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Global Navigation in Dynamic Environments Using Case-Based Reasoning

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Abstract

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.

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Authors and Affiliations

  1. Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Sweden

    Maarja Kruusmaa

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  1. Maarja Kruusmaa
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Kruusmaa, M. Global Navigation in Dynamic Environments Using Case-Based Reasoning. Autonomous Robots 14, 71–91 (2003). https://doi.org/10.1023/A:1020979520454

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