Optimal scheduling for refueling multiple autonomous aerial vehicles

The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed

A Process Algebra Genetic Algorithm

A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.United States. Air Force Office of Scientific Research (Michigan/AFRL Collaborative Center in Control Science Grant FA 8650-07-2-3744)United States. Air Force Office of Scientific Research (Grant FA8655-09-1-3066

Scheduling and sequence reshuffle for autonomous aerial refueling of multiple UAVs

In this paper, we formulate the autonomous aerial refueling of multiple unmanned aerial vehicles (UAVs) as a scheduling problem. In order to find the optimal refueling sequence of UAVs, an efficient dynamic programming algorithm is introduced. When UAVs leave or join the queue, the optimal sequence needs to be recalculated. A systematic reshuffling method is developed such that the UAV sequence can be reconfigured by using the least amount of shuffle steps, where only one UAV changes its position in each step. By introducing a metric over UAV sequences, this reconfiguration effort is quantified and is treated as an additional cost which can be integrated into the dynamic programming algorithm

UAVs Task and Motion Planning in the Presence of Obstacles and Prioritized Targets

The intertwined task assignment and motion planning problem of assigning a team of fixed-winged unmanned aerial vehicles to a set of prioritized targets in an environment with obstacles is addressed. It is assumed that the targets’ locations and initial priorities are determined using a network of unattended ground sensors used to detect potential threats at restricted zones. The targets are characterized by a time-varying level of importance, and timing constraints must be fulfilled before a vehicle is allowed to visit a specific target. It is assumed that the vehicles are carrying body-fixed sensors and, thus, are required to approach a designated target while flying straight and level. The fixed-winged aerial vehicles are modeled as Dubins vehicles, i.e., having a constant speed and a minimum turning radius constraint. The investigated integrated problem of task assignment and motion planning is posed in the form of a decision tree, and two search algorithms are proposed: an exhaustive algorithm that improves over run time and provides the minimum cost solution, encoded in the tree, and a greedy algorithm that provides a quick feasible solution. To satisfy the target’s visitation timing constraint, a path elongation motion planning algorithm amidst obstacles is provided. Using simulations, the performance of the algorithms is compared, evaluated and exemplified