In this paper, we present a novel optimization algorithm for assigning weapons to targets based on desired kill probabilities. For the given weapons, targets, and desired kill probabilities, our optimization algorithm assigns weapons to targets that satisfy the desired kill probabilities and minimize the overkill. The minimization of overkill assures that any proper subset of the weapons assigned to a target results in a kill probability that is less than the desired kill probability on such a target. Computational results for up to 120 weapons and 120 targets indicate that the performance of this algorithm yields an average improvement in quality of solutions of 26.8% over the greedy algorithms, whereas execution times remained on the order of milliseconds.
In many applications, it is required that heterogeneous multi-robots are grouped to work on multi-targets simultaneously. Therefore, this paper proposes a control method for a single-master multi-slave (SMMS) teleoperator to cooperatively control a team of mobile robots for a multi-target mission. The major components of the proposed control method are the compensation for contact forces, modified potential field based leader-follower formation, and robot-task-target pairing method. The robot-task-target paring method is derived from the proven auction algorithm for a single target and is extended for multi-robot multi-target cases, which optimizes effect-based robot-task-target pairs based on heuristic and sensory data. The robot-task-target pairing method can produce a weighted attack guidance table (WAGT), which contains benefits of different robot-task-target pairs. With the robot-task-target pairing method, subteams are formed by paired robots. The subteams perform their own paired tasks on assigned targets in the modified potential field based leader-follower formation while avoiding sensed obstacles. Simulation studies illustrate system efficacy with the proposed control method.
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