Collaborative robot manipulators are being used to assist human workers performing physically intensive tasks. The finite reach of the manipulator often results in the robot being operated in proximity to kinematic singularity, negatively affecting the stability and performance of operation. Methods of controlling manipulators in the proximity of singularities and mitigating their effects on performance have been widely researched. However, little attention has been given to developing suitable methods for handling singularities specifically for applications where the manipulator physically interacts with a human operator. In these applications additional factors such as human comfort and interaction experience need to be considered. This work presents a framework for handling robotic singularities developed with the human operator in mind. Singularity robustness is achieved using a novel approach to dampen motion of the manipulator along singular directions. An exponential scaling shapes the damping to create a smooth behavior beneficial for physical human–robot interaction. The damping is applied asymmetrically depending on if the robot is heading towards or away from singular configurations, improving the interaction experience for the human operator. Additionally, bounded virtual forces are used to subtly guide the operator away from singular configurations. The proposed framework is validated in simulation and tested on an industrial manipulator.

Trust is key to achieving successful Human-Robot Interaction (HRI). Besides trust of the human co-worker in the robot, trust of the robot in its human co-worker should also be considered. A computational model of a robot’s trust in its human co-worker for physical human-robot collaboration (pHRC) is proposed. The trust model is a function of the human co-worker’s performance which can be characterized by factors including safety, robot singularity, smoothness, physical performance and cognitive performance. Experiments with a collaborative robot are conducted to verify the developed trust model.

Laminar jamming (LJ) is a method to achieve variable stiffness in robotics that has attracted notable attention because of its simple working principle and potential high stiffness variation. This article reviews the lock/unlock mechanisms of LJ structures. The application of these mechanisms in robotics is discussed, including grippers, continuum robots, wearable robots, robot arms, and more. Furthermore, the performance and limitations of the mechanisms to vary the stiffness of LJ are qualitatively and quantitatively analyzed. This performance analysis focuses mainly on the potential of LJ mechanisms to be applied in robot arms with variable stiffness and their potential to attenuate the impact between human beings and robot arms. The modeling of LJ through analytical and finite element methods is described, and their evolution towards design methodologies is discussed. To conclude, the directions and recommendations that should be followed in research on LJ are discussed. These include the improvement of existing lock/unlock mechanisms, the development of new lock/unlock mechanisms, and the development of more control algorithms for robot arms that incorporate LJ structures.

A model-based assistance-as-needed paradigm has been developed to govern the assistance provided by an assistive robot to its operator. This paradigm has advantages over existing methods of providing assistance-as-needed for applications such as robotic rehabilitation. However, implementation of the model-based paradigm requires a control scheme to be developed which controls the robot so as to provide the assistance calculated by the model-based paradigm to its operator. In this paper an admittance control scheme for providing model-based assistance-as-needed is presented. It is developed considering its suitability for human-robot interaction, and its role within the model-based assistance-as-needed framework. Results from the control implemented on an example robot showed it is capable of providing the operator with the desired level of assistance as governed by the model-based paradigm. This is an essential requirement for the paradigm to be capable of providing efficacious assistance-as-needed in applications such as robotic rehabilitation.

With the increasing number of robots being developed to physically assist humans in tasks such as rehabilitation and assistive living, more intelligent and personalized control systems are desired. In this paper we propose the use of a musculoskeletal model to estimate the strength of the user, from which information can be utilized to improve control schemes in which robots physically assist humans. An optimization model is developed utilizing a musculoskeletal model to estimate human strength in a specified dynamic state. Results of this optimization as well as methods of using it to observe muscle-based weaknesses in task space are presented. Lastly potential methods and problems in incorporating this model into a robot control system are discussed.

Autonomous Underwater Vehicle-Manipulator Systems (AUVMS) operating in shallow waters or near-surface environments may be exposed to wave disturbances which will cause undesired motion of the end effector. This paper presents a method to maneuver the manipulator joints and counteract undesired motion of the vehicle body, in order to maintain a steady end-effector position in the inertial frame. An Autoregressive (AR) model is used to predict vehicle motion, and then combined with Model Predictive Control (MPC) to optimize joint motion. Simulation was conducted using real data to verify the efficacy of this method.