Consensus-Based Dynamic Task Allocation for Multi-Robot System Considering Payloads Consumption

TL;DR

The paper addresses multi-robot task allocation under payload consumption by proposing a consensus-based payload algorithm (CBPA). It introduces a two-phase framework—payload bundle construction with a payload assignment matrix and a consensus phase with update rules—to dynamically form conflict-free coalitions that meet task demands. The approach uses data structures like the winning robots matrix, time matrix, and payload matrices, and relies on diminishing marginal gains to guarantee finite-time convergence. Experimental results, including both simulation and physical demonstrations, show CBPA achieves higher total task gains and better payload utilization than CBBA, particularly in dynamic and tightly-coupled tasks.

Abstract

This paper presents a consensus-based payload algorithm (CBPA) to deal with the condition of robots' capability decrease for multi-robot task allocation. During the execution of complex tasks, robots' capabilities could decrease with the consumption of payloads, which causes a problem that the robot coalition would not meet the tasks' requirements in real time. The proposed CBPA is an enhanced version of the consensus-based bundle algorithm (CBBA) and comprises two primary core phases: the payload bundle construction and consensus phases. In the payload bundle construction phase, CBPA introduces a payload assignment matrix to track the payloads carried by the robots and the demands of multi-robot tasks in real time. Then, robots share their respective payload assignment matrix in the consensus phase. These two phases are iterated to dynamically adjust the number of robots performing multi-robot tasks and the number of tasks each robot performs and obtain conflict-free results to ensure that the robot coalition meets the demand and completes all tasks as quickly as possible. Physical experiment shows that CBPA is appropriate in complex and dynamic scenarios where robots need to collaborate and task requirements are tightly coupled to the robots' payloads. Numerical experiments show that CBPA has higher total task gains than CBBA.

Figures (5)

• Figure 1: Task Schedules
• Figure 2: The mobile multi-robot physical platform of MRTA. Green robots carry reconnaissance payload, white robots carry strike payload, and gray robot carries reconnaissance and strike payloads
• Figure 3: Task Execution Path of CBPA
• Figure 4: Task Execution Path of Auction-based Algorithm
• Figure 5: Task Total Gains