Robotica最新文献

Collaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

As the proportion of the elderly population in the USA expands, so will the demand for rehabilitation and social care, which play an important role in maintaining function and mediating motor and cognitive decline in older adults. The use of social robotics and telemedicine are each potential solutions but each have limitations. To address challenges with classical telemedicine for rehabilitation, we propose to use a social robot-augmented telepresence (SRAT), Flo, which was deployed for long-term use in a community-based rehabilitation facility catering to older adults. Our goals were to explore how clinicians and patients would use and respond to the robot during rehab interactions. In this pilot study, three clinicians were recruited and asked to rate usability after receiving training for operating the robot and two of them conducted multiple rehab interactions with their patients using the robot (eleven patients with cognitive impairment and/or motor impairment and 23 rehab sessions delivered via SRAT in total). We report on the experience of both therapists and patients after the interactions.

In view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Closed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

This paper systematically investigates a soft-rigid hybrid pneumatic actuator (SRHPA), which consists of a rigid-foldable twisting skeleton capable of a large range of helical motion and a soft bellows muscle with high linear driving force. Considering the unique varying-pitch helical motion of the foldable skeleton, the analytical model mapping the input force generated by the bellows muscle and output forces of the actuator is revealed and verified with a simulation of the force analysis. Prototypes of the actuator are developed by fabricating the twisting skeleton with multilayered aluminum composite panels and 3D-printing the bellows muscle with thermoplastic polyurethane (TPU) 95A filament. The static and dynamic performances of the prototypes are tested to validate the analytical modeling of output forces. Using the actuator as a module, a novel bipedal inchworm robot with four modules is developed and tested to demonstrate its adaptability in confined space by switching between the going-straight, the turning-around, and the rotating gaits. The hybrid actuator and the inchworm robot with zero onboard electronics have the potential to be deployed in extreme environments where pneumatically actuated systems are preferred over electrical machines and drives, such as in nuclear and explosive environments.

Robots with multi-sensors always have a problem of weak pairing among different modals of the collected information produced by multi-sensors, which leads to a bad perception performance during robot interaction. To solve this problem, this paper proposes a Force Vision Sight (FVSight) sensor, which utilizes a distributed flexible tactile sensing array integrated with a vision unit. This innovative approach aims to enhance the overall perceptual capabilities for object recognition. The core idea is using one perceptual layer to trigger both tactile images and force-tactile arrays. It allows the two heterogeneous tactile modal information to be consistent in the temporal and spatial dimensions, thus solving the problem of weak pairing between visual and tactile data. Two experiments are specially designed, namely object classification and slip detection. A dataset containing 27 objects with deep presses and shallow presses is collected for classification, and then 20 slip experiments on three objects are conducted. The determination of slip and stationary state is accurately obtained by covariance operation on the tactile data. The experimental results show the reliability of generated multimodal data and the effectiveness of our proposed FVSight sensor.

Continuum robot has become a research hotspot due to its excellent dexterity, flexibility and applicability to constrained environments. However, the effective, secure and accurate path planning for the continuum robot remains a challenging issue, for that it is difficult to choose a suitable inverse kinematics solution due to its redundancy in the confined environment. This paper presents a collision-free path planning method based on the improved artificial potential field (APF) for the cable-driven continuum robot, in which the beetle antennae search algorithm is adopted to deal with the optimal problem of APF without the necessary for velocity kinematics. In addition, the local optimum problem of traditional APF is solved by the randomness of the antennae’s direction vector which can make the algorithm easily jump out of local minima. The simulation and experimental results verify the efficiency of the proposed path planning method.