Swarm-inspired solution strategy for the search problem of unmanned aerial vehicles

Learning from the emergent behaviour of social insects, this research studies the influences of environment to collective problem-solving of insect behaviour and distributed intelligent systems. Literature research has been conducted to understand the emergent paradigms of social insects, and to investigate current research and development of distributed intelligent systems. On the basis of the literature investigation, the environment is considered to have significant impact on the effectiveness and efficiency of collective problem-solving. A framework of collective problem-solving is developed in an interdisciplinary context to describe the influences of the environment to insect behaviour and problem-solving of distributed intelligent systems. The environment roles and responsibilities are transformed into and deployed as a problem-solving mechanism for distributed intelligent systems. A swarm-inspired search strategy is proposed as a behaviour-based cooperative search solution. It is applied to the cooperative search problem of Unmanned Aerial Vehicles (UAVs) with a series of experiments implemented for evaluation. The search environment represents the specification and requirements of the search problem; defines tasks to be achieved and maintained; and it is where targets are locally observable and accessible to UAVs. Therefore, the information provided through the search environment is used to define rules of behaviour for UAVs. The initial detection of target signal refers to modified configurations of the search environment, which mediates local communications among UAVs and is used as a means of coordination. The experimental results indicate that, the swarm-inspired search strategy is a valuable alternative solution to current approaches of cooperative search problem of UAVs. In the proposed search solution, the diagonal formation of two UAVs is able to produce superior performance than the triangular formation of three UAVs for the average detection time and the number of targets located within the maximum time length