Dual-arm Manipulator Research Articles

Dual-arm Cable Manipulation by Whole-body Control of a Humanoid Robot

Dual-arm Cable Manipulation by Whole-body Control of a Humanoid Robot

Development of a dual Arm Manipulator aimed for human-robot collaboration

Development of a dual Arm Manipulator aimed for human-robot collaboration

Design of an Anthropomorphic, Compliant, and Lightweight Dual Arm for Aerial Manipulation

This paper presents an anthropomorphic, compliant and lightweight dual arm manipulator designed and developed for aerial manipulation applications with multi-rotor platforms. Each arm provides four degrees of freedom in a human-like kinematic configuration for end effector positioning: shoulder pitch, roll and yaw, and elbow pitch. The dual arm, weighting 1.3 kg in total, employs smart servo actuators and a customized and carefully designed aluminum frame structure manufactured by laser cut. The proposed design reduces the manufacturing cost as no computer numerical control machined part is used. Mechanical joint compliance is provided in all the joints, introducing a compact spring-lever transmission mechanism between the servo shaft and the links, integrating a potentiometer for measuring the deflection of the joints. The servo actuators are partially or fully isolated against impacts and overloads thanks to the flange bearings attached to the frame structure that support the rotation of the links and the deflection of the joints. This simple mechanism increases the robustness of the arms and safety in the physical interactions between the aerial robot and the environment. The developed manipulator has been validated through different experiments in fixed base test-bench and in outdoor flight tests.

A Closed-Form Analytical Modeling of Internal Impulses With Application to Dynamic Machining Task: Biologically Inspired Dual-Arm Robotic Approach

Inspired by the human upper extremity, a musculoskeletal dual-arm manipulator is designed for light machining tasks such as hammering and sawing. We claim that musculoskeletal structure has the advantage over conventional robot structures in terms of less internal impulsive forces while performing these machining tasks. We investigated the advantages (in terms of external and internal impulses) of a musculoskeletal dual-arm model over a dual-arm model without muscles, musculoskeletal single arm model, and single arm model without muscles. Moreover, to model the hardness of the material during these tasks, an effective mass model was newly developed and experimentally verified for two different (soft and hard) materials. In order to validate the proposed methodology, a belted ellipsoid denoting the external and internal impulse geometry is employed to analyze the sawing and hammering tasks. Finally, it was found by performing a hammering task that the musculoskeletal dual-arm model is more effective in terms of generating more external impulse while experiencing less internal impulses at the joints.

Toward optimal mapping of human dual-arm motion to humanoid motion for tasks involving contact with the environment

In this article, we explore human motion skills in the dual-arm manipulation tasks that include contact with equipment with the final aim to generate human-like humanoid motion. Human motion is analyzed using the optimization approaches starting with the assumption that human motion is optimal. A combination of commonly used optimization criteria in the joint space with the weight coefficients is considered: minimization of kinetic energy, minimization of joint velocities, minimization of the distance between the current and ergonomic positions, and maximization of manipulability. The contribution of each criterion for seven different dual-arm manipulation tasks to provide the most accurate imitation of the human motion is given via suggested inverse optimization approach calculating values of weight coefficients. The effects on actors’ body characteristics and the characteristics of the environment (involved equipment) on the choice of criterion functions are additionally analyzed. The optimal combination of weight coefficients calculated by the inverse optimization approach is used in our inverse kinematics algorithm to transfer human motion skills to the motion of the humanoid robots. The results show that the optimal combination of weight coefficients is able to generate human-like humanoid motions rather than individual one of the considered criterion functions. The recorded human motion and the motion of the humanoid robot ROMEO, obtained with the strategy used by human and defined by our inverse optimal control approach, for the tasks “opening/closing a drawer” are assessed visually and quantitatively.

고가반 하중의 원격 제어용 구조로봇 개발

We introduce to a humanoid rescue robot that is designed to have a large load carrying capacity. The robot’s goal is to lift and transfer patients or soldiers with impaired mobility for rescue and assistance on the battlefield or degraded human engineered environments. The other goal is to dispose of and move dangerous objects or explosive ordnance. The rescue robot is designed to have dual arm manipulators and a hybrid tracked and legged mobile platform to execute this kind of task. Real time closed loop inverse kinematics and dynamics-based motion optimization enables the effective performance of rescue tasks. The mechanical design and control algorithms are explained and the effectiveness is demonstrated in physical experiments with the humanoid rescue robot HURCULES.

Parameter estimator integrated-sliding mode control of dual arm robots

This paper presents the parameter estimator-integrated sliding mode control of dual arm robots. To do so, a robust adaptive controller is designed for 3D cooperative motion of dual-arm robots based on the frame of second-order sliding mode control (SOSMC). Additionally, the method of the model-reference adaptive control (MRAC) is also utilized for constituting an adaptation mechanism then integrating it into the control loop to estimate the unknown robot parameters. By doing so, the proposed controller is robust with disturbances and parametric uncertainties. Furthermore, the adaptive behavior is also achieved in which the control system does not need the information of many system parameters. Finally, the applicability and feasibility of the proposed controller is presented through a 4 four degree-of-freedoms (DOFs) dual-arm manipulator.

Adaptive neural network second-order sliding mode control of dual arm robots

An adaptive robust control system is considered for dual-arm manipulators (DAM) using the combination of second-order sliding mode control (SOSMC) and neural networks. The SOSMC deals with the system robustness when faced with external disturbances and parametric uncertainties. Meanwhile, the radial basis function network (RBFN) is to constitute an adaptation mechanism for approximating the unknown dynamic model of DAM. The stability of model estimator-integrated controller is analyzed using Lyapuov theory. To show the effectiveness of proposed controller, a four DOFs-DAM is applied as an illustrating example. The results reveal that the controller works well, excellently adapt to no information of robot modeling.

Multimodal sensor-based whole-body control for human–robot collaboration in industrial settings

This paper describes the development of a dual-arm robotic system for industrial human–robot collaboration. The robot demonstrator described here possesses multiple sensor modalities for the monitoring of the shared human–robot workspace and is equipped with the ability for real-time collision-free dual-arm manipulation. A whole-body control framework is used as a key control element which generates a coherent output signal for the robot’s joints given the multiple controller inputs, tasks’ priorities, physical constraints, and current situation. Furthermore, sets of controller-constraints combinations of the whole-body controller constitute the basic building blocks that describe actions of a high-level action plan to be sequentially executed. In addition, the robotic system can be controlled in an intuitive manner via human gestures. These individual robotic capabilities are combined into an industrial demonstrator which is validated in a gearbox assembly station of a Volkswagen factory.

Design of an anthropomorphic dual-arm robot with biologically inspired 8-DOF arms

From the perspective of kinematics, dual-arm manipulation in robots differs from single-arm manipulation in that it requires high dexterity in a specific region of the manipulator's workspace. This feature has motivated research on the specialized design of manipulators for dual-arm robots. These recently introduced robots often utilize a shoulder structure with a tilted angle of some magnitude. The tilted shoulder yields better kinematic performance for dual-arm manipulation, such as a wider common workspace for each arm. However, this method tends to reduce total workspace volume, which results in lower kinematic performance for single-arm tasks in the outer region of the workspace. To overcome this trade-off, the authors of this study propose a design for a dual-arm robot with a biologically inspired four degree-of-freedom shoulder mechanism. This study analyzes the kinematic performance of the proposed design and compares it with that of a conventional dual-arm robot from the perspective of workspace and single-/dual-arm manipulability. The comparative analysis revealed that the proposed structure can significantly enhance single- and dual-arm kinematic performance in comparison with conventional dual-arm structures. This superior kinematic performance was verified through experiments, which showed that the proposed method required shorter settling time and trajectory-following performance than the conventional dual-arm robot.

Multi-cameras visual servoing for dual-arm coordinated manipulation

SUMMARYAlthough image-based visual servoing (IBVS) provides good performance in many dual-arm manipulation applications, it reveals some fatal limitations when dealing with a large position and orientation uncertainty. The object features may leave the camera's field of view, and the dual-arm robot may not converge to their goal configurations. In this paper, a novel vision-based control strategy is presented to resolve these limitations. A visual path planning method for dual-arm end-effector features is proposed to regulate the large initial poses to the pre-alignment poses. Then, the visual constraints between the position and orientation of two objects are established, and the sequenced subtasks are performed to attain the pose alignment of two objects by using a multi-tasks IBVS method. The proposed strategy has been implemented on a MOTOMAN robot to perform the alignment tasks of plug–socket and cup–lid, and results indicate that the plug and socket with the large initial pose errors 145.4 mm, 43.8○ (the average errors of three axes) are successfully aligned with the allowed pose alignment errors 3.1 mm, 1.1○, and the cup and lid with the large initial pose errors 131.7 mm, 20.4○ are aligned with the allowed pose alignment errors −2.7 mm, −0.8○.

IMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing

This report describes an intelligent and intuitive dual-arm robotic system for industrial human–robot collaboration which provides the basis for further work between DFKI (Robotics Innovation Center) and Volkswagen Group (Smart Production Lab) in the field of intuitive and safe collaborative robotics in manufacturing scenarios. The final robot demonstrator developed in a pilot project possesses multiple sensor modalities for environment monitoring and is equipped with the ability for online collision-free dual-arm manipulation in a shared human–robot workspace. Moreover, the robot can be controlled via simple human gestures. The capabilities of the robotic system were validated at a mockup of a gearbox assembly station at a Volkswagen factory.

Analysis and Design of the Dual Arm Manipulator for Rescue Robot

Analysis and Design of the Dual Arm Manipulator for Rescue Robot

Redundancy analysis of cooperative dual-arm manipulators

This paper presents the redundancy analysis of two cooperative manipulators, showing how they can be considered as a single redundant manipulator through the use of the relative Jacobian matrix. In this way, the kinematic redundancy can be resolved by applying the principal local optimization techniques used in the single manipulator case. We resolve the redundancy by using the Jacobian null space technique, which permits us to perform several tasks with different execution priority levels at the same time; this is a useful feature, especially when the manipulators are to be mounted on and cooperate with a mobile platform. As an illustrative example, we present a case study consisting of two planar manipulators mounted on a smart wheelchair, whose degrees of redundancy are employed to move an object along a pre-defined path, while avoiding an obstacle in the manipulator’s workspace at the same time.

Whole Body Control of a Dual-Arm Mobile Robot Using a Virtual Kinematic Chain

Dual-arm manipulators have more advanced manipulation abilities compared to single-arm manipulators and manipulators mounted on a mobile base have additional mobility and a larger workspace. Combining these advantages, mobile dual-arm robots are expected to perform a variety of tasks in the future. Kinematically, the configuration of two arms that branches from the mobile base results in a serial-to-parallel kinematic structure. In order to respond to external disturbances, this serial-to-parallel kinematic structure makes inverse kinematic computations non-trivial, as the motion of the base has to take the needs of both arms into account. Instead of using the dual-arm kinematics directly, we propose to use a virtual kinematic chain (VKC) to specify the common motion of the two arms. We formulate a constraint-based programming solution which consists of two parts. In the first part, we use an extended serial kinematic chain including the mobile base and the VKC to formulate constraints that realize the desired orientation and translation expressed in the world frame. In the second part, we use the resolved VKC motion to constrain the common motion of the two arms. In order to explore the redundancy of the two arms in an optimization framework, we also provide a VKC-oriented manipulability measure as well as its closed-form gradient. We verify the proposed approach with simulations and experiments that are performed on a PR2 robot, which has two 7 degrees of freedom (DoF) arms and a 3 DoF mobile base.

ロボットによるバラ積みピッキングのための視覚プランニング

This paper proposes a method for view planning to be used for randomized bin-picking. Assuming a 3D sensor attached at the wrist, we consider obtaining the pose of 3D sensor minimizing the occuluded area of the view captured during the previous picking trial. Then, by comparing the current and the previous view, we consider identifying the pose of object only where the difference between two view is large. The effectiveness of the proposed method is verified by experiment of using a dual-arm manipulator.

Constraint-Based and Sensorless Force Control With an Application to a Lightweight Dual-Arm Robot

The increasing demand for flexible robotized solutions in SMEs motivates the research on innovative and general control algorithms suitable for a large variety of applications. Particularly suited for this are constraint-based trajectory generation and control methods, which have been deeply investigated in the last decade. In this letter, we present an application of such a control framework for sensorless execution of a force control task as applied to a dual-arm redundant manipulator. Experiments and simulations confirm the effectiveness of the approach, even in the case of parametric uncertainties.

Coordinated Resolved Motion Control of Dual-Arm Manipulators with Closed Chain

When applied to some tasks, such as payload handling, assembling, repairing and so on, the two arms of a humanoid robot will form a closed kinematic chain. It makes the motion planning and control for dual-arm coordination very complex and difficult. In this paper, we present three types of resolved motion control methods for a humanoid robot during coordinated manipulation. They are, respectively, position-level, velocity-level and acceleration-level resolved motion control methods. The desired pose, velocity and acceleration of each end-effector are then resolved according to the desired motion of the payload and the constraints on the closed-chain system without consideration of the internal force. Corresponding to the three cases above, the joint variables of each arm are then calculated using the inverse kinematic equations, at position-level, velocity-level or acceleration-level. Finally, a dynamic modelling and simulation platform is established based on ADAMS and Matlab software. The proposed methods are verified by typical cases. The simulation results show that the proposed control strategy can realize the dual-arm coordinated operation and the internal force of the closed chain during the operation is controlled in a reasonable range at the same time.

Point Cloud Based Bin Picking: Object Recognition and Pose Estimation Using Region Growing Segmentation Algorithm

Presented paper describes experimental bin picking using Kinect sensor, region-growing algorithm, latest ROS-Industrial drivers and dual arm manipulator Motoman SDA10f.As well known if manipulation with objects of regular shapes by suction cup is required, it is sufficient to estimate only 5DoF for successful pick. In such a case simpler region growing algorithm may be used instead of complicated 3D object recognition and pose estimation techniques, resulting in shorter processing time and decrease of computational load. Experimental setup for such a scenario, manipulated objects and results using region growing segmentation algorithm are explained in detail. Video and link to open-source code of described application are provided as well.

The composite hierarchical control of multi-link multi-DOF space manipulator based on UDE and improved sliding mode control

The problem of manipulating objects cooperatively for multi-link multi-degree of freedom space manipulator is very challenging because of the multisource disturbances, including nonlinear coupling, model uncertainties, and external disturbances. To solve this issue, a composite hierarchical control strategy is proposed, which has two layers. In inner layer, uncertainty and disturbance estimator (UDE) is employed to estimate the composite uncertainty that comprises the effect of multisource disturbances and compensate for it, producing the decoupled system. Furthermore, taking the error in UDE estimation into consideration, the chattering–eliminating sliding mode control is designed to suppress it in the outer layer. However, the obtained controller requires the measurement of joint velocities apart from joint positions. To address this issue, a robust velocity observer that employs the UDE-estimated uncertainty is proposed. The notable feature of the proposed design is that it requires neither accurate plant model nor any information about the uncertainty. Also, the design requires only joint position measurements for its implementation. Finally, to demonstrate the effectiveness of the composite hierarchical controller, the simulations of a planar dual-arm manipulator system and the comparisons of the proposed method with the other existing designs are presented.