Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

Assessment and training in home-baesd telerehabilitation ofr arm mobility impairment

The aging population and limited healthcare capacities call for a change in how rehabilitation care is provided. There is a need to provide more autonomous and scalable care that can be more easily transferred out of the clinic and into home environments. One important barrier to this objective is achieving reliable assessment of motor performance using low-cost technology. Toward this end, an assessment framework and methodology is proposed. The framework uses 4 sequential games to measure aspects of range of motion, range of force, control of motion, and control of force. Parameters derived from the range of motion task are used to define motion requirements in all subsequent assessment games, while parameters derived from the range of force task are used to define subsequent lifting force requirements. A 12-week usability study was conducted in which 9 patients completed the clinical testing phase and 6 therapists and 7 patients completed the questionnaire. Feedback from the questionnaire shows the system is easy to use and integrates well in the clinical setting. The most commonly requested modifications were the inclusion of more games and the incorporation of hand training. Some initial position and force data are shown for one subject and discussion on implications for mobility assessment using the developed device are provided.Peer Reviewe