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Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, thus increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts for virtual reality applications, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection based on hand motion and gaze to improve the time for the robot and human security in a virtual environment. We then studied the effect of prediction. Results from comparisons show that the prediction models improved the robot time by 3% and safety by 17%. When used alongside gaze, prediction with Gaussian process models resulted in an improvement of the robot time by 2% and the safety by 13%.

In this paper, we propose a novel system in which a human and a device work in unison on a single task with the aim to simultaneously support human cognitive and physical functions. In this system, the cognitive and physical burdens of the task are shared between the human and device, with the human understanding the entire task and performing rough upstream actions. Additionally, the device understands the surrounding environment, objects, and the human and executes detailed downstream actions. A prototype device was developed to assist in solving silhouette puzzles that abstract and generalize cognitive and physical tasks. The prototype is a pen-shaped device held in the user’s hand. The device picks up puzzle pieces and rotates them to the appropriate position. A display is placed in front of the user and provides feedback information for communication between the system and user. Experiments showed that the proposed device reduced the time required to solve these puzzles. Furthermore, the work was found to be accurate with no errors, and the time required was stable with no irregularities. The subjective evaluation results using the NASA Task Load Index showed that the proposed system can decrease mental load and burden. Moreover, it was confirmed that the human and device must communicate with each other to work simultaneously on one task. In conclusion, the proposed device can support human cognitive and physical functions simultaneously.

Cyber-Physical Systems (CPSs) are the core of Industry 4.0 applications, integrating advanced technologies such as sensing, data analytics, and artificial intelligence. This kind of combination typically consists of networked sensors and decision-making processes in which sensor-generated data drive the control decisions. Hence, the trustworthiness of the sensors is essential to guarantee performance, safety and quality during operation. Formal model verification techniques are a valuable tool allowing strong reasoning about the high-level design of CPSs. However, the uncertainty exhibited by the underlying sensor networks is often ignored. Manufacturing processes typically involve composition of various modular CPSs that work as a whole, such as multiple Collaborative Robots (cobots) working together as a production line, which improves the flexibility and resilience of the production process. It is still challenging to verify this class of compositional process while also considering uncertainty. We propose a novel verification framework for modular CPSs that combines sensor-level data-driven fault detection and system-level model-driven probabilistic model checking. The resulting framework can rigorously quantify sensor readings’ trustworthiness, enabling formal reasoning for system failure prediction and reliability analysis. We validated our approach on a cobots-based manufacturing process.

The paper presents a new version of the existing elastic band algorithm used for path finding, with application in the field of collaborative robotics and point-to-point movements. The algorithm places control points on the path and dynamically modifies the position of these control points in reaction to any obstacles located or moving in the workspace. The control points are updated in the robot space (TCP space), obstacles are represented by a set of grid-aligned voxels acquired by a camera system. Repulsive forces are created between the obstacles and the robot body (represented by a set of points covering evenly the surface of individual links) and transferred to the locations of the elastic band control points. The method is computationally effective and provides a smooth and length-optimal path while considering collisions in a more accurate manner than traditional usage of simple bounding volumes. The dynamic iterative updating of elastic band provides a clear principle for modification of the trajectory even close to the actual location of the robot as it is following the trajectory. The algorithm is verified on a set of practical experiments made on a physical UR3 robot with simulated obstacles, and the results are also compared to other commonly used methods for dynamic obstacle avoidance.

The primary goal of this project was to develop a general identification method via software that can be applied to collaborative robots. To achieve this, the collaborative ultralight robots Kinova Gen2 and Kuka LWR4+ with seven degrees of freedom (M-DOF) were used. Specifically, the “recursive Newton-Euler” formulation was used to provide a set of parameters that could describe the body structure and to create a general symbolic representation for collaborative robots. For parameter estimation, the least squares method was used. In addition, trajectories generated with random numbers typically do not produce consistent results; thus, verified trajectories were used. To verify trajectories, real robots were simulated with V-Rep before being executed. When untested trajectories are first tested on robots, undesirable results may occur. This method was convenient for parameter estimation and robot health; saves time; and increases the consistency of results. Algorithms were coded in MATLAB and ROS packages via Python. MATLAB, ROS, and V-Rep worked together in the Ubuntu operating system. The identification methods were modeled, implemented, tested, and validated successfully, and the results for both robots are reported in this article.

In this paper, a new comprehensive dynamic friction model for a collaborative industrial robot joint that considers the velocity, temperature, and load torque is proposed. The variation of load-dependent friction among the four-quadrant operation depending on the sign of load-torque and speed is studied. The new model’s passivity property is analyzed to obtain a physically meaningful and experimentally identified friction model. A sufficient condition is presented in terms of a simple algebraic inequality involving the parameters of the model. The model parameter identification procedure and validation of model effectiveness are demonstrated experimentally on a commercial collaborative robot manipulator. Moreover, the proposed friction model’s benefits are demonstrated in two different robot applications: friction compensation and direct teaching (smooth lead-through programming) applications. Significant tracking performance improvement in the root-mean-square errors up to 76% was achieved with the proposed friction model compared to the uncompensated cases in the friction compensation application. In the direct teaching application, the new model, which precisely estimates joint friction, results in a significant decrease in interaction forces up to 66%. These experimental results validate the performance of the proposed friction model.

Smart factories should be able to respond to catastrophic situations proactively, such as recalls caused by production line disruptions and equipment failures. Therefore, the necessity for predictive maintenance technology, such as fault detection or diagnosis of equipment has increased in recent years. In particular, predicting the faults of collaborative robots is becoming increasingly crucial because smart factories pursue efficient collaboration between humans and devices. However, collaborative robots have the characteristic of executing programmable motions designed by an operator, rather than performing fixed tasks. If existing fault diagnosis methods are applied to non–fixed programmable motions, problems arise in terms of setting absolute criteria for fault analysis, interpreting the meanings of detected values, and fault tracking or fault cause analysis. Therefore, we propose a method of programmable motion-fault detection by analyzing motion residuals to solve the three problems mentioned above. The proposed method can expand the fault diagnostic range of collaborative robots.

This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system.

The flexibility of the robot assembly process is critical, and a robot assembly system that is not flexible may damage the workpieces. Most researchers make the assembly process flexible by installing a six-dimensional force/torque sensor at the end of robots, but doing so will result in an increase in the costs of the robotic assembly system. To this end, this paper proposes an external force/torque calculation algorithm based on dynamic model identification to replace the six-dimensional force/torque sensor; the algorithm can reduce the costs while achieving a flexible assembly. In this paper, the impedance model of the environment and the dynamic model of the robot with friction are unified. Based on the unified model, the virtual contact surface is proposed to optimize the assembly. To ensure the accuracy of the assembly, the compliant control method of this paper uses the PD-based position control as the control inner loop and the impedance control as the control outer loop. To verify the accuracy of the compliant control method, a 6-DOF series collaborative robot which is developed in our laboratory is used to complete the peg-in-hole assembly experiment. The experimental results show that the algorithm has good flexibility and positional accuracy.