Abstract
The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.
Highlights
Unmanned aerial vehicles (UAVs), originally developed for the military, have become preeminent platforms in civil applications, such as agriculture [1,2,3], transportation [4,5], mineral exploration [6,7,8], and search and rescue (SAR) operations [9,10,11]
This study introduces a fish-inspired algorithm for the task allocation for multi-UAVs (FIAM) during an SAR mission, which exploits ideas from fish schooling and foraging behavior where fish aggregate in schools to search for food, and each school follows a single leader
ant-colony optimization (ACO) and its variations poorly perform in dynamic contexts, such as SAR missions, which is a common problem in all the metaheuristics [38], as recalculations are needed once a new task arrives, i.e., a new survivor discovered, which results in large computation overheads due to the exponential run time [39]
Summary
Unmanned aerial vehicles (UAVs), originally developed for the military, have become preeminent platforms in civil applications, such as agriculture [1,2,3], transportation [4,5], mineral exploration [6,7,8], and search and rescue (SAR) operations [9,10,11]. This study introduces a fish-inspired algorithm for the task allocation for multi-UAVs (FIAM) during an SAR mission, which exploits ideas from fish schooling and foraging behavior where fish aggregate in schools to search for food, and each school follows a single leader. Fish behavior has previously been considered for various task assignment problems (e.g., [22,23,24]), it has not been considered for resolving the MUTA problem All, these benefits come at no cost of running time where FIAM was investigated in-depth by simulating SAR missions within a strictly controlled evaluation framework. A new algorithm inspired by fish schooling and foraging behavior, to address the special challenges of the task allocation problem, which is among the NP-hard problems.
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