Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Exploring artificial intelligence in the new millennium
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Multi-objective exploration and search for autonomous rescue robots: Research Articles
Journal of Field Robotics
Adaptive multi-robot wide-area exploration and mapping
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Role-Based Autonomous Multi-robot Exploration
COMPUTATIONWORLD '09 Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns
Multi-robot exploration and fire searching
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
UAV intelligent path planning for wilderness search and rescue
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An information-based exploration strategy for environment mapping with mobile robots
Robotics and Autonomous Systems
ICAS '10 Proceedings of the 2010 Sixth International Conference on Autonomic and Autonomous Systems
Trade-off between exploration and reporting victim locations in USAR
WOWMOM '10 Proceedings of the 2010 IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Multi-objective optimization for dynamic task allocation in a multi-robot system
Engineering Applications of Artificial Intelligence
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Some applications require autonomous robots to search an initially unknown environment for static targets, without any a priori information about environment structure and target locations. Targets can be human victims in search and rescue or materials in foraging. In these scenarios, the environment is incrementally discovered by the robots exploiting exploration strategies to move around in an autonomous and effective way. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations on the frontier between the known and the unknown portions of the environment according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented some MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment.