Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

In this paper, a novel adaptive tracking control technique is developed for multiple-input-multiple-output (MIMO) nonlinear systems with model uncertainty and under output constraints occurring in a limited time interval (OCOLT). The OCOLT, which is a type of constraints occurring some time after (rather than from the beginning of) system operation and with limited duration, can be found in many practical systems and has not been effectively addressed in the literature up to now. By designing a new shift function and with the aid of barrier functions, the constrained system is transformed into an unconstrained one. A new disturbance observer then is designed to estimate unknown disturbances. The resultant control method is able to not only address the OCOLT, but also deal with the constraint-free output case and the infinite-time constrained one (i.e., constraints existing for all $t\geq 0$) without the need to revise the control structure. Finally, The effectiveness of the proposed technique is assessed in simulation.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems’ uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.

In this article, neural network (NN)-based sliding mode control schemes are proposed for an n-link robotic manipulator with system uncertainties, input deadzone, and external perturbations. A novel error-shifting function is proposed to release initial conditions. NNs are employed to approximate the unknown parameters of both system uncertainties and input deadzone. To update the sliding mode scheme, two advanced sliding mode surfaces with error-shifting function and barrier function are proposed to reduce the dependency of prior information and to realize a finite time convergence result, collectively. It should be pointed out that the proposed methods do not require initial states to satisfy the prescribed constraint caused by the barrier function and can be applied under unknown initial conditions. Furthermore, finite-time convergence for both tracking errors and NN weights is guaranteed. The effectiveness of the proposed schemes is demonstrated by simulation and experiments on the KINOVA robot.

In this article, we focus on human-to-cobot dual-arm handover operations for large box-type objects. The efficiency of handover operations should be ensured and the naturalness as if the handover is going on between two humans. First of all, we study the human–human dual-arm large box-type object natural handover process to guide this research. Then, for efficiency, we combine the probabilistic approach with the online learning algorithm to predict the beginning of the handover task and handover positions. The online updating probabilistic models can deal with not only human givers’ regular motion patterns but also their irregular motion patterns. Then, to guarantee that human givers can perform handover operations naturally, we apply the probabilistic robot skill learning method kernelized movement primitives (KMPs) to adapt the learned receiving skills and fulfill some constraints for safety based on online predicted results. Furthermore, we give special attention to the dual-arm grasp strategy and control design to guarantee a stable grasp. In addition, we equip this handover system on a Baxter cobot and extend its grippers to make it more suitable for dual-arm handover operations. The experimental results show that the proposed handover system can solve human-to-cobot dual-arm handover operations for large box-type objects naturally and efficiently.