Multi-robot Mission Planning with Energy Replenishment

Success of numerous long-term robotic explorations in air, on the ground, and under water is dependent on the ability of the robots to operate for an extended period of time. The continuous operation of robots hinges on smart energy consumption and replenishment of the robots. This dissertation addresses the multi-robot system continuous operation problem by developing two mission planning architectures regarding two types of energy replenishment, which can be adapted to different mission scenarios based on mission requirements and available resources. The first type of energy replenishment utilizes static charging stations to provide a recharging opportunity to primary working robots, who can periodically revisit static charging stations to be recharged through the mission. The static energy replenishment mission planning method simultaneously generates energy efficient trajectories for multiple robots and schedules energy cycling using a Genetic Algorithm (GA). The mission planning method accounts for environmental obstacles, disturbances, and can adapt to priority search distribution. The second energy replenishment approach extends working robots operation by deploying a team of mobile charging stations to rendezvous and charge working robots. A graph transformation method is developed for mobile charging stations to solve persistent operation problem of working robots with pre-defined trajectories. Consideration of dynamic currents effect and obstacles are integrated into the method. To optimize trajectories of both working robots and mobile charging stations, a GA based mission planning method is designed with the capability of re-planning to account for mission uncertainty. Simulation validations are performed through solving long-term mission planning problems. A variety of real-world mission scenarios employing teams of underwater, aerial, and ground robots are simulated with multiple mission objectives under various environmental and robot constraints. The effectiveness of both developed mission planning methods in area coverage, handling energy limitations, and mission constraints are discussed and analyzed by numerical studies