This paper presents an approach to haptically controlling an industrial robot without using any external sensors for human-robot collaborative assembly. The sensorless haptic control approach is enabled by the dynamic models of the robot where only joint angles and joint torques are measurable. Accurate dynamic models of the robot in the presliding and sliding regimes are developed to estimate the external forces/torques, where the friction model is also explored. The estimated external force applied to the robot by an operator is converted to the reference position and speed of the robot by an admittance controller. In this research, adaptive admittance control is adopted to support human-robot collaborative assembly, naturally and easily, with accurate positioning and control for smooth movement. Moreover, torque-based commands are used to control the robot’s assembly operations. Finally, the proposed approach is validated by a case study on assisting an operator during the collaborative assembly of a car engine.

In human–robot collaborative assembly, robots are often required to dynamically changetheir preplanned tasks to collaborate with human operators in close proximity. One essential requirement of such an environment is enhanced flexibility and adaptability, as well asreduced effort on the conventional (re)programming of robots, especially for complexassembly tasks. However, the robots used today are controlled by rigid native codes thatcannot support efficient human–robot collaboration. To solve such challenges, thisarticle presents a novel function block-enabled multimodal control approach for symbiotichuman–robot collaborative assembly. Within the context, event-driven function blocks asreusable functional modules embedded with smart algorithms are used for the encapsulation of assembly feature-based tasks/processes and control commands that are transferredto the controller of robots for execution. Then, multimodal control commands in the form ofsensorless haptics, gestures, and voices serve as the inputs of the function blocks to triggertask execution and human-centered robot control within a safe human–robot collaborativeenvironment. Finally, the performed processes of the method are experimentally validatedby a case study in an assembly work cell on assisting the operator during the collaborativeassembly. This unique combination facilitates programming-free robot control and theimplementation of the multimodal symbiotic human–robot collaborative assembly withthe enhanced adaptability and flexibility.

As an emerging communication modality, brainwaves can be used to control robots for seamless assembly, especially in noisy environments where voice recognition is not reliable or when an operator is occupied with other tasks and unable to make gestures. This paper investigates human-robot collaborative assembly based on function blocks and driven by brainwaves. Using wavelet transform, brainwaves measured by EEG sensors are converted to time-frequency images and subsequently classified by a convolutional neural network (CNN) as commands to trigger a network of function blocks for assembly actions. The effectiveness of the system is experimentally validated through an engine-assembly case study.

Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.

In human-robot collaborative assembly, leveraging multimodal commands for intuitive robot control remains a challenge from command translation to efficient collaborative operations. This article investigates multimodal data-driven robot control for human-robot collaborative assembly. Leveraging function blocks, a programming-free human-robot interface is designed to fuse multimodal human commands that accurately trigger defined robot control modalities. Deep learning is explored to develop a command classification system for low-latency and high-accuracy robot control, in which a spatial-temporal graph convolutional network is developed for a reliable and accurate translation of brainwave command phrases into robot commands. Then, multimodal data-driven high-level robot control during assembly is facilitated by the use of event-driven function blocks. The high-level commands serve as triggering events to algorithms execution of fine robot manipulation and assembly feature-based collaborative assembly. Finally, a partial car engine assembly deployed to a robot team is chosen as a case study to demonstrate the effectiveness of the developed system.