Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints

Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distributed approach to coordinate a team of heterogeneous UAVs cooperating efficiently in patrolling missions around irregular areas, with low communication ranges and memory storage requirements. Hence it can be used with small‐scale UAVs with limited and different capabilities. The presented system uses a modular architecture and solves the problem by dividing the area between all the robots according to their capabilities. Each aerial robot performs a decomposition based algorithm to create covering paths and a ’one‐to‐one’ coordination strategy to decide the path segment to patrol. The system is decentralized and fault‐tolerant. It ensures a finite time to share information between all the robots and guarantees convergence to the desired steady state, based on the maximal minimum frequency criteria. A set of simulations with a team of quad‐rotors is used to validate the approach

Performance Guarantee of a Sub-Optimal Policy for a Discrete Markov Decision Process and Its Application to a Robotic Surveillance Problem

This dissertation deals with the development and analysis of sub-optimal decision algorithms for a collection of robots that assist a remotely located operator in perimeter surveillance. The operator is tasked with the classification of incursions across the perimeter. Whenever there is an incursion into the perimeter, a nearby Unattended Ground Sensor (UGS) signals an alert. A robot services the alert by visiting the alert location, collecting evidence in the form of video imagery, and transmitting it to the operator. The accuracy of operator's classification depends on the volume and freshness of information gathered and provided by the robots at locations where incursions occur. There are two competing needs for a robot: it needs to spend adequate time at an alert location to collect evidence for aiding the operator in accurate classification but it also needs to service other alerts as soon as possible, so that the evidence collected is relevant. The control problem is to determine the optimal amount of time a robot must spend servicing an alert. The incursions are stochastic and their statistics are assumed to be known. The control problem may be posed as a Markov Decision Problem (MDP). Dynamic Programming(DP) provides the optimal policy to the MDP. However, because of the "curse of dimensionality" of DP, finding the optimal policy is not practical in many applications. For a perimeter surveillance problem with two robots and five UGS locations, the number of states is of the order of billions. Approximate Dynamic Programming (ADP) via Linear Programming (LP) provides a way to approximate the value function and derive sub-optimal strategies. Using state partitioning and ADP, this dissertation provides different LP formulations for upper and lower bounds to the value function of the MDP, and shows the relationship between LPs and MDP. The novel features of this dissertation are (1) the derivation of a tractable lower bound via LP and state partitioning, (2) the construction of a sub-optimal policy whose performance exceeds the lower bound, and (3) the derivation of an upper bound using a non-linear programming formuation. The upper and lower bounds provides approximation ratio to the value function. Finally, illustrative perimeter surveillance examples corroborate the results derived in this dissertation

Air Force Institute of Technology Research Report 2011

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics