Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018

A risk assessment infrastructure for powered wheelchair motion commands without full sensor coverage

Smart powered wheelchairs offer the possibility of enhanced mobility to a large and growing population---most notably older adults---and a key feature of such a chair is collision avoidance. Sensors are required to detect nearby obstacles; however, complete sensor coverage of the immediate neighbourhood is challenging for reasons including financial, computational, aesthetic, user identity and sensor reliability. It is also desirable to predict the future motion of the wheelchair based on potential input signals; however, direct modeling and control of commercial wheelchairs is not possible because of proprietary internals and interfaces. In this thesis we design a dynamic egocentric occupancy map which maintains information about local obstacles even when they are outside the field of view of the sensor system, and we construct a neural network model of the mapping between joystick inputs and wheelchair motion. Using this map and model infrastructure, we can evaluate a variety of risk assessment metrics for collaborative control of a smart wheelchair. One such metric is demonstrated on a wheelchair with a single RGB-D camera in a doorway traversal scenario where the near edge of the doorframe is no longer visible to the camera as the chair makes its turn.Science, Faculty ofComputer Science, Department ofGraduat

Towards a Legal end Ethical Framework for Personal Care Robots. Analysis of Person Carrier, Physical Assistant and Mobile Servant Robots.

Technology is rapidly developing, and regulators and robot creators inevitably have to come to terms with new and unexpected scenarios. A thorough analysis of this new and continuosuly evolving reality could be useful to better understand the current situation and pave the way to the future creation of a legal and ethical framework. This is clearly a wide and complex goal, considering the variety of new technologies available today and those under development. Therefore, this thesis focuses on the evaluation of the impacts of personal care robots. In particular, it analyzes how roboticists adjust their creations to the existing regulatory framework for legal compliance purposes. By carrying out an impact assessment analysis, existing regulatory gaps and lack of regulatory clarity can be highlighted. These gaps should of course be considered further on by lawmakers for a future legal framework for personal care robot. This assessment should be made first against regulations. If the creators of the robot do not encounter any limitations, they can then proceed with its development. On the contrary, if there are some limitations, robot creators will either (1) adjust the robot to comply with the existing regulatory framework; (2) start a negotiation with the regulators to change the law; or (3) carry out the original plan and risk to be non-compliant. The regulator can discuss existing (or lacking) regulations with robot developers and give a legal response accordingly. In an ideal world, robots are clear of impacts and therefore threats can be responded in terms of prevention and opportunities in form of facilitation. In reality, the impacts of robots are often uncertain and less clear, especially when they are inserted in care applications. Therefore, regulators will have to address uncertain risks, ambiguous impacts and yet unkown effects