Showing papers by "Paolo Rocco published in 2018"

Operator Awareness in Human–Robot Collaboration Through Wearable Vibrotactile Feedback

Abstract: In industrial scenarios, requiring human–robot collaboration, the understanding between the human operator and his/her robot coworker is paramount. On the one side, the robot has to detect human intentions, and on the other side, the human needs to be aware of what is happening during the collaborative task. In this letter, we address the first issue by predicting human behavior through a new recursive Bayesian classifier, exploiting head, and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgments to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human–robot collaboration scenario, and we found that adding haptic feedback is particularly helpful to improve the performance when the human–robot cooperation task is performed by nonskilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interface, able to give information about the prediction reliability, are essential features to improve performance in a human–robot collaboration in industrial environments.

Occlusion-Free Visual Servoing for the Shared Autonomy Teleoperation of Dual-Arm Robots

Abstract: Visual servoing in telerobotics provides information to the operator about the remote location and assists in the task execution to reduce stress on the user. Occlusions in this scenario can, on the one hand, lead to the visual servoing failure, and, on the other hand, degrade the user experience and navigation performance due to the obstructed vision of elements of interest. In this letter, we consider a teleoperation system composed of two robot arms where one is remotely operated, while the other is autonomous and equipped with an eye-in-hand camera sensor. We propose a reactive unified convex optimization based controller that allows the execution of occlusion-free tasks by autonomously adjusting the camera robot, so as to keep the teleoperated one in the field of view. The occlusion avoidance is formulated inside the optimization as a constraint in the image space, it is formally derived from a collision avoidance analogy and made robust against noisy measurements and dynamic environment. A state machine is used to switch the control policy whenever an occlusion might occur. We validate our approach with experiments on a 14 d.o.f. dual-arm ABB YuMi robot equipped with an red, green, blue (RGB) camera and teleoperated by a 3 d.o.f. Novint Falcon device.

Constrained model predictive control for mobile robotic manipulators

Abstract: This paper discusses the application of a constraint-based model predictive control (MPC) to mobile manipulation tracking problems. The problem has been formulated so as to guarantee offset-free tracking of piecewise constant references, with convergence and recursive feasibility guarantees. Since MPC inputs are recomputed at every control iteration, it is possible to deal with dynamic and unknown scenarios. A number of motion constraints can also be easily included: Acceleration, velocity and position constraints have been enforced, together with collision avoidance constraints for the mobile base and the arm and field-of-view constraints. Such constraints have been extended over the prediction horizon maintaining a linear-quadratic formulation of the problem. Navigation performance has been improved by devising an online algorithm that includes an additional goal to the problem, derived from the classical vortex field approach. Experimental validation shows the applicability of the proposed approach.

Human Intention Estimation based on Neural Networks for Enhanced Collaboration with Robots

Abstract: In human-robot collaboration, the robot is required to provide assistance to the user by facilitating task execution. However, due to stability requirements, a well-damped admittance behavior of the robot is necessary during interaction, thus inducing fatigue in the operator. While available schemes involve variable impedance controllers to mitigate this effect, here we propose an alternative approach entailing a proactive robot behavior that assists in the cooperative execution of trajectories towards desired goals, by estimating the user intention. To this end, we make use of Recurrent Neural Networks (RNNs) to predict and classify cooperative motions, on the basis of a set of predefined goals in the workspace and model-based generated data of human movements. Manual guidance validation experiments are conducted on a 6 d.o.f. ABB IRB140 industrial robot equipped with a force sensor.

Robotic Handling of Liquids with Spilling Avoidance: A Constraint-Based Control Approach

Abstract: Handling liquids with spilling avoidance is a topic of interest for a broad range of fields, both in industry and in service robotic applications In this paper we present a new control architecture for motion planning of industrial robots, able to tackle the problem of liquid transfer with sloshing control We do not focus on a complete sloshing suppression, but we show how to enforce an anti spilling constraint This less conservative approach allows to impose higher accelerations, reducing motion time A constraint-based approach, amenable to an Online implementation, has been developed The proposed controller generates trajectories in real time, in order to follow a reference path, while being compliant to the spilling avoidance constraint The approach has been validated on a 6 degree of freedom industrial ABB robot

Human Pose Estimation in Presence of Occlusion Using Depth Camera Sensors, in Human-Robot Coexistence Scenarios

Abstract: Collaborative robotics over the last few years has gained increasing interest in the industrial scenario. Co-bots can be equipped with vision sensors and cognitive software layers, allowing the robot to figure out human intentions. To make this level of perception possible, human pose estimation algorithms are required. Several techniques have been already proposed to tackle this problem, which however present some weaknesses in particular when occlusions occur. This work proposes an algorithm for human pose estimation in the situations of partial occlusion, based on particle filter techniques. We have proved its validity in a realistic human-robot coexistence scenario, where a human and a dual arm robot have to perform tasks in a shared workspace.

Hybrid control of manipulators in human-robot coexistence scenarios

Abstract: This paper addresses safe motion planning for a manipulator, in environments shared with human operators. We formulate the problem as an optimal control problem for a hybrid system integrating three different operation modes: nominal, soft safety, and hard safety. The manipulator is assigned a nominal trajectory to reach a target position. If no human is present, then, the manipulator tracks the nominal trajectory; if a human enters its workspace, it tries to avoid it but without adopting too sharp and abrupt actions, except when strictly needed for safety. The decision on when and to what mode to commute is taken online, via a model predictive control approach involving a constrained optimization program with binary variables setting the active/nonactive status of the operating modes. The resulting control input is applied in a receding horizon fashion and the nominal trajectory towards the target is re-computed based on the current state of the system. The proposed approach is applied to a realistic simulation environment and appears computationally feasible and promising.