The aim of the study was to develop a control system for a robotic wheelchair with an extensive user interface that is able to support users with different impairments. Different concepts for a robotic wheelchair design for disabled people are discussed. The selected approach is based on a cognitive multimodal user interface to maximize autonomy of the wheelchair user and to allow him or her to communicate intentions by high-level instructions. Manual, voice, eye tracking, and BCI (brain-computer interface) signals can be used for strategic control whereas an intelligent autonomous system can perform low-level control. A semiotic model of the world processes sensory data and plans actions as a sequence of high-level tasks or behaviors for the control system. A software and hardware architecture for the robotic wheelchair and its multimodal user interface was proposed. This architecture supports several feedback types for the user including voice messages, screen output, as well as various light indications and tactile signals. The paper describes novel solutions that have been tested on real robotic devices. The prototype of the wheelchair uses a wide range of sensors such as a camera, range finders, and encoders to allow operator to move safely and provide object and scene recognition capabilities for the wheelchair. Dangerous behavior of the robot is interrupted by low-level reflexes. Additional high-level safety procedures can be implemented for the planning subsystem. The developed architecture allows utilizing user interfaces with a considerable time lag that are usually not suitable for traditional automated wheelchair control. This is achieved by increasing time allocated for processing of the interface modules, which is known to increase the accuracy of such interfaces as voice, eye tracking, and BCI. The increased latency of commands is mitigated by the increased automation of the wheelchair since high-level tasks can be given less frequently than manual control. The prospective solutions use a number of technologies based on registration of parameters of human physiological systems, including brain neural networks, in relation to the task of indirect control and interaction with mobile technical systems.

No abstract

No abstract

No abstract

No abstract