Equilibrium-Based Force and Torque Control for an Aerial Manipulator to Interact with a Vertical Surface

In this paper, a force and torque controller for aerial manipulation is developed using an unmanned aerial vehicle equipped with a robotic arm to interact near or on a vertical surface such as a wall. Control of aerial manipulators interacting with the environment is a challenging task due to dynamic interactions between aerial vehicles, robotic arms, and environment. To achieve this, modeling of aerial manipulators is first investigated and presented considering interaction with the environment. Nonlinear models of generic aerial manipulators, as well as of a prototype aerial manipulator composed of a hexacopter with a three-joint robotic arm, are established. An equilibrium-based force and torque controller is developed to conduct tasks that require the aerial manipulator to exert forces and torques on a wall. Simulations and experiments validate the performance of the controller that successfully applies desired forces and torques to an object fixed on a wall while flying near the wall

Neural-learning-based force sensorless admittance control for robots with input deadzone

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic manipulators in response to external torques from the unknown environment is achieved by admittance control. Inspired by broad learning system (BLS), a flatted neural network structure using Radial Basis Function (RBF) with incremental learning algorithm is proposed to estimate the external torque, which can avoid retraining process if the system is modelled insufficiently. To deal with uncertainties in the robot system, an adaptive neural controller with dynamic learning framework is developed to ensure the tracking performance. Experiments on the Baxter robot have been implemented to test the effectiveness of the proposed method

Sensor Fusion of Force and Acceleration for Robot Force Control

In this paper, robotic sensor fusion of acceleration and force measurement is considered. We discuss the problem of using accelerometers close to the end-effectors of robotic manipulators and how it may improve the force control performance. We introduce a new model-based observer approach to sensor fusion of information from various different sensors. During contact transition, accelerometers and force sensors play a very important role and it can overcome many of the difficulties of uncertain models and unknown environments, which limit the domain of application of currents robots used without external sensory feedback. A model of the robot-grinding tool using the new sensors was obtained by system identification. An impedance control scheme was proposed to verify the improvement. The experiments were carried out on an ABB industrial robot with open control system architecture

CAD-based approach for identification of elasto-static parameters of robotic manipulators

The paper presents an approach for the identification of elasto-static parameters of a robotic manipulator using the virtual experiments in a CAD environment. It is based on the numerical processing of the data extracted from the finite element analysis results, which are obtained for isolated manipulator links. This approach allows to obtain the desired stiffness matrices taking into account the complex shape of the links, couplings between rotational/translational deflections and particularities of the joints connecting adjacent links. These matrices are integral parts of the manipulator lumped stiffness model that are widely used in robotics due to its high computational efficiency. To improve the identification accuracy, recommendations for optimal settings of the virtual experiments are given, as well as relevant statistical processing techniques are proposed. Efficiency of the developed approach is confirmed by a simulation study that shows that the accuracy in evaluating the stiffness matrix elements is about 0.1%.Comment: arXiv admin note: substantial text overlap with arXiv:0909.146

Safe Robotic Grasping: Minimum Impact-Force Grasp Selection

This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving the longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper, we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms applying minimum impact forces if a collision occurs during a desired post-grasp manipulation. We build on the methods of augmented robot-object dynamics models and "effective mass" and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the post-grasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201

Stiffness modeling of robotic manipulator with gravity compensator

The paper focuses on the stiffness modeling of robotic manipulators with gravity compensators. The main attention is paid to the development of the stiffness model of a spring-based compensator located between sequential links of a serial structure. The derived model allows us to describe the compensator as an equivalent non-linear virtual spring integrated in the corresponding actuated joint. The obtained results have been efficiently applied to the stiffness modeling of a heavy industrial robot of the Kuka family

Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments

This paper presents two novel control methodologies for the cooperative manipulation of an object by N robotic agents. Firstly, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Secondly, we propose a control protocol that guarantees predefined transient and steady-state performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid, and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudo-inverse to account for potential differences in the agents' power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings

Control of an anthropomorphic manipulator involved in physical human-robot interaction

Dissertação de mestrado em Engenharia MecânicaThe objective of the dissertation is to flexibly control the end effector velocity of a redundant 7-DOF manipulator by using a differential kinematics approach, while ensuring the safety of the robotic arm from exceeding the physical limits of joints in terms of position, velocity and acceleration. The thesis also contributes with a real-time obstacle avoidance strategy for controlling anthropomorphic robotic arms in dynamic obstacle environments, taking account of sudden appearances or disappearances of mobile obstacles. A method for compensating force errors due to changes in the orientation of end effectors, independent from structures of force sensors, is developed to achieve high accuracy in force control applications. A novel method, the Virtual Elastic System, is proposed to control mobile manipulators for physical Human-Robot Interaction (pHRI) tasks in dynamic environments, which enables the combination of an Inverse Differential Kinematics for redundant robotic arms and a Dynamical Systems approach for nonholonomic mobile platforms. Experiments with a 7-DOF robotic arm, side-mounted on a nonholonomic mobile platform, are presented with the whole robot obstacle avoidance, proving the efficiency of the developed method in pHRI scenarios, more specifically, cooperative human-robot object transportation tasks in dynamic environments. Extensions of the method for other mobile manipulators with holonomic mobile platforms or higher degrees of freedom manipulators are also demonstrated through simulations

Stiffness Analysis Of Multi-Chain Parallel Robotic Systems

The paper presents a new stiffness modelling method for multi-chain parallel robotic manipulators with flexible links and compliant actuating joints. In contrast to other works, the method involves a FEA-based link stiffness evaluation and employs a new solution strategy of the kinetostatic equations, which allows computing the stiffness matrix for singular postures and to take into account influence of the external forces. The advantages of the developed technique are confirmed by application examples, which deal with stiffness analysis of a parallel manipulator of the Orthoglide famil