Abstract
This paper studies the target searching problem using swarms of unmanned aerial vehicles (UAVs) in unknown environments which information is unknown to the UAVs, other than features they detect through their sensors. Effective decision and control methods are required for UAVs that consider their limitations and characteristics when confronted with target searching problems. A cooperative target searching method is proposed for swarm UAVs based on an improved bean optimization algorithm (BOA) called Robot Bean Optimization Algorithm (RBOA). Compared with conventional BOAs used for optimal computation, RBOA has two main modifications for the cooperative control of swarm robots: 1) it accounts for the free motion space of individual UAVs using a Thiessen polygon; and 2) it adds a free space search mechanism to improve the efficiency of target searching. Based on the above improvements, and by integrating a multi-phase search mechanism and scheduling control strategy, a swarm UAV collaborative search simulation platform is built for experimental purposes. The results obtained from search simulations show that the RBOA can outperform adaptive robotic particle swarm optimization (A-RPSO) in target searches in complex and unknown environments, especially with fewer evolutionary generations and smaller numbers of robots. The RBOA, which is inspired by plant population evolutionary patterns, has fast and effective search capabilities, distributed collaborative interaction, and emergent swarm intelligence. It provides new ideas and support for research into the control of swarm UAVs and swarm robots.
Highlights
Unmanned aerial vehicles (UAVs) originated in the military and are a type of aircraft that is unmanned and remotely or autonomously controlled
bean optimization algorithm (BOA)-BASED COOPERATION METHOD FOR TARGET SEARCHING BY SWARM UAVS With the aim of achieving swarm UAV target searching, we designed and constructed the robot bean optimization algorithm (RBOA) based on the BOA by adding free motion space, internal search mechanism, multi-phase search mechanism, and scheduling control strategy
The RBOA has three advantages, which result in a higher level of performance compared with other approaches
Summary
Unmanned aerial vehicles (UAVs) originated in the military and are a type of aircraft that is unmanned and remotely or autonomously controlled. Cooperative control is very important in this application [14], and many scholars have carried out research into cooperative searching with UAVs. For example, reference [15] used UAVs to search for lost targets in unknown environments. When solving complex optimization problems, the BOA has a relatively fast optimization speed and outstanding adaptability to dynamic environments It demonstrates amazing swarm adaptive survivability of plants and is suitable for researching swarm distributions, collaboration, and the emergence of swarm intelligence. A robot bean optimization algorithm (RBOA) is proposed and applied to simulate a target search task in unknown complex environments by swarm UAVs. The main characteristics of the RBOA include: (1) a free motion space, (2) an internal search mechanism, (3) a multi-phase search mechanism, and (4) a scheduling control strategy.
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