Robot path planning is a necessary requirement for today’s autonomous
industry as robots are becoming a crucial part of the industry. Planning a path in a
dynamic environment that changes over time is a difficult challenge for mobile robots.
The robot needs to continuously avoid all the obstacles in its path and plan a suitable
trajectory from the given source point to the target point within a dynamically changing
environment. In this study, we will use Deep Q-Learning (Q-Learning using neural
networks) to avoid the obstacles in the environment, which are being dynamically
created by the user. The main aim of the robot is to plan a path without any collision
with any of the obstacles. The environment is simulated in the form of a grid that
initially contains information on the starting and the target location of the robot. Robots
need to plan an obstacle-free path for the given points. The user introduces obstacles
whenever he/she wishes during the simulation to make the environment dynamic. The
accuracy of the path is judged by the path planned by the robot. Various architectures
of neural networks are compared in the study that follows. Simulation results are
analyzed for the evaluation of an optimized path, and the robot is able to plan a path in
the dynamic environment.
Keywords: Artificial neural network, Deep learning, Neural networks, Reinforcement learning, Robot.
