An Integrated Experience-Based Approach to Navigational Path Planning for Autonomous Mobile Robots

Ashok Goel, Michael Donnellan, Nancy Vazquez, Tod Callantine

Navigational path planning is a classical problem in autonomous mobile robotics. Most AI approaches to path planning use goal-directed heuristic search of problem spaces defined by spatial models of the navigation space. This paper explores an alternative approach that integrates a new case-based method with the traditional model-based method. Core issues in using case-based methods include the content, representation and indexing of past cases, organization of the case memory, retrieval of cases from memory relevant to the current problem, adaptation of retrieved cases to meet the specification of the current problem, and verification of the adapted solution to the problem. Our hypothesis is that spatial models of navigation spaces can provide answers to some of these issues in case-based path planning. The Router system examines this hypothesis in the context of path planning in geographical spaces. It uses a hierarchically-organized spatial model of the navigation space to index the cases and to organize the case memory. It also uses model-based reasoning to adapt past path-plans and to verify new ones. In addition, Router uses a flexible control architecture that allows for opportunistic selection and integration of the case-based and model-based planning methods. This paper provides an overview of the Router system.

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