Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Artificial Intelligence
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Distributed multi-robot coordination in area exploration
Robotics and Autonomous Systems
Distributed multi-robot coordination in area exploration
Robotics and Autonomous Systems
Efficient exploration of unknown indoor environments using a team of mobile robots
Annals of Mathematics and Artificial Intelligence
The giving tree: constructing trees for efficient offline and online multi-robot coverage
Annals of Mathematics and Artificial Intelligence
Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes
Autonomous Agents and Multi-Agent Systems
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
Robotic path planning using multi neuron heuristic search
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Distributed Constraint Reasoning Applied to Multi-robot Exploration
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
A Multi-agent Architecture for Multi-robot Surveillance
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning
Artificial Intelligence Review
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Hi-index | 0.00 |
Exploration of an unknown environment is one of the major applications of multi robot systems. A popular concept for the exploration problem is based on the notion of frontiers: the boundaries of the current map from where target points are allocated to multiple robots. Exploring an environment is then about entering into the unexplored area by moving towards the targets. To do so they must have an optimal path planning algorithm that chooses the shortest route with minimum energy consumption. Aiming at the problem, we discuss a modification to the well known A* algorithm that satisfies these requirements. Furthermore, we discuss improvements to the target allocation strategy, by pruning the frontier cells, because the computation burden for optimal allocation is increases with the number of frontier cells. The proposed approach has been tested with a set of environments with different levels of complexity depending on the density of the obstacles. All exploration paths generated were optimal in terms of smoothness and crossovers.