An event-controlled online trajectory generator based on the human-robot interaction force processing (original) (raw)
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An Online Trajectory generator-Based Impedance control for co-manipulation tasks
2014 IEEE Haptics Symposium (HAPTICS), 2014
This paper addresses the problem of heavy load co-manipulation in the context of physical human-robot interactions (PHRI). During PHRI, the resulting motion should be truly intuitive and should not restrict in any way the operator's will to move the robot such he would like. The idea proposed in this paper consists in considering the PHRI problem for handling tasks as a constrained optimal control problem. For this purpose, we have designed a new modified impedance control method named Online Trajectory generator-Based Impedance (OTBI) Control. This method relies on the implementation of a specific event controlled online trajectory generator (OTG) interconnected to a classical structure of impedance control allowing a good tracking of the generated trajectory. This OTG is designed so as to translate the human operator intentions to ideal trajectories that the robot must follow. It works as an automaton with two states of motion whose transitions are controlled by comparing the magnitude of the force to an adjustable threshold, in order to enable the operator to keep authority over the robot's states of motion. The key idea of this approach consists in generating a velocity trajectory for the end-effector that would stay collinear at every moment to the PHRI force. The overall strategy is applied to a two DOF robot.
Shared Admittance Control for Human-Robot Co-manipulation based on Operator Intention Estimation
Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, 2018
Collaborative robots are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In this paper, we assume the presence of an operator cooperating with a lightweight robotic arm, able to autonomously navigate its workspace, while the human co-worker physically interacts with it leading and influencing the execution of the shared task. In this scenario, we propose a human-robot co-manipulation method in which the autonomy of the robot is regulated according to the operator intentions. Specifically, the operator contact forces are assessed with respect to the autonomous motion of the robot inferring how the human motion commands diverges from the autonomous ones. This information is exploited by the system to adjust its role in the shared task, leading or following the operator and to proactively assist him during the co-manipulation. The proposed approach has been demonstrated in an industrial use case consisting of a human operator that interacts with a Kuka LBR iiwa arm to perform a cooperative manipulation task. The collected results demonstrate the effectiveness of the proposed approach.
Optimizing Motion of Robotic Manipulators in Interaction with Human Operators
Recently, the problem of how to manipulate industrial robots that interact with human operators attracts a lot of attention in robotics research. This interest stems from the insight that the integration of human operators into robot based manufacturing systems may increase productivity by combining the abilities of machines with those of humans. In such a Human-Robot-Interaction (HRI) setting, the challenge is to manipulate the robots both safely and efficiently. This paper proposes an online motion planning approach for robotic manipulators with HRI based on model predictive control (MPC) with embedded mixedinteger programming. Safety-relevant regions, which are potentially occupied by the human operators, are generated online using camera data and a knowledge-base of typical human motion patterns. These regions serve as constraints of the optimization problem solved online to generate control trajectories for the robot. As described in the last part of the paper, the proposed method is realized for a lab-scale HRI scenario.
Enhancing Shared Control via Contact Force Classification in Human-Robot Cooperative Task Execution
2017
In this paper, we present a novel method to support physical human-robot interaction during the execution of collaborative manipulation tasks. In the proposed approach, the robot is able to infer the operator intentions from the human contact forces, exploiting such information to properly react to the operator interventions and suitably adapt the execution of the shared task. In particular, we assume that the robotic system can autonomously generate and execute Cartesian trajectories, while a human operator can provide interventions exerting contact forces on the robot itself. The resulting robot motion is obtained by mixing in an adaptive manner the input commands provided by both the robotic control system and the human operator. In our approach, human intention estimation relies on a Neural Network capable of distinguishing the operator contact forces that support or oppose the autonomous motion planned by the robotic system. We tested the system at work in different scenarios c...
Human robot cooperation with compliance adaptation along the motion trajectory
Autonomous Robots
In this paper we propose a novel approach for intuitive and natural physical human-robot interaction in cooperative tasks. Through initial learning by demonstration, robot behavior naturally evolves into a cooperative task, where the human co-worker is allowed to modify both the spatial course of motion as well as the speed of execution at any stage. The main feature of the proposed adaptation scheme is that the robot adjusts its stiffness in path operational space, defined with a Frenet-Serret frame. Furthermore, the required dynamic capabilities of the robot are obtained by decoupling the robot dynamics in operational space, which is attached to the desired trajectory. Speed-scaled dynamic motion primitives are applied for the underlying task representation. The combination allows a human co-worker in a cooperative task to be less precise in parts of the task that require high precision, as the precision aspect is learned and provided by the robot. The user can This is one of the several papers published in Autonomous Robots comprising the Special Issue on Learning for Human-Robot Collaboration.
Structured Task Execution during Human-Robot Co-manipulation
2018
We consider a scenario in which a human operator physically interacts with a lightweight robotic manipulator to accomplish structured co-manipulation tasks. We assume that these tasks are interactively executed by combining the plan guidance and the human physical guidance. In this context, the human guidance is continuously monitored and interpreted by the robotic system to infer whether the human intentions are aligned or not with respect to the planned activities. This way, the robotic system can adapt the execution of the tasks according to the human intention. In this paper we present an overview of the overall framework and discuss some initial results.
A Controller Based on Online Trajectory Generation for Object Manipulation
2013
In this paper, we present a new solution to build a reactive trajectory controller for object manipulation in Human Robot Interaction (HRI) context. Using an online trajectory generator, the controller build a time-optimal trajectory from the actual state to a target situation every control cycle. A human aware motion planner provides a trajectory for the robot to follow or a point to reach. The main functions of the controller are its capacity to track a target, to follow a trajectory with respect to a task frame, or to switch to a new trajectory each time the motion planner provides a new trajectory. The controller chooses a strategy from different control modes depending on the situation. Visual servoing by trajectory generation and control is presented as one application of the approache. To illustrate the potential of the approach, some manipulation results are presented.
Force and position control of manipulators during constrained motion tasks
IEEE Transactions on Robotics and Automation, 1989
Trajectory control of a manipulator constrained by the contact of the end-effector with the environment represents an important class of control problems. In this paper, a method is proposed whereby both contact force exerted by the manipulator, and the position of the end-effector while in contact with the surface are controlled. The controller parameters are derived based on a linearized dynamic model of the manipulator during constrained motion. Hence the method is valid only in a neighborhood aboul the point of linearization. Additionally, a perfect kinematic model of the contact surface is assumed. The proposed method exploits the fundamental structure of the dynamic formulation of the manipulator's constrained motion. With this formulation, the trajectory control problem is naturally expressed in terms of the state vector variables of the model of the constrained dynamic system. A detailed numerical example illustrates the proposed method.
Sensors
The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
Control Strategies for Dual Arm Co-Manipulation of Flexible Objects in Industrial Environments
2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 2020
The introduction of collaborative robots had a great impact in the development of robotic solutions for cooperative tasks typically performed by humans, specially in industrial environments where robots can act as assistants of operators. Even so, the coordinated manipulation of large and deformable parts between dual arm robots and humans rises many technical challenges, ranging from the coordination of both robotic arms to the detection of the forces applied by the operator. This paper presents a novel control architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop. The obtained results demonstrate the validity of the implemented control architecture as well as its suitability for the implementation of collaborative cyber-physical systems.