This paper focuses on time-optimal path tracking, which involves planning of robot motions along prescribed geometric paths. Starting from a discretized convex reformulation of time-optimal path tracking problems, a log-barrier based batch solution method is presented which allows to rapidly obtain an approximate solution with smooth actuator torques. Based on this batch method, a recursive variant is derived for on-line path tracking. By means of an experimental test case in which the path data is generated on-line by human demonstration, the results and trade-offs in calculation time, delay and path duration are compared for the batch and recursive variant of the log-barrier method as well as for an exact solution method.

This paper investigates and demonstrates the feasibility of identifying contact dynamics parameters forstiffroboticpayloadsusing a robotic system. The contact dynamics model for stiff payloads is motivated, and theoretical parameter values and bounds are provided. Then, the effect of nonidealities such as surface roughness and plastic deformation on the theoretical values is demonstrated. A row-wise-scaled total least-squares parameter estimation algorithm is proposed and applied to experimental data measured using the special purpose dexterous manipulator task verification facility manipulator at the Canadian Space Agency. The experimental results are compared to a separate set of experiments with a material testing machine as well as finite-element modeling results. Finally, the experimental findings are generalized by providing guidelines for the maximum identifiable payload stiffness as a function of the position resolution, the maximum exertable force, and the structural stiffness of the robotic system.

Robots are increasingly used to perform complex tasks, which often involve interaction and contact with unstructured environments. By identifying geometric uncertainties and the dynamic behavior of the environment on-line, the autonomy of intelligent robot systems can be considerably improved. This paper considers the 2D case of an industrial robot equipped with a probe to explore an unknown environment. The goal is to estimate from the measured end-effector position, velocity and forces not only the environmental contact dynamics parameters, but also geometric parameters such as the environment position and orientation, and the position of the probe end-point with respect to the robot end-effector. To this end, a Kalman filter based algorithm is proposed, which enforces physical constraints and which is executed in an event-triggered way to improve convergence and robustness. Experimental results illustrate the viability of the proposed algorithm.