In this paper, we analyze systems with low throughput human-machine interfaces (such as a brain-computer interface, single switch interface) from the controls perspective. We develop some principles for performance improvement in such systems based on the parallelization of inference and robot motion. The proposed principles are used to design a novel shared position control to navigate a circular massless holonomic robot in a known environment. The system is implemented in simulation and integrated with a real robotic wheelchair. Robot experiments demonstrated the viability of the proposed navigation method in various modes of operation.

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. “Any-angle” attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A* on an 8-connected graph, the path can turn with 45°-increments only. The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front. In its purest form, CWave requires for computation only integer arithmetics and multiplication by two, but can accumulate the distance error at turning points. A modified version of CWave with minimal usage of floating-point calculations is also developed. It allows to eliminate any accumulative errors which is proven mathematically and experimentally on several maps. The performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta* and Field A* adapted for single-source planning. The limitations of the current implementations of the algorithm as well as potential improvements are discussed.

In this work, we are presenting a navigation framework for electric wheelchairs which integrates various alternative input interfaces (voice control with Google Glass, voice control with CMU Sphinx, and facial expression control with Emotiv EPOC) with assistive navigation. Assistive navigation compensates for the limitations of the alternative interfaces by implementing obstacle avoidance. This enables people with limited control over their limbs to freely navigate within indoor environments. A set of use cases designed to ensure safe and reliable navigation is also presented.