Autonomous UAV based search operations using Constrained Sampling Evolutionary Algorithms
This paper introduces and studies the application of Constrained Sampling Evolutionary Algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness the power of Evolutionary Algorithms (EA) when operating in complex, noisy, multimodal optimization problems and transfer the advantages of their approach to real time real world problems that can be transformed into search and optimization challenges. These types of problems are denoted as Constrained Sampling problems and are characterized by the fact that the physical limitations of reality do not allow for an instantaneous determination of the fitness of the points present in the population that must be evolved. A general approach to address these problems is presented and a particular implementation using Differential Evolution as an example of CS-EA is created and evaluated using teams of UAVs in search and rescue missions. The results are compared to those of a Swarm Intelligence based strategy in the same type of problem as this approach has been widely used within the UAV path planning field in different variants by many authors.
uav evolutionary algorithms swarm intelligence team coordination robot coordination unmanned aerial vehicles