Robotica最新文献

Motivated by practical applications of inspection and maintenance, we have developed a wall-climbing robot with passive compliant mechanisms that can autonomously adapt to curved surfaces. At first, this paper presents two failure modes of the traditional wall-climbing robot on the variable curvature wall surface and further introduces the designed passive compliant wall-climbing robot in detail. Then, the motion mechanism of the passive compliant wall-climbing robot on the curved surface is analyzed from stable adsorption conditions, parameter design process, and force analysis. At last, a series of experiments have been carried out on load capability and curved surface adaptability based on a developed principle prototype. The experimental results indicated that the wall-climbing robot with passive compliant mechanisms can effectively promote both adsorption stability and adaptability to variable curvatures.

Aimed at the challenges of wide-angle mobile robot visual perception for diverse field applications, we present the spherical robot visual system that uses a 360° field of view (FOV) for realizing real-time object detection. The spherical robot image acquisition system model is developed with optimal parameters, including camera spacing, camera axis angle, and the distance of the target image plane. Two 180$^{circ}$-wide panoramic FOVs, front and rear view, are formed using four on-board cameras. The speed of the SURF algorithm is increased for feature extraction and matching. For seamless fusion of the images, an improved fade-in and fade-out algorithm is used, which not only improves the seam quality but also improves object detection performance. The speed of the dynamic image stitching is significantly enhanced by using a cache-based sequential image fusion method. On top of the acquired panoramic wide FOVs, the YOLO algorithm is used for real-time object detection. The panoramic visual system for the spherical robot is then tested in real time, which outputs panoramic views of the scene at an average frame rate of 21.69 fps and panoramic views with object detection at an average of 15.39 fps.

Physically compliant actuator brings significant benefits to robots in terms of environmental adaptability, human–robot interaction, and energy efficiency as the introduction of the inherent compliance. However, this inherent compliance also limits the force and position control performance of the actuator system due to the induced oscillations and decreased mechanical bandwidth. To solve this problem, we first investigate the dynamic effects of implementing variable physical damping into a compliant actuator. Following this, we propose a structural scheme that integrates a variable damping element in parallel to a conventional series elastic actuator. A damping regulation algorithm is then developed for the parallel spring-damping actuator (PSDA) to tune the dynamic performance of the system while remaining sufficient compliance. Experimental results show that the PSDA offers better stability and dynamic capability in the force and position control by generating appropriate damping levels.

A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

SLAM Benchmark plays a pivotal role in the field by providing a common ground for performance evaluation. In this paper, a novel methodology of simultaneous localization and mapping benchmark and map accuracy improvement (SLAMB&MAI) is introduced. It can objectively evaluate errors of localization and mapping, and further improve map accuracy by utilizing evaluation results as feedback. The proposed benchmark transforms all elements into a global frame and measures the errors between them. The comprehensiveness consists in the benchmark of both localization and mapping, and the objectivity consists in the consideration of the correlation between localization and mapping by the preservation of the original pose relations between all reference frames. The map accuracy improvement is realized by first obtaining the optimization that minimizes the errors between the estimated trajectory and ground truth trajectory and then applying it to the estimated map. The experimental results showed that the map accuracy can be improved by an average of 15%. The optimization that yields minimal localization errors is obtained by the proposed Centre Point Registration-Iterative Closest Point (CPR-ICP). This proposed Iterative Closest Point (ICP) variant pre-aligns two point clouds by their centroids and least square planes and then uses traditional ICP to minimize the error between them. The experimental results showed that CPR-ICP outperformed traditional ICP, especially in cases involving large-scale environments. To the extent of our knowledge, this is the first work that can not only objectively benchmark both localization and mapping but also revise the estimated map and increase its accuracy, which provides insights into the acquisition of ground truth map and robot navigation.

Currently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process. In this paper, a hybrid pouring robot is proposed to solve these limitations, and a vision-based hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35–40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters. Consequently, the designed pouring robot will significantly enhance the automation level of the casting industry.

In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.