A novel approach to minimum-time control is presented. It is stated in terms of a hierarchical optimization problem, which is standard in the field of robotics. This is advantageous as already existing tools can be used to approach its solution. Our formulation is applied to the online generation of trajectories for industrial robots performing pick and place operations in the presence of obstacles. Model predictive control is used in order to achieve reactive system behavior and to obtain accurate local approximations of the collision avoidance constraints (which are nonconvex). Our approach has the capacity to suppress high frequency chattering in the control signal in the presence of noise: a common drawback of aggressive control strategies. Experiment using two SCARA robots that share the same working environment is used to evaluate the presented approach.

The synthesis of multi-fingered grasps on nontrivial objects requires a realistic representation of the contact between the fingers of a robotic hand and an object. In this work, we use a patch contact model to approximate the contact between a rigid object and a deformable anthropomorphic finger. This contact model is utilized in the computation of Independent Contact Regions (ICRs) that have been proposed as a way to compensate for shortcomings in the finger positioning accuracy of robotic grasping devices. We extend the ICR algorithm to account for the patch contact model and show the benefits of this solution.

The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.

The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact that a single symbolic state or action may be geometrically instantiated in infinitely many ways. When a symbolic action cannot be geometrically validated, we may need to backtrack in the space of geometric configurations, which greatly increases the complexity of the whole planning process. In this paper, we address this problem using intervals to represent geometric configurations, and constraint propagation techniques to shrink these intervals according to the geometric constraints of the problem. After propagation, either (i) the intervals are shrunk, thus reducing the search space in which geometric backtracking may occur, or (ii) the constraints are inconsistent, indicating the non-feasibility of the sequence of actions without further effort. We illustrate our approach on scenarios in which a two-arm robot manipulates a set of objects, and report experiments that show how the search space is reduced.

Coordinating multiple autonomous ground vehicles is paramount to many industrial applications. Vehicle trajectories must take into account temporal and spatial requirements, e.g., usage of floor space and deadlines on task execution. In this paper we present an approach to obtain sets of alternative execution patterns (called trajectory envelopes) which satisfy these requirements and are conflict-free. The approach consists of multiple constraint solvers which progressively refine trajectory envelopes according to mission requirements. The approach leverages the notion of least commitment to obtain easily revisable trajectories for execution.

The article presents an improved formulation of an existing model predictive control scheme used to generate online "stable" walking motions for a humanoid robot. We introduce: (i) a change of variable that simplifies the optimiza tion problem to be solved; (ii) a simply bounded formulation in the case when the positions of the feet are predetermined; (iii) a formulation allowing foot repositioning (when the system is perturbed) based on ℓ1- and ℓ∞-norm minimization; (iv) a formulation that accounts for (approximate) double support constraints when foot repositioning occurs.

This article presents a comparison between dense and sparse model predictive control (MPC) formulations, in the context of walking motion generation for humanoid robots. The former formulation leads to smaller, the latter one to larger but more structured optimization problem. We put an accent on the sparse formulation and point out a number of advantages that it presents. In particular, motion generation with variable center of mass (CoM) height, as well as variable discretization of the preview window, come at a negligible additional computational cost. We present a sparse formulation that comprises a diagonal Hessian matrix and has only simple bounds (while still retaining the possibility to generate motions for an omnidirectional walk). Finally, we present the results from a customized code used to solve the underlying quadratic program (QP).

The concept of independent contact regions on a target object's surface, in order to compensate for shortcomings in the positioning accuracy of robotic grasping devices, is well known. However, the numbers and distributions of contact points forming such regions is not unique and depends on the underlying computational method. In this work we present a computation scheme allowing to prioritize contact points for inclusion in the independent regions. This enables a user to affect their shape in order to meet the demands of the targeted application. The introduced method utilizes frictionless contact constraints and is able to efficiently approximate the space of disturbances resistible by all grasps comprising contacts within the independent regions.

Since the introduction of independent contact regions in order to compensate for shortcomings in the positioning accuracy of robotic hands, alternative methods for their generation have been proposed. Due to the fact that (in general) such regions are not unique, the computation methods used usually reflect the envisioned application and/or underlying assumptions made. This paper introduces a parallelizable algorithm for the efficient computation of independent contact regions, under the assumption that a user input in the form of initial guess for the grasping points is readily available. The proposed approach works on discretized 3D-objects with any number of contacts and can be used with any of the following models: frictionless point contact, point contact with friction and soft finger contact. An example of the computation of independent contact regions comprising a non-trivial task wrench space is given.

This article addresses the fast solution of a Quadratic Program underlying a Linear Model Predictive Control scheme that generates walking motions. We introduce an algorithm which is tailored to the particular requirements of this problem, and therefore able to solve it efficiently. Different aspects of the algorithm are examined, its computational complexity is presented, and a numerical comparison with an existing state of the art solver is made. The approach presented here, extends to other general problems in a straightforward way.

This article addresses the real-time implementation issues of a model predictive control based walking pattern generation for a humanoid robot. We approximate the multibody dynamic model with a linear discrete time system, and at each step solve a quadratic program in order to keep the output within a predefined set of constraints. The focus is on creating an efficient framework for forming and solving the underlying optimization problem. For that purpose we develop: a) a reliable guess for the active constraints at optimality; b) a fast way of generating an initial feasible point with respect to the set of constraints for each preview interval; c) a variable discretization sampling time. A simple implementation of a standard primal active set algorithm which exploits a "hot start" is used to demonstrate the advantages of the first point, while the latter one is verified using an existing dual solver.

Building on previous propositions to generate walking gaits online through the use of linear model predictive control, the goal of this paper is to show that it is possible to allow on top of that a continuous adaptation of the positions of the foot steps, allowing the generation of stable walking gaits even in the presence of strong perturbations, and that this additional adaptation requires only a minimal modification of the previous schemes, especially maintaining the same linear model predictive form. Simulation results are proposed then on the HRP-2 humanoid robot, showing a significant improvement over the previous schemes.

This article introduces a new technique for planning reactionless paths to a point in Cartesian space, for manipulators mounted on a free-floating satellite. It is based on decomposition of the manipulator joint space into sets referred to as primitives which have redundancy one with respect to the attitude motion of the base body. The time duration of the manipulator motion is divided into sub-intervals. During a given sub-interval only one primitive is used. The choice of feasible sequence of primitives and times for their actuation, that satisfies given path constraints is made using mixed-variables optimization solver based on a mesh adaptive direct search algorithm

This paper deals with problems related to the capture of a tumbling satellite by a space robot. The minimization of the base attitude deviation before and after the contact with the target is discussed from the viewpoint of angular momentum distribution. By using the bias momentum approach during the approaching phase, impedance control during the impact, and distributed momentum control during the post-impact phase, we propose a possible control sequence for the successful completion of a capturing operation

This paper deals with problems related to the capture of a tumbling satellite by a space robot. The minimization of the base attitude deviation after the contact with the target is discussed from the viewpoint of angular momentum distribution. A new capturing strategy utilizing bias momentum is introduced. By using the null space of the coupling inertia matrix we present a method for angular momentum redistribution while following a desired trajectory.

This paper presents a new strategy for capturing a free floating satellite initially having angular momentum. The main focus is on the base attitude before, during and after the catch. We use a joint space orthogonal decomposition procedure involving the so-called reaction null space during the pre-impact and post-impact phases. The idea of preloading bias momentum in a space robot system is discussed. Furthermore, we show that if proper post impact control is utilized the base deviation can be minimized.