586 research outputs found

    A survey of robot manipulation in contact

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    In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks

    Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

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    In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

    Trajectory Generation for Assembly Tasks Via Bilateral Teleoperation

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    Abstract in UndeterminedFor assembly tasks, the knowledge of both trajectory and forces are usually required. Consequently, we may use kinesthetics or teleoperation for recording human demonstrations. In order to have a more natural interaction, the operator has to be provided with a sense of touch. We propose a bilateral teleoperation system which is customized for this purpose. We introduce different coordinate frames to make the design of a 6-DOF teleoperation straightforward. Moreover, we suggest using tele-admittance, which simplifies instructing the robot. The compliance due to the slave controller allows the robot to react quickly and reduces the risk of damaging the workpiece

    Cooperative Object Manipulation with Force Tracking on the da Vinci Research Kit

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    The da Vinci Surgical System is one of the most established robot-assisted surgery device commended for its dexterity and ergonomics in minimally invasive surgery. Conversely, it inherits disadvantages which are lack of autonomy and haptic feedback. In order to address these issues, this work proposes an industry-inspired solution to the field of force control in medical robotics. This approach contributes to shared autonomy by developing a controller for cooperative object manipulation with force tracking utilizing available manipulators and force feedback. To achieve simultaneous position and force tracking of the object, master and slave manipulators were assigned then controlled with Cartesian position control and impedance control respectively. Because impedance control requires a model-based feedforward compensation, we identified the lumped base parameters of mass, inertias, and frictions of a three degree-of-freedom double four-bar linkage mechanism with least squares and weighted least squares regression methods. Additionally, semidefinite programming was used to constrain the parameters to a feasible physical solution in standard parameter space. Robust stick-slip static friction compensation was applied where linear Viscous and Coulomb friction was inadequate in modeling the prismatic third joint. The Robot Operating System based controller was tested in RViz to check the cooperative kinematics of up to three manipulators. Additionally, simulation with the dynamic engine Gazebo verified the cooperative controller applying a constant tension force on a massless spring-damper virtual object. With adequate model feedback linearization, the cooperative impedance controller tested on the da Vinci Research Kit yielded stable tension force tracking while simultaneously moving in Cartesian space. The maximum force tracking error was +/- 0.5 N for both a compliant and stiff manipulated object

    Energy-based control approaches in human-robot collaborative disassembly

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    Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References

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    This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors

    Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

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    Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors

    An Omnidirectional Aerial Platform for Multi-Robot Manipulation

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    The objectives of this work were the modeling, control and prototyping of a new fully-actuated aerial platform. Commonly, the multirotor aerial platforms are under-actuated vehicles, since the total propellers thrust can not be directed in every direction without inferring a vehicle body rotation. The most common fully-actuated aerial platforms have tilted or tilting rotors that amplify the aerodynamic perturbations between the propellers, reducing the efficiency and the provided thrust. In order to overcome this limitation a novel platform, the ODQuad (OmniDirectional Quadrotor), has been proposed, which is composed by three main parts, the platform, the mobile and rotor frames, that are linked by means of two rotational joints, namely the roll and pitch joints. The ODQuad is able to orient the total thrust by moving only the propellers frame by means of the roll and pitch joints. Kinematic and dynamic models of the proposed multirotor have been derived using the Euler- Lagrange approach and a model-based controller has been designed. The latter is based on two control loops: an outer loop for vehicle position control and an inner one for vehicle orientation and roll-pitch joint control. The effectiveness of the controller has been tested by means of numerical simulations in the MATLAB c SimMechanics environment. In particular, tests in free motion and in object transportation tasks have been carried out. In the transportation task simulation, a momentum based observer is used to estimate the wrenches exchanged between the vehicle and the transported object. The ODQuad concept has been tested also in cooperative manipulation tasks. To this aim, a simulation model was considered, in which multiple ODQuads perform the manipulation of a bulky object with unknown inertial parameters which are identified in the first phase of the simulation. In order to reduce the mechanical stresses due to the manipulation and enhance the system robustness to the environment interactions, two admittance filters have been implemented: an external filter on the object motion and an internal one local for each multirotor. Finally, the prototyping process has been illustrated step by step. In particular, three CAD models have been designed. The ODQuad.01 has been used in the simulations and in a preliminary static analysis that investigated the torque values for a rough sizing of the roll-pitch joint actuators. Since in the ODQuad.01 the components specifications and the related manufacturing techniques have not been taken into account, a successive model, the ODQuad.02, has been designed. The ODQuad.02 design can be developed with aluminum or carbon fiber profiles and 3D printed parts, but each component must be custom manufactured. Finally, in order to shorten the prototype development time, the ODQuad.03 has been created, which includes some components of the off-the-shelf quadrotor Holybro X500 into a novel custom-built mechanical frame
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