This thesis employs the classical theory of Dynamic programming especially Reinforcement learning to solve two research problems: i) Mobile Robot navigation in an unknown environment; ii) Resource allocation in wildfire fighting.
The first part of this thesis deals with mobile robot navigation in an unknown environment. Robot navigation in an unknown environment is a very challenging problem. To navigate to a desired goal position in a given environment optimally, the robot needs to interact with the environment, remember the results of such interactions and understand the environment. Reinforcement learning is a classical way to solve this problem. This thesis is focused on improving the reinforcement learning approach applied for robot navigation using a variable grid size strategy. Also, we focus on a novel way to map and understand the obstacles of the environment. The variable grid size algorithm is an extension of reinforcement learning. We describe the methods of the uniform (fixed) and the variable resolution grid size based reinforcement learning. A test bed is used to evaluate the real world scenario and a variety of simulation environments are used to evaluate the improved algorithm.
The second part of the thesis is focused on resource allocation in fighting forest wildfires. Wildfires have known to wreak havoc and their ever increasing intensity have made us rethink about fire fighting strategies. Many decision support systems have been deployed in the recent past. However, most of them lack the ability to provide strategies to optimally contain the fire and their ability to adapt to dynamic conditions in the case of multiple wildfire sites in the same vicinity is limited. A dynamic programming based approach for optimal resource allocation to minimize the total burnt area has been employed in this thesis. For the sake of simplicity only homogeneous scenarios have been considered. We subject the proposed algorithm to Monte-Carlo simulations to obtain optimal strategies applicable when dealing with multiple fire sites. We conclude by discussing the results obtained in both the applications and the other possible applications of the above discussed methodology.