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This paper aims to propose cooperative control strategies for dual-arm robots in different human–robot collaborative tasks in assembly processes. The authors set three different regions where robot performs different collaborative ways: “teleoperate” region, “co-carry” region and “assembly” region. Human holds the “master” arm of dual-arm robot to operate the other “follower” arm by our proposed controller in “teleoperation” region. Limited by the human arm length, “follower” arm is teleoperated by human to carry the distant object. In the “co-carry” region, “master” arm and “follower” arm cooperatively carry the object to the region close to the human. In “assembly” region, “follower” arm is used for fixing the object and “master” arm coupled with human is used for assembly.

A human moving target estimated method is proposed for decreasing efforts for human to move “master” arm, radial basis functions neural networks are used to compensate for uncertainties in dynamics of both arms. Force feedback is designed in “master” arm controller for human to perceive the movement of “follower” arm. Experimental results on Baxter robot platform show the effectiveness of this proposed method.

Experimental results on Baxter robot platform show the effectiveness of our proposed methods. Different human-robot collaborative tasks in assembly processes are performed successfully under our cooperative control strategies for dual-arm robots.

In this paper, cooperative control strategies for dual-arm robots have been proposed in different human–robot collaborative tasks in assembly processes. Three different regions where robot performs different collaborative ways are set: “teleoperation” region, “co-carry” region and “assembly” region.

Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment.

The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment.

The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment.

This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon.

A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy.

The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy.

The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.

To present an open architecture for real‐time sensory feedback control of a dual‐arm industrial robotic cell. The setup is composed of two industrial robot manipulators equipped with force/torque sensors and pneumatic grippers, a vision system and a belt conveyor.

The original industrial robot controllers have been replaced by a single PC with software running under a real‐time variant of the Linux operative system.

The new control architecture allows advanced control schemes to be developed and tested for the single robots and for the dual‐arm robotic cell, including force control and visual servoing tasks.

An advanced user interface and a simulation environment have been developed, which permit fast, safe and reliable prototyping of planning and control algorithms.

The objective of this work was to demonstrate a novel approach to human machine interaction that seamlessly uses teleoperation and automation in a complex environment.

This work leverages developments in the area of operational software Operational Software Components for Advanced Robotics (OSCAR), decision making, human‐machine interface, and motion planning. This demonstration uses a 17 degrees‐of‐freedom (DOF) dual arm robot that is equipped with modern tool changers, crash protectors, force‐torque sensors and electrical and pneumatic power at the tools. Four different end‐effector tools are also provided. These are electric grippers, electric rotary saw, electric drill, and a pneumatic spray gun. The system can be used both in teleoperation and automation mode. In teleoperation mode, the user has a choice of five different input devices. These are computer keyboard, spaceball and spacemouse, RSI manual controller and kraft force feedback controller. Automation is performed using a novel graphical user interface with 3D graphics used for previewing and verifying manipulator motion. Automation tasks that are demonstrated include automatic grasping, sawing, drilling, spray painting, point‐to‐point motion, and teaching. The controller for the dual arm system is developed using OSCAR and supports a variety of decision‐making algorithms and obstacle avoidance. The integration of this controller with the input devices and human machine interface is done using a novel protocol that is based on Extensible Markup Language (XML) for maximum reuse and distributed integration. This protocol is further based on a well‐defined and scalable XML schema that can be easily extended as controller functionality is changed and/or additional input devices are added.

It is necessary to combine automation with teleoperation to reduce worker fatigue and also provide higher value robotic functions. This is possible as most remote tasks can be broken down into structured and unstructured components. On the integration front, we see XML‐based integration providing a loosely coupled system that can make interoperability between various robot systems possible. For end‐effector tooling, it is better to have special purpose tools that can be switched out versus the use of a general purpose tool such as a robotic hand.

This research was done in a laboratory environment, and as such, its application in the field will require partnering with a commercial entity. Force‐feedback on manual controllers during teleoperation was not very effective. In fact, providing visual queues to the operator about the forces were a better guide to the operator.

The software for this work provides obstacle avoidance capability. The obstacle avoidance is based on a known world model that is derived from a CAD environment. In reality, this model will have to be sensed in real‐time, and decoded into a geometric model. Significant work in this area needs to be done.

The software developed for this work was based on the OSCAR software framework. This is a unique framework that at its core uses performance criteria to control the behavior of the robot during teleoperation and automation. The value of this work is that it shows as completely feasible the control of a 17 DOF dual arm system using the latest integration technologies (such as XML), integrated simulation, multiple tools and multiple input devices. It also shows that all these choices can be provided to an operator through a single user interface.

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.

In the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.

The system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.

The effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.

The P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

Describes a dual‐arm mobile manipulator that can autonomously scan natural terrain using a typical handheld landmine detector in a manner similar to a human operator.

Presents a terrain‐scanning robot that consists of two articulated arms mounted on an off‐road remotely operated vehicle. One arm carries a laser and four ultrasonic rangefinders to build a terrain map. The map is used in real time to generate an obstacle‐free path for the second arm that manipulates the landmine detector autonomously. The arms are mounted on the vehicle that is controlled by an operator from a safe distance. Motion planning and control of the robot is carried out using an embedded computer that is linked to a host computer to transmit the detector data and operator commands.

Finds that the terrain‐scanning robot can effectively manipulate a relatively large landmine detector on rugged terrain with undulations and obstacles.

Proposes real‐time motion planning that may be equally applicable to other mobile manipulators.

Provides a technology that together with state‐of‐the‐art landmine sensors will offer a safe solution for detecting hidden landmines and clearing them from the postwar countries.

Introduces the concept of a dual‐arm mobile terrain scanning robot for landmine detection in off‐road missions and civilian areas where truck‐mounted detectors are inefficient.