Juan C. Grieco

Cecilia Murrugarra, Osberth De Castro, Juan Carlos Grieco et al.

In this paper we propose an implicit force control scheme for a one-link flexible manipulator that interact with a compliant environment. The controller was based in the mathematical model of the manipulator, considering the dynamics of the beam flexible and the gravitational force. With this method, the controller parameters are obtained from the structural parameters of the beam (link) of the manipulator. This controller ensure the stability based in the Lyapunov Theory. The controller proposed has two closed loops: the inner loop is a tracking control with gravitational force and vibration frequencies compensation and the outer loop is a implicit force control. To evaluate the performance of the controller, we have considered to three different manipulators (the length, the diameter were modified) and three environments with compliance modified. The results obtained from simulations verify the asymptotic tracking and regulated in position and force respectively and the vibrations suppression of the beam in a finite time.

Bernardo Aceituno-Cabezas, Jose Cappelletto, Juan C. Grieco et al.

Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization approach for multilegged footstep planning which can be adapted to different platforms, regardless the number and geometry of legs. This approach leverages Mixed-Integer Convex Programming to account for the non-convex constraints that represent footstep rotation and obstacle avoidance. The planning problem is formulated as an optimization problem which considers robot geometry and reachability with linear constraints, and can be efficiently solved using optimization software. To demonstrate the functionality and adaptability of the planner, a set of tests are performed on a BH3R hexapod and a LittleDog quadruped on scenarios which can't be easily handled with discrete searches, such tests are solved efficiently in fractions of a second. This work represents, to the knowledge of the authors, the first successful implementation of a continuous optimization-based multilegged footstep planner.