A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

Abstract

In this article, we present an obstacle avoidance controller implemented in a field programmable gate array for an electric wheelchair. It is based on a traditional approach with ultrasonic sensors and fuzzy logic. Various tests were conducted to characterize the prototype and to evaluate the controller performance. The results showed that the system is able to acquire data from sensors and make decisions 46.16 times per second. The sensors' coverage extends 3 m to the front, rear, left, and right sides of the wheelchair; moreover, the sensors detect 0.95-cm diameter objects at 40 cm. The power consumption was evaluated, and it was found that the hardware architecture reduces the battery life by only 0.87%. Furthermore, the controller helped to navigate in confined areas, avoiding obstacles with cautious movements and decreasing the likelihood of collision. The proposed methodology uses data from eight sonars distributed around the wheelchair to make navigation decisions, besides the hardware-based architecture guarantees real-time control and on-time response.

Agent-Based Intelligent Interface for Wheelchair Movement Control

“…However, improvements can still be made at a general level. For example, the incorporation of more advanced systems in the collision system, such as the one proposed in [ 9 ] can be used, so that the experience of user control with any interface can be greatly improved. On the other hand, the evaluation of the results obtained could be more exhaustive in terms of control methods by increasing the number of attempts and users but fundamentally incorporating in the case studies people with different disabilities to assess the adequacy of each one of the proposals to each of the disabilities.…”

“…However, great progress is being made in applying artificial intelligence techniques to the control of motorized wheelchairs, adapting control systems to different disabilities. We can find several examples in previous works such as [ 8 ] where different control alternatives are offered for people with quadriplegia by using ocular movements or voice commands; [ 9 ] where ultrasonic sensors data is used in combination with fuzzy logic to implement an obstacle avoidance system, improving the navigation in confined areas; [ 10 ] where Force Sensing Resistors are used in a head-movement based controlled interface; or [ 11 ] where a hand gesture based control is implemented by using inertial measurement unit and electromyography sensors. These are some of the many examples in the current literature where we can see how the control systems are increasingly improving through the combination of machine learning techniques and the data collected from different sensors.…”

Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot

“…According to the outcomes of the comparison, the fuzzy logic system pruned based on human experience outperformed the proposed learning-based fuzzy logic system. Similarly, much work conducted for the point-to-point navigation of mobile robots while avoiding obstacles can be seen in the literature [ 42 , 43 , 44 , 45 , 46 ].…”

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

“…They can remotely control a moving platform [11,12,41,42] with these technologies in complex daily tasks [14,43]. A wheelchair is a good substitute, for example Reference [44]. It's orientation commands are created by a wheelchair mounted eye movement tracking system [45][46][47].…”