Robotica最新文献

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.

Flexible endoscopy is the gold standard modality for diagnosis and therapeutic intervention of various colorectal conditions. A high bar is currently set for any new technology to replace the current modern colonoscope, but limitations do exist. For a robotic system to gain acceptance, ideally a clear advantage over the established standard needs to be demonstrated. The application of robotic technology inspired by locomotion observed in animals has been demonstrated in many fields including colonoscopy. A myriad of novel concepts has been proposed, which can overcome the anatomical and technical challenges.

This review discusses novel and innovative examples of bioinspired robotic locomotion in the colon with a detailed comparison of studies alongside separating the discussion by animal sections of insect, marine and reptile locomotion. We also discuss the current advantages and challenges a bioinspired robot will bring to the colon.

Bioinspired robotics in the colon is an exciting field of research with the potential to improve upon current existing high standards of practice in colonoscopy. By addressing areas that the conventional colonoscope is weaker in, studies are demonstrating improvement upon current limitations of standard practice and providing an insight into new methods of engineering and fabrication. Focus on the technological, mechanical and regulatory barriers is key to achieve acceptance into standard practice and will allow the aspiration of a safe, low discomfort, low cost and potentially fully autonomous robotic colonoscope to be not too distant in the future of colonoscopy.

Hyper-redundant manipulators are produced by cascading several mechanisms on top of each other as modules. The discrete actuation makes their control easier because discrete actuators usually do not need any feedback to control. So far, several methods have been proposed to solve the inverse kinematic problem of discretely actuated, hyper-redundant manipulators. The two-by-two searching method is better than the other methods in terms of CPU time and error. In this article, the mentioned method is generalized by choosing an arbitrary number of modules as pending modules in each step of the solution instead of the necessary two. For validation, the proposed method is compared with nine meta-heuristic searching algorithms: simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization, gray wolf optimizer, stochastic fractal search, whale optimization algorithm, Giza pyramid construction, and flying fox optimization. Furthermore, the effect of the number of pending modules on CPU time and error is investigated. All the numerical problems have been solved for two case studies, one is planar and the other is spatial.

This article designs a robotic Chinese character writing system that can resist random human interference. Firstly, an innovative stroke extraction method of Chinese characters was devised. A basic Chinese character stroke extraction method based on cumulative direction vectors is used to extract the components that make up the strokes of Chinese characters. The components are then stitched together into strokes based on the sequential base stroke joining method. To enable the robot to imitate handwriting Chinese character skills, we utilised stroke information as the demonstration and modelled the skills using dynamic movement primitives (DMPs). To suppress random human interference, this article combines improved DMPs and conductance control to adjust robot trajectories based on real-time visual measurements. The experimental results show that the proposed method can accurately extract the strokes of most Chinese characters. The designed trajectory adjustment method offers better smoothness and robustness than direct rotating and translating curves. The robot is able to adjust its posture and trajectory in real time to eliminate the negative impacts of human interference.

This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.