GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.

AUTONOMOUS POWER DISTRIBUTION SYSTEMS

Using robotic systems for many missions that require power distribution can decrease the need for human intervention in such missions significantly. For accomplishing this capability a robotic system capable of autonomous navigation, power systems adaptation, and establishing physical connection needs to be developed. This thesis presents developed path planning and navigation algorithms for an autonomous ground power distribution system. In this work, a survey on existing path planning methods along with two developed algorithms by author is presented. One of these algorithms is a simple path planner suitable for implementation on lab-size platforms. A navigation hierarchy is developed for experimental validation of the path planner and proof of concept for autonomous ground power distribution system in lab environment. The second algorithm is a robust path planner developed for real-size implementation based on lessons learned from lab-size experiments. The simulation results illustrates that the algorithm is efficient and reliable in unknown environments. Future plans for developing intelligent power electronics and integrating them with robotic systems is presented. The ultimate goal is to create a power distribution system capable of regulating power flow at a desired voltage and frequency adaptable to load demands.