This paper focuses on time-optimal path-constrained trajectory planning, a subproblem in time-optimal motion planning of robot systems. Through a nonlinear change of variables, the time-optimal trajectory planning is transformed here into a convex optimal control problem with a single state. Various convexity-preserving extensions are introduced, resulting in a versatile approach for optimal trajectory planning. A direct transcription method is presented that reduces finding the globally optimal trajectory to solving a second-order cone program using robust numerical algorithms that are freely available. Validation against known examples and application to a more complex example illustrate the versatility and practicality of the new method.Index Terms-time-optimal trajectory planning, time-optimal control, convex optimal control, path tracking, trajectory planning, convex optimization, second-order cone program, direct transcription.

This paper focuses on time-optimal and time-energy optimal path tracking, which are subproblems in optimal motion planning of robot systems. Through a nonlinear change of variables, the time-energy optimal path tracking problem is transformed here into a convex optimal control problem with a single state variable. A direct transcription method is presented that reduces finding the globally optimal trajectory to solving a second-order cone program using robust numerical algorithms that are freely available. Application to a six-DOF KUKA 361 industrial robot carrying out a writing task illustrates the practicality of the new method.

This paper investigates and demonstrates the feasibility of identifying contact dynamics parameters for stiff robotic payloads using 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.

No abstract