Sudipta Chatterjee, Angshuman Majumdar, Arighna Basak, Amitesh Das, Vertika Rai,
Volume 22, Issue 1 (3-2026)
Abstract
This paper offers a comprehensive examination of smart robotic wheelchairs and their role in enhancing the mobility and independence of individuals with disabilities. Conventional wheelchairs often restrict users, leading to limited movement and accessibility. The emergence of smart robotic wheelchairs presents a promising solution to these issues. The study provides an overview of wheelchair technology, highlights challenges faced by individuals with disabilities, and assesses the benefits and drawbacks of smart robotic wheelchairs through a review of previous research. It delves into the features and functionalities of these wheelchairs, such as navigation and obstacle avoidance, autonomous and semi-autonomous modes, and customizable control options. Additionally, it analyses user experience, performance evaluation, and the impact on mobility and independence. The paper concludes by outlining future research directions and recommendations to further empower individuals with disabilities and enhance their quality of life.
Zead Mohammed Yosif , Basil Shukr Mahmood, Saad Z. Alkhayat, Aws Hazim Saber ,
Volume 22, Issue 2 (3-2026)
Abstract
A mobile robot must be autonomous to avoid obstacles while traveling towards the target. Dynamic obstacle avoidance remains a significant challenge in mobile robotics. Although reactive navigation strategies have been applied to address this problem, relying on the single-stage module often results in limited efficiency and restricted overall performance. This paper proposes combining an adaptive neuro-fuzzy inference system (ANFIS) and a neural network (NN). The data for obstacle severity classification were used to train the Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses the ANFIS to avoid obstacles during the mobile robot's motion and to avoid collisions. Based on our empirical study, three essential features have been considered in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the ANFIS System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared to state-of-the-art research papers. The proposed work demonstrated better performance in path length reduction (approximately 6%) and time taken reduction to reach the target, which is reduced by about 60%.
