Auction-based distributed task allocation algorithm for drone swarms

Drone swarm research has surged due to their superior task performance. This paper introduces Harmony DTA, an auction-based algorithm for task allocation in heterogeneous drone swarms. Prior research primarily focuses on minimizing overall costs associated with assignments. In contrast, Harmony DTA not only minimizes total costs through an enhanced cost calculation function, but also ensures equitable distribution of workload among drones. Additionally, the proposed two-stage auction process reduces the total message size utilized during communication. Simulations and field tests were conducted to assess the effectiveness of the proposed algorithm. In addition, the performance of the algorithm was evaluated by comparing it with the CBBA (Consensus-Based Bundle Algorithm) algorithm in cases where all messages are transmitted between agents and some messages are not transmitted due to communication problems. Based on the simulation findings, the suggested algorithm demonstrates an ability to address the assignment problem with a mean cost reduction of 20% and a mean reduction in message size of 50% compared to CBBA in scenarios without communication issues. However, in situations where communication obstacles lead to some messages being untransmitted between agents, Harmony DTA exhibits inferior performance to CBBA, attributed to conflicting assignments arising from the absence of a consensus phase.