in dense, obstacle-rich environments. The successful demonstration of MRTA-RM across test scenarios provides a strong foundation for further exploration and optimization of multi-robot task allocation strategies.