The project combines Conflict-Based Search (CBS) and Q-Learning algorithms for solving the path planning problem in a dynamic environment with multiple robots. The project aims to develop an efficient and adaptive solution to navigate multiple robots while avoiding conflicts and adapting to changing conditions.
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Conflict-Based Search (CBS): Implement the CBS algorithm to handle collision avoidance and path optimization among multiple robots. CBS efficiently resolves conflicts and generates collision-free paths.
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Q-Learning for Adaptation: Integrate Q-Learning, a reinforcement learning algorithm, to enable robots to learn and adapt their paths based on the changing environment and dynamic obstacles.
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Adaptive Decision-Making: Develop a decision-making mechanism that combines CBS and Q-Learning to allow robots to dynamically adjust their routes while considering potential conflicts and learned Q-values.