Industrial robots are widely used as flexible, re-programmable positioning devices in manufacturing plants, and allow to save costs, increase productivity and raise quality. However, they still present capital intensive investments. Therefore, increasing their autonomy and efficiency are topics of ongoing research. With the increasing availability of cheap computation power and low-cost sensors, considerable gains in efficiency and autonomy can be realized through the use of additional sensors and sophisticated data processing and control algorithms. My PhD research focuses on two major research topics.

The first topic of my PhD research deals with contact modeling and identification and aims to advance the autonomy and intelligence of robots that interact with their environment. To this end, robots are equipped with force sensors, in addition to their built-in position sensors, to gather information about their environment. To interpret measurements from these sensors, this research formulates and validates contact models, which describe the behavior of robots in contact with their environment. To characterize the interaction of robots with unstructured or uncertain environments, this research also develops contact parameter identification algorithms to identify the parameters of contact models. Based on the identified contact parameters, the accuracy and robustness of the low-level control, as well as the task execution can be improved on-line, while knowledge of the contact parameters can be used off-line to design and validate constrained robotic tasks by means of computer simulations.

The second topic of my PhD research deals with optimal robot motion planning and aims to advance the efficiency of robot motions. By optimizing robot motions, while taking into account their dynamic behavior, robots can fully exploit their capabilities and make full use of their actuators. This research develops computer-aided algorithms for planning of robot motions along prescribed geometric paths, called path tracking problems, with time as the main optimality criterion. These path tracking algorithms allow to relieve operators from the burden of manual optimization and tuning, and reduce the downtime of robots, while increasing their productivity.