To extend the MGCBS algorithm to handle dynamic environments or uncertainty in the agent's movements, several modifications can be implemented. One approach is to incorporate real-time updates of the environment into the algorithm. This can involve updating the TIS Forest data structure based on new information about obstacles or changes in the environment. By dynamically adjusting the shortest paths and their lengths in response to changes, the algorithm can adapt to dynamic environments.
Another strategy is to introduce probabilistic models or uncertainty factors into the path planning process. This can be achieved by incorporating probabilistic movement models for the agents, considering uncertainties in their actions or the environment. By integrating probabilistic reasoning into the decision-making process, the algorithm can account for uncertainties and make more robust path planning decisions.
Furthermore, the algorithm can be enhanced to include collaborative decision-making mechanisms among agents in dynamic environments. By enabling agents to communicate, share information, and coordinate their movements in real-time, the algorithm can optimize path planning considering the dynamic nature of the environment and the interactions between agents.