Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Introduction to Algorithms
Moving-Target Search: A Real-Time Search for Changing Goals
IEEE Transactions on Pattern Analysis and Machine Intelligence
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Mine Detection and Route Planning in Military Warfare using Multi Agent System
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 02
Obstacle Avoidance Path Planning for Mobile Robot Based on Ant-Q Reinforcement Learning Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Real-time heuristic search: first results
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
A new motion planning approach based on artificial potential field in unknown environment
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Engineering Applications of Artificial Intelligence
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This research presents an optimization technique for route planning and exploration in unknown environments. It employs the hybrid architecture that implements detection, avoidance and planning using autonomous agents with coordination capabilities. When these agents work for a common objective, they require a robust information interchange module for coordination. They cannot achieve the goal when working independently. The coordination module enhances their performance and efficiency. The multi agent systems can be employed for searching items in unknown environments. The searching of unexploded ordinance such as the land mines is an important application where multi agent systems can be best employed. The hybrid architecture incorporates learning real time A* algorithm for route planning and compares it with A* searching algorithm. Learning real time A* shows better results for multi agent environment and proved to be efficient and robust algorithm. A simulated ant agent system is presented for route planning and optimization and proved to be efficient and robust for large and complex environments.