Envisioning the employment of rope-assisted humanoid robots to reduce human intervention for operations in the heights, this preliminary work addresses the modeling and motion planning problems for a rope-assisted bipedal robot. The mathematical features of this system outnumber the ones of typical humanoid robots, including: under-actuation of the floating-base joints, the rope pulling effect and the passive connection between the robot body and the rope master-point. These characteristics render the study of a rope-assisted bipedal robot both fascinating and unexplored, raising motion planning challenges when attempting to plan dynamic suspended maneuvers, as rappelling. To this end, we first introduce a template three-mass model of a bipedal robot connected through passive joints to an extensible rope, which is in turn modeled as a two-mass body. Based on this, a family of optimal control problems is presented to plan rappelling maneuvers.

The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts, pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control planning of lower-body primitives, based on a template kinematic model, and online control of the upper-body motion to maintain balance. Three stabilizing strategies are presented, whose performance is compared in two types of simulated jumps, while experimental validation is performed on a half-squat jump using the CENTAURO robot.

This paper presents a framework providing a full pipeline to execute a complex physical interaction behaviour of a humanoid bipedal robot, both from a theoretical and a practical standpoint. Building from a multi-contact control architecture that combines contact planning and reactive force distribution capabilities, the main contribution of this work consists in the integration of a sample-based motion planning layer conceived for transitioning movements where obstacle and self-collisions avoidance is involved. To plan these motions we use Rapidly Exploring Random Tree (RRT) projected on the contacts manifold and validated through the Centroidal Statics (CS) model, to ensure static balance on non-coplanar surfaces. Finally, we successfully validate the presented planning and control architecture on the humanoid robot COMAN+ performing a wall-plank task.

Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a data-driven controller design method, based on the Virtual Reference Feedback Tuning (VRFT) approach, is introduced. Advantages in terms of robustness with respect to unknown environment stiffness are discussed and demonstrated. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.

The present paper exploits set invariance theory to address the problem of robot implicit force control in presence of stiffness uncertainty in the interaction model. A numerical approach is introduced to compute the invariance function for constraints with arbitrary relative degree. The method is then applied to robot force control, enhancing force regulation performance, in terms of steady state error and convergence speed, despite model mismatch and measurement noise. Its effectiveness is experimentally validated and compared to previous results of set invariance control on a hybrid force/position task performed with a 6 degrees of freedom industrial robot equipped with a force/torque sensor.

In this work a constraint-based control approach is proposed in order to perform a cap rotation task with a dual-arm robot. The method relies on the introduction of a robust specification for the constraint on the interaction force arising during the task, accounting for robot-environment contact model uncertainties, in addition to force measurement noise and surface uncertainties. Experiments have been performed on an ABB dual-arm prototype robot to validate the proposed approach in a cap assembly task, employing a model-based sensorless observer of the interaction forces.

Fast and sensorless peg-in-hole insertion is a challenging task for a robotic manipulator. In order to deal with the peg-in-hole insertion problem without any need of an external force/torque sensor, this paper proposes to actively accomplish compliance in the insertion task relying on an admittance based control. This is combined with a real-time trajectory generator, by means of constraint based optimization, where a model-based sensorless observer of interaction forces is exploited. Experiments have been performed on an ABB dual-arm 7-DOF lightweight prototype robot to validate the proposed approach, with an insertion speed comparable to human manual execution and in presence of geometric uncertainty.

Classical control approaches to robot force control have been extensively addressed by research in the last decades and are now considered a paradigm when dealing with force control for industrial robots. With this respect, the present paper exploits the capability of state-of-the-art Quadratic Programming (QP) solvers to specify a simple and intuitive constraint-based optimization strategy aiming at improving closed-loop performance of a classical force controller, such as the implicit force control with pure integral action for a position-controlled manipulator in contact with a compliant environment. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.

Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.

This paper addresses the problem of collision avoidance in human-robot interaction. To this end, we introduce the concept of kinetostatic safety field, a novel safety assessment about the risk in the vicinity of a rigid body (including a robot link or a human body part). The safety field depends on the position and velocity of the body but it is also influenced by its real shape and size. Since all the computation can be performed in closed form, the safety field is suitable for real-time applications. Moreover, we present a safety-oriented control strategy for redundant manipulators, based on safety field and developed entirely on the kinematic level, where the kinematic redundancy is exploited for simultaneous task performance and collision avoidance, such as self-collision avoidance and human-robot coexistence. The proposed control strategy is validated through experiments performed on ABB's FRIDA dual arm robot.