This thesis develops human-robot interaction strategies that ensure the safety of the human participant through planning and control. The control and planning strategies are based on explicit measures of danger during interaction. The level of danger is estimated based on factors influencing the impact force during a human-robot collision, such as the effective robot inertia, the relative velocity and the distance between the robot and the human.

A danger criterion is developed for use during path planning based on static and quasi-static danger factors, such as the relative distance and the overall robot inertia. A planner algorithm is proposed that minimizes this criterion. A danger index, developed for the real-time safe control module, tracks dynamic danger parameters such as the relative velocity and the effective inertia at the impact point. The safe control module uses this index to identify and respond to real-time hazards not anticipated in the planning stage. Both the planning and the real-time safe control strategy have been tested in simulation and experiments.

Another key requirement for improving safety is the ability of the robot to perceive its environment, and specifically the human behavior and reaction to robot movements. This thesis also examines the feasibility of using human monitoring information (such as head rotation and physiological monitoring) to further improve the safety of the human robot interaction. A human monitoring module is developed using machine vision and physiological signal monitoring. The vision component tracks the location of the human in the robot's workspace, as well as the human head orientation. The physiological signal component monitors the human physiological signals such as heart rate, perspiration rate, and muscle contraction, and estimates the human emotional response based on these signals. If anxiety or stress is detected, the robot takes corrective action to respond to the human's distress.

The planning, control and human monitoring components are integrated in a robotic system and tested with human subjects. A systematic and safe interaction strategy utilizing the methods described above, and applicable to a range of human-robot interaction tasks, is presented.