Method for Mobile Robot Navigation Using Reinforcement Learning in an Unfamiliar and Complicated Environment

In recent years, mobile robots (MRs) have seen explosive growth in use across several industries, including production, healthcare, security, as well as the automation of warehouses. Ensuring the safety and effectiveness of these MRs' functioning requires the design of efficient control mechanisms that can adapt to changing situations. Give a new way to manage MRs using reinforcement learning (RL) in this paper. The process begins with the development of a mathematical model, followed by the training of a neural network (NN) to create a control plan for the robot using reinforcement learning (RL). The policy is learned by MR via trial and error as it explores its environment and is rewarded based on its behavior. The robot will be more motivated to avoid obstacles and get toward its aim if it is offered incentives. Hiring a deep Q-learning (QL) agent has opened up new possibilities for robot navigation, including the ability to learn successfully traverse unfamiliar terrains while avoiding hazards. After an RL model finds the target location using MR autonomously in an unknown territory, a Deep Q-Network (DQN) is used to get there. Evaluation of our methods via the usage of an Epsilon-Greedy algorithm simulation. In comparison to existing MR monitoring systems, the technique is more efficient and safer, according to the findings.