Capability Driven Robotic Swarms in Reconnaissance-Based Operations

Abstract

Abstract : While 21st century surveillance techniques have started to utilize unmanned robotic platforms to gradually relieve many of the dangers and pressures placed on a human being, there is still a significant cost issue when addressing the overall effectiveness and efficiency. It is obvious that the unmanned robotic vehicle's ability to carry out surveillance missions is unparalleled by any human due to technological advances in electronic sensors. However, a single autonomous robot faces many limitations which decrease its cost efficiency and prevent it from being the best choice for various missions. On the other hand, a group of robots working together can remove many of the limitations imposed on one single unit. However, controlling a group of robots is inherently more complex than controlling just one. Initial controllers focused on the swarms mean position and variance in order to control its individual members and direct the entire swarm. This approach was successful in directing a swarm to a certain location; however, the swarm units were not always in the best position to use their individual capabilities. Since there is no way to control each individual unit's position without specific coding, the swarm may be in the correct location but some of the units may be rendered useless due to their position to the target point. This is one of the major concerns for using a mean/variance based controller in reconnaissance operations. This standard controller is better used for formation control of relative unit position based operations. This project developed new methodologies for optimizing the performance of a heterogeneous swarm in reconnaissance based operations.