This article investigates the problem of controlling the speed of robots in collaborative workcells for automated manufacturing. The solution is tailored to robotic cells for cooperative assembly of aircraft fuselage panels, where only structural elements are present and robots and humans can share the same workspace, but no physical contact is allowed, unless it happens at zero robot speed. The proposed approach addresses the problem of satisfying the minimal set of requirements of an industrial human–robot collaboration (HRC) task: precision and reliability of human detection and tracking in the shared workspace; correct robot task execution with minimum cycle time while assuring safety for human operators. These requirements are often conflicting with each other. The former does not only concern with safety only but also with the need of avoiding unnecessary robot stops or slowdowns in case of false-positive human detection. The latter, according to the current regulations, concerns with the need of computing the minimum protective separation distance between the human operator and the robots by adjusting their speed when dangerous situations happen. This article proposes a novel fuzzy inference approach to control robot speed enforcing safety while maximizing the level of productivity of the robot minimizing cycle time as well. The approach is supported by a sensor fusion algorithm that merges the images acquired from different depth sensors with those obtained from a thermal camera, by using a machine learning approach. The methodology is experimentally validated in two experiments: the first one at a lab-scale and the second one performed on a full-scale robotic workcell for cooperative assembly of aeronautical structural parts. Note to Practitioners —This article discusses a way to handle human safety specifications versus production requirements in collaborative robotized assembly systems. State-of-the-art (SoA) approaches cover only a few aspects of both human detection and robot speed scaling. The present research work proposes a complete pipeline that starts from a robust human tracking algorithm and scales the robot speed in real time. An innovative multimodal perception system composed of two depth cameras and a thermal camera monitors the collaborative workspace. The speed scaling algorithm is optimized to take on different human behaviors during less risky situations or more dangerous ones to guarantee both operator safety and minimum production time with the aim of better profitability and efficiency for collaborative workstations. The algorithm estimates the operator intention for real-time computation of the minimum protective distance according to the current safety regulations. The robot speed is smoothly changed for the psychological advantages of operators, both in the case of single and multiple workers. The result is a complete system, easily implementable on a standard industrial workcell.

A robust grasp controller for both slipping avoidance and controlled sliding is proposed based on force/tactile feedback only. The model-based algorithm exploits a modified LuGre friction model to consider rotational frictional sliding motions. The modification relies on the Limit Surface concept where a novel computationally efficient method is introduced to compute in real-time the minimum grasping force to balance tangential and torsional loads. The two control modalities are considered by the robot motion planning algorithm that automatically generates robot motions and gripper commands to solve complex manipulation tasks in a material handling application.

This article describes a set of control algorithms for in-hand object handling using a parallel jaw gripper equipped with force/tactile sensors. The control strategy is model based and relies upon the limit surface concept. The LuGre friction model is combined with the limit surface method to set up a dynamic model of soft contact. The model is exploited to estimate the relative velocity of the object with respect to the fingers, so as to control the grip force to counteract possible slipping events due to external disturbances. Force/tactile feedback, the only perception source used by the algorithms, is suitably exploited not only for safe grasping of a variety of objects with uncertain weight and inertial properties, but also for in-hand manipulation actions, like object pivoting or gripper pivoting. Since the algorithm is based on the control of the object velocity, accuracy of the desired object positioning depends on the initial grasp configuration, as well as on the accuracy of the friction model parameters. Such manipulation skills are evaluated in the execution of various pick and place tasks typical of an in-store logistic scenario.

Workspace monitoring is a critical hw/sw component of modern industrial work cells or in service robotics scenarios, where human operators share their workspace with robots. Reliability of human detection is a major requirement not only for safety purposes but also to avoid unnecessary robot stops or slowdowns in case of false positives. The present paper introduces a novel multimodal perception system for human tracking in shared workspaces based on the fusion of depth and thermal images. A machine learning approach is pursued to achieve reliable detection performance in multi-robot collaborative systems. Robust experimental results are finally demonstrated on a real robotic work cell.

The work implements a new real-time flexible motion planning method used for reactive object manipulation in pick and place tasks typical of in-store logistics scenarios such as shelf replenishment of retail stores.This method uses a new hybrid pipeline to recognize and localize an object observed through a depth camera, by integrating and optimizing state of the art techniques.The proposed algorithm guarantees recognition robustness and localization accuracy.The desired object is then manipulated.The motion planner, based on the obstacles detected in the scene, plans a collision-free path towards the target pose.The planned trajectory optimizes a cost function that reflects the best solution among those available and produces natural and smooth path through a smart IK constrained solution which avoids robot unnecessary reconfigurations.A reactive control based on distributed proximity sensors is finally adopted to locally modify the planned trajectory in real time to avoid collisions with uncertain or dynamic obstacles.Experimental results in a supermarket scenario populated with cluttered obstacles demonstrate smoothness of the robot motions and reactive capabilities in a typical fetch and carry task.

Parallel jaw grippers have a limited dexterity, however they can still be used for in-hand manipulation tasks, such as pivoting or other controlled sliding motions of the grasped object. A rotational sliding maneuver is challenging since the grasped object can easily slip if the grip force is not properly adjusted to allow rotational sliding while avoiding translational sliding at the same time. This paper has a twofold aim. First, it intends to refine control algorithms to avoid both rotational and linear slippage, already presented by the authors, by proposing a novel sliding motion model that leads to a grip force as small as possible to avoid slippage, so as to enlarge the set of fragile and deformable objects that can be safely grasped with this approach. Second, the paper exploits the motion model to set up a new algorithm for controlled rotational sliding, thus enabling challenging in-hand manipulation actions. All control algorithms are sensor-based, exploiting a sensorized gripper equipped with a six-axis force/tactile sensor, which provides contact force and torque measurements as well as orientation of the object with respect to the gripper. A set of experiments are executed on a Kuka iiwa showing how the proposed control algorithms are effective to both avoid slippage and allow a controlled sliding motion.

In-hand manipulation is certainly one of the most challenging problems in robotic manipulation. Solutions to this problem depend on the specific device used to grab the object, but nowadays, the trend is to exploit not only the gripper but also external constraints, such as other objects in the environment or external forces, like gravity. This allows a robot to manipulate an object even with very simple grippers, like a parallel gripper. Nevertheless, even for a simple grasping task, which aims at grabbing the object with a given fixed orientation or for executing a controlled slip, information on the contact between the fingers of the gripper and the object is relevant. In these cases, both linear and rotational slipping should be controlled during the grasping phase and during the motion phase. The present paper proposes a control strategy for the first objective, namely slipping avoidance. The strategy is based on contact information provided by a six-axis force/tactile sensor, able to measure contact force and torque as well as able to provide information on the contact geometry, that means orientation of the object with respect to the gripper. Experiments on a parallel gripper sensorized with a new force/tactile sensor and mounted on a Kuka iiwa show how the strategy successfully allows the robot to safely manipulate a rigid object in various friction conditions of its surface.

The main objective of this paper is to propose an experimental modal analysis procedure, based on the use of a gray-box model for flexible structures. The described approach presents interesting advantages with respect to commercial solutions: ease of use due to the low number of parameters to set for an identification session; no need for expert users, even in the presence of particular cases such as double modes, since it does not use a stabilization diagram to be elaborated; use of a gray-box model whose unknown parameters have a clear physical meaning. All these characteristics are discussed in the paper, and the performance of the proposed procedure has been evaluated by using experimental data available from a non-trivial standard benchmark. The results have been compared with those obtained by using a commercial tool.

This paper proposes an experimental study of slipping avoidance algorithms based on force/tactile perception data. The claim is that contact force measurements alone or tactile data alone are not sufficient for an effective slipping avoidance strategy in real world conditions. Integrated force/tactile sensors able to provide measurements of both the contact force vector and spatially distributed tactile maps are the key enabling technology for efficient slipping avoidance control algorithms that can actually work with real world objects under no restricting assumption on the contact geometry or with unknown physical properties of the objects. The paper proposes a new slipping avoidance control scheme, which usefully exploits an integrated force/tactile sensor mounted on the parallel gripper of a Kuka youBot. The results show how the strategy successfully allows the robot to safely manipulate real-world objects, both rigid and compliant, in various friction conditions of their surface, both stable and slippery.

The paper presents a novel slipping control algorithm based on the exploitation of the tactile sensor integrated into the DEXMART anthropomorphic robotic hand. The Extended Kalman Filter (EKF) is used to solve in real-time the nonlinear model of the sensor, allowing to estimate the contact geometry variables and the friction coefficient. The innovative proposed slipping control is based on the use of the estimation error of the EKF as an indicator of incipient slipping events. The control algorithm computes the suitable grip force based on the estimation error. The effectiveness of the proposed approach is shown with experimental results.

The control of a flexible arm is the focus of this work. The dynamic model is obtained via an assumed mode method, and a singularly perturbed model of the system is then derived. The new feature of this model is that multiple boundary layers are considered, one for each assumed mode. A composite control strategy is then pursued. First a slow control is designed for the rigid system, then a number of fast controls (one for each layer) make the fast dynamics in the layers suitably damped. A case study is finally worked out and simulation results are presented.