00544nas a2200133 4500008003900000245010400039210006900143100001700212700001400229700001800243700001500261700001600276856011800292 2018 d00aApproximate hybrid model predictive control for multi-contact push recovery in complex environments0 aApproximate hybrid model predictive control for multicontact pus1 aMarcucci, T.1 aDeits, R.1 aGabiccini, M.1 aBicchi, A.1 aTedrake, R. uhttp://95.110.227.47/publications/approximate-hybrid-model-predictive-control-multi-contact-push-recovery-complex01719nas a2200157 4500008003900000245009500039210006900134520112900203100001701332700001701349700002101366700002001387700001701407700001501424856012201439 2017 d00aParametric Trajectory Libraries for Online Motion Planning with Application to Soft Robots0 aParametric Trajectory Libraries for Online Motion Planning with 3 a

In this paper we propose a method for online motion planning of constrained nonlinear systems. The method consists of three steps: the offline generation of a library of parametric trajectories via direct trajectory optimization, the online search in the library for the best candidate solution to the optimal control problem we aim to solve, and the online refinement of this trajectory. The last phase of this process takes advantage of a sensitivity-like analysis and guarantees to comply with the first-order approximation of the constraints even in case of active set changes. Efficiency of the trajectory generation process is discussed and a valid strategy to minimize online computations is proposed; together with this, an effective procedure for searching the candidate trajectory is also presented. As a case study, we examine optimal control of a planar soft manipulator performing a pick-and-place task: through simulations and experiments, we show how crucial online computation times are to achieve considerable energy savings in the presence of variability of the task to perform.

1 aMarcucci, T.1 aGarabini, M.1 aGasparri, G., M.1 aArtoni, Alessio1 aGabiccini, M1 aBicchi, A. uhttp://95.110.227.47/publications/parametric-trajectory-libraries-online-motion-planning-application-soft-robots.html01695nas a2200157 4500008004100000245007600041210006900117260004000186520118200226653001301408100001701421700001801438700001701456700001501473856004901488 2017 eng d00aTowards Minimum-Information Adaptive Controllers for Robot Manipulators0 aTowards MinimumInformation Adaptive Controllers for Robot Manipu aMay 24–26 2017, Seattle USAbIEEE3 a

The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between
which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters.
Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot.

10aRobotics1 aMarcucci, T.1 aDella Santina1 aGabiccini, M1 aBicchi, A. uhttps://ieeexplore.ieee.org/document/7963602