USP Electronic Research Repository

Obstacle avoidance of a point - mass robot using feedforward neural network

Chaudhary, Kaylash C. and Lal, Goel and Prasad, Avinesh and Chand, Vishal and Sharma, Sushita and Lal, Avinesh (2021) Obstacle avoidance of a point - mass robot using feedforward neural network. [Conference Proceedings]

[img] PDF - Published Version
Restricted to Repository staff only

Download (383kB)


Machine learning is presently acknowledged as a significant ingredient of research in many fields, including robotics. The use of robots to perform assorted tasks is evident in difficult, uncompromising, and hazardous spaces and sectors such as manufacturing, transportation, healthcare, landmines, mining, patrolling, disaster relief etc. For a robot to carry out its assigned task, it normally has to navigate safely without collisions to different locations, which also means understanding its working environment, collectively known as the robot navigation problem. This paper considers finding a solution using neural networks to the robot navigation problem, particularly the path planning problem that includes fixed obstacles. The objective of the path planning problem is to find a route to the final destination that is optimal and also collision-free. Different training algorithms and network structures are used to construct models that can predict a turning angle for the point-mass robot which will be used to avoid obstacles in the robot's path to the destination. This paper will present a comparative analysis of the performance of different feedforward neural network models. The results suggest that the feedforward neural network model with 10 neurons and Bayesian regularization performed the best. The model has been used to avoid obstacles in two different environments. The trajectories show that the robot has safely avoided obstacles in its path and reached the destination.

Item Type: Conference Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User: Ms Shalni Sanjana
Date Deposited: 06 Apr 2022 02:23
Last Modified: 06 Apr 2022 02:23

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...