A Machine Learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments. (October 2022)

• Record Type: Journal Article
• Authors: Das, Subhranil; Mishra, Sudhansu Kumar
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Autonomous Mobile Robot -- Machine Learning -- Stochastic Gradient Descent -- Collision Avoidance -- Linear Regression -- Path Panning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108376
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A dataset is created using velocities of wheels and distance of obstacle from AMR. The left and right turn of AMR is framed as a two class problem. The parameters of the proposed algorithm are updated by SGD optimization technique. The proposed algorithm provides better results with other existing algorithms. Abstract: In this paper, a novel approach based on Machine Learning (ML) concept, i.e., the Adaptive Stochastic Gradient Descent Linear Regression (ASGDLR) algorithm, is developed to segregate an AMR's directional movement as right and left turn. Moreover, the developed algorithm is employed for path planning and navigational purposes. Here, real-time velocities of the right and left wheel and distance from obstacle data have been acquired by two Infrared (IR) sensors and one Ultrasonic (US) sensor positioned on the AMR. The weights of the proposed ASGDLR model are iteratively updated by applying Stochastic Gradient Descent (SGD) optimization technique by considering the difference between the actual velocity and model output velocity as an error signal. For the performance analysis of the proposed algorithm, three different performance indices, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), have been evaluated. The efficacy of the proposed algorithm is compared with six different AI-based regression algorithms for validation purposes. Moreover, the performance of the proposed algorithm is investigated for both obstacle avoidance and path planning in dense and cluttered environments. The simulation findings indicate that our proposed algorithm can accomplish tasks more efficiently than others while eliminating their shortcomings. Graphical abstract: Image, graphical abstract