1,721,133 research outputs found
Efficient ISO/TS 15066 Compliance through Model Predictive Control
No full textIn the actual industrial scenarios, human operators and robots work together sharing the workspace. Such proximity requires special attention in ensuring safety for the human operator, which is often translated in collision avoidance behaviour or high speed reduction. Adhering safety however is not the only aspect that must be taken into account. For many tasks, such as welding, it is crucial to ensure that the robot performs exactly the planned path. To optimize robot performance while complying with safety regulations, this work introduces a novel optimal nonlinear control problem. It prioritizes path preservation, exploiting redundancy to minimize task execution time, while explicitly adhering to the constraints imposed by ISO/TS 15066. To achieve high-performance outcomes, the control problem is addressed using the Model Predictive Control (MPC) approach. The proposed strategy has been experimentally validated in both simulations and a real-world industrial task involving a Kuka LWR4+ robot
An Optimal Human-Based Control Approach for Mobile Human-Robot Collaboration
No full textIn collaborative robotic applications, human and robot have to cooperate in executing a common job. During the collaboration, the operator’s experience in the job is of paramount importance. Furthermore, it is essential to ensure that the two agents are always close enough to perform the assigned task, without jeopardizing the safety of the operator. In this paper, we propose an integrated architecture that allows the human operator to drive the collaboration based on its position. The robot is forced to stay close to the human during the execution of the task, but without colliding. Moreover the architecture is capable of changing online the size of the collaborative area, making it suitable for most of the collaborative jobs. The proposed approach is validated on a UR10e mobile manipulator which has been mounted on a MIR100 collaborative platform
Optimal Energy Tank Initialization for Minimum Sensitivity to Model Uncertainties
No full textEnergy tanks have gained popularity inside the robotics and control communities over the last years, since they represent a formidable tool to enforce passivity (and, thus, input/output stability) of a controlled robot, possibly interacting with uncertain environments. One weak point of passification strategies based on energy tanks concerns, however, their initialization. Indeed, a too large initial energy can cause practical unstable behaviors, while a too low initial energy level can prevent the correct execution of the task. This shortcoming becomes even more relevant in presence of uncertainties in the robot model and/or environment, since it may be hard to predict in advance the correct (safe) amount of initial tank energy for a successful task execution. In this paper we then propose a new strategy for addressing this issue. The recent notion of closed-loop state sensitivity is exploited to derive precise bounds (tubes) on the tank energy behavior by assuming parametric uncertainty in the robot model. These tubes are then exploited in a novel nonlinear optimization problem aiming at finding both the best trajectory and the minimal initial tank energy that allow executing a positioning task for any value of the uncertain parameters in a given range. The approach is finally validated via a statistical analysis in simulation and experiments on real robot hardware
A Safety-Aware Architecture for Task Scheduling and Execution for Human-Robot Collaboration
No full textIn collaborative robotic applications, human and robot have to work together to accomplish a common job, composed by a set of tasks. In order to achieve an efficient human-robot collaboration (HRC), it is important to have an integration between a proper task scheduling strategy and a task execution strategy. The first must deal with the variability of the two agents, while the second must deal with the safety standards. In this paper, we propose an integrated architecture for task scheduling and execution in a collaborative cell. The tasks are dynamically scheduled handling the uncertainity in both the human and the robot behaviors. Subsequently, at the execution level, the task is accomplished computing trajectories comply with the safety regulations. The planning information are mutually integrated in real-time with the scheduling procedure in order improve the HRC
Emotion-Aware Control Framework for Human-Robot Collaboration
No full textThe advent of industrial scenarios involving close interaction between humans and robots, without physical barriers, requires careful examination of the impact on safety and human work experience. In this paper a control framework for Human-Robot Collaboration (HRC) that explicitly integrates human emotions and ISO/TS 15066 safety requirements is proposed. The framework employs a motion planning strategy to generate collision-free trajectories considering only the robot joints limits. Subsequently, the speed of the robot is modulated online in order to ensure a safe and efficient collaboration. In other words, the behaviour of the robot adapts to the human operator emotions. The overall framework has been validated on a UR5e
A Time-Optimal Energy Planner for Safe Human-Robot Collaboration
No full textThe human-robot collaboration scenarios are characterized by the presence of human operators and robots that work in close contact with each other. As a consequence, the safety regulations have been updated in order to provide guidelines on how to asses safety in these new scenarios. In particular, Power and Force Limiting (PFL) collaborative mode describes how the energy should be regulated during the collaboration. Based on these guidelines, we propose a new optimal trajectory planner which, by exploiting the variability of the robot's inertia as a function of its configuration, is able to return trajectories that can be travelled at greater speed and in less time, while guaranteeing the safety limits according to the standard. The proposed planner was validated first in simulation, comparing completion times with other state-of-the-art planning algorithms, and then experimentally, demonstrating the performance of the planned trajectories during physical interaction with the environment. Both validations confirm the effectiveness of the proposed planner, which returns shorter completion times while ensuring safe interaction
A Novel DMPs Based Approach to Comply ISO/TS 15066
No full textLearning by demonstration techniques are becoming popular in human-robot collaboration (HRC) because they allow exploiting the versatility of collaborative robots. In this context, Dynamic Motion Primitives (DMPs) are a standard method for allowing human operators to easily teach tasks to robots. However, DMPs do not guarantee compliance with ISO/TS 15066 safety standards. This work addresses these issues by introducing a control pipeline that adapts DMPs through an optimization problem to ensure compliance with the Speed and Separation Monitoring (SSM) safety mode. The approach has been experimentally validated in a real scenario, where a UR5e robot and a human operator collaborate on a polishing task
A Safety-Aware Kinodynamic Architecture for Human-Robot Collaboration
No full textThe new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other. Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior. The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator
Safe Multimodal Communication in Human-Robot Collaboration
No full textThe new industrial settings are characterized by the presence of human and robots that work in close proximity, cooperating in performing the required job. Such a collaboration, however, requires to pay attention to many aspects. Firstly, it is crucial to enable a communication between this two actors that is natural and efficient. Secondly, the robot behavior must always be compliant with the safety regulations, ensuring always a safe collaboration. In this paper, we propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands while always respecting safety regulations. The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task and additionally, with the safety layer, the robot can scale its speed to ensure the operator’s safety
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