# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Principles of Dynamic Obstacle Avoidance and Path Planning  
Several principles have been tested in developing a mobile robot capable of planning its paths and avoiding obstacles while performing the set task in a static, dynamic, or unknown environment. However, evidence from the review of 19 experiential studies on dynamic obstacle avoidance and path planning shows that all the studies rely on the principlesof reinforcement learning techniques in training mobile robots to move in an obstacle- and collision-free path. However, these principles of reinforcement learning techniques really round training the robot to learn and extract information about the obstacles in the navigating environment [47]. In addition, the reinforcement learning model has employed deep learning and continuous learning principles to allow mobile robots to plan their path in a dynamic obstacle environment [48]. This allows a mobile robot to decide on motion control autonomously and how to plan its path and avoid obstacles without being pre-informed about the environment. Likewise, it is a safe time for a self-monitored approach. To overcome path planning issues for mobile robots, [49] conducted an experiment that focused on creating a novel, robust, RL-based algorithm using Q-learning and the Artificial Potential Field (APF) approach. Q-learning has become more widely used recently to plan the path of mobile robots. It does, however, show slow convergence to the ideal answer. The APF method improves the conventional Q-learning approach in order to get around this restriction. In this experiment, suitable paths for mobile robots in both known and new locations were calculated using a QAPF learning algorithm. The length of the path, how smooth the path was, and the learning time were all utilized to determine how effective planning was. Path planning issues were successfully resolved in offline and online modes for both known and unknowable situations by the QAPF learning algorithm. The experiment's simulation findings demonstrated that the algorithm may accelerate learning and improve path planning in terms of path length and path smoothness. The algorithm successfully satisfied the three criteria for the path planning problem: smoothness, length, and safety. It was very dependable for mobile robot route planning since it generated better results across all test scenarios and had a stable training duration. In order to enhance the performance of mobile robots, [PERSON] et al. [50] presented an experimental navigation system based on DRL that integrated a recurrent neural network with DDQN. It practiced with physical robots, three-dimensional TurtleBot simulation environments, and two-dimensional simulation environments. Obstacles, prizes on the map, and discount variables were adjusted in accordance with the environment to increase the viability of the navigation system. The strategy operated well, as demonstrated by the experiments, making the outcomes of path planning more practical. Additionally, the experimental results demonstrated that the suggested algorithm increased the path length and path-finding efficiency. As a framework to describe complex relationships and collaboration, [PERSON] et al. [51] present deep reinforcement learning in avoiding pedestrian collisions, safe and smooth transitions. However, when the number of agents in the environment increase, they are built using fundamental presumptions about the behavior of other agents that are false. In order to efficiently avoid obstacles and arrive at a specific point, mobile robot agents were taught. To address the issue of not being able to discover a path in a particular circumstance, a path planner was combined with reinforcement learning-based obstacle avoidance. For the differential drive wheel robot, the training policy was implemented in the robot operating system (ROS) and evaluated in both virtual and physical situations.