Path Planning Approaches for Mobile Robot Navigation in Various Environments: A Review

Sanyal, Alok; Zafar, Mohd.; Mohanta, J. C.; Ahmed, Faiyaz

doi:10.1007/978-981-15-9956-9_55

Cited by 8 publications

(1 citation statement)

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“…Hence in recent years, various methods for planning the waypoints have been developed that involve the use of artificial intelligence techniques and heuristic approaches. However, with the increasing complexity of the working environment, different obstacles of varying sizes exist [5][6][7]. So, to address the above issues, the model-free reinforcement learning approach is used for path planning where the robot generates the optimal path through trial and error in a limited workspace.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Bio-Inspired Reinforcement Learning Algorithms for Manipulator Obstacle Avoidance

et al. 2022

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Path planning for robotic manipulators has proven to be a challenging issue in industrial applications. Despite providing precise waypoints, the traditional path planning algorithm requires a predefined map and is ineffective in complex, unknown environments. Reinforcement learning techniques can be used in cases where there is a no environmental map. For vision-based path planning and obstacle avoidance in assembly line operations, this study introduces various Reinforcement Learning (RL) algorithms based on discrete state-action space, such as Q-Learning, Deep Q Network (DQN), State-Action-Reward- State-Action (SARSA), and Double Deep Q Network (DDQN). By positioning the camera in an eye-to-hand position, this work used color-based segmentation to identify the locations of obstacles, start, and goal points. The homogeneous transformation technique was used to further convert the pixel values into robot coordinates. Furthermore, by adjusting the number of episodes, steps per episode, learning rate, and discount factor, a performance study of several RL algorithms was carried out. To further tune the training hyperparameters, genetic algorithms (GA) and particle swarm optimization (PSO) were employed. The length of the path travelled, the average reward, the average number of steps, and the time required to reach the objective point were all measured and compared for each of the test cases. Finally, the suggested methodology was evaluated using a live camera that recorded the robot workspace in real-time. The ideal path was then drawn using a TAL BRABO 5 DOF manipulator. It was concluded that waypoints obtained via Double DQN showed an improved performance and were able to avoid the obstacles and reach the goal point smoothly and efficiently.

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Section: Introductionmentioning

confidence: 99%