Global Level Path Planning for Mobile Robots in Dynamic Environments

Authors:
Maarja Kruusmaa
Affiliations:
Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823 S-30118 Halmstad, Sweden
Venue:
Journal of Intelligent and Robotic Systems
Year:
2003

Citing 13
Cited 0

Using genetic algorithms to learn reactive control parameters for autonomous robotic navigation

Adaptive Behavior
Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
Learning metric-topological maps for indoor mobile robot navigation

Artificial Intelligence
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Machine Learning - Special issue on learning in autonomous robots
Robot Motion Planning

Robot Motion Planning
Globally Consistent Range Scan Alignment for Environment Mapping

Autonomous Robots
Multistrategy Adaptive Path Planning

IEEE Expert: Intelligent Systems and Their Applications
Route Planning by Analogy

ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving

IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
CBR: Strengths and Weaknesses

IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Categorizing Case-Base Maintenance: Dimensions and Directions

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Stratified case-based reasoning: reusing hierarchical problem solving episodes

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The focussed D* algorithm for real-time replanning

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a self-adapting approach to global level path planning in dynamic environments. The aim of this work is to minimize risk and delays in possible applications of mobile robots (e.g., in industrial processes). We introduce a hybrid system that uses case-based reasoning as well as grid-based maps for decision-making. Maps are used to suggest several alternative paths between specific start and goal point. The casebase stores these solutions and remembers their characteristics. Environment representation and casebase design are discussed. To solve the problem of exploration vs. exploitation, a decision-making strategy is proposed that is based on the irreversibility of decisions. Forgetting strategies are discussed and evaluated in the context of case-based maintenance. The adaptability of the system is evaluated in a domain based on real sensor data with simulated occupancy probabilities. Forgetting strategies and decision-making strategies are evaluated in simulated environments. Experiments show that a robot is able to adapt in dynamic environments and can learn to use paths that are less risky to follow.