Cognitive Robotics最新文献

Rolling machinery is ubiquitous in power transmission and transformation equipment, but it suffers from severe faults during long-term running. Automatic fault diagnosis plays an important role in the production safety of power equipment. This paper proposes a novel cross-domain co-attention network (CDCAN) for fault diagnosis of rolling machinery. Multiscale features cross time and frequency domains are respectively extracted from raw vibration signal, which are then fused with a co-attention mechanism. This architecture fuses layer-wise activations to enable CDCAN to fully learn the shared representation with consistency across time and frequency domains. This characteristic helps CDCAN provide more faithful diagnoses than state-of-the-art methods. Experiments on bearing and gearbox datasets are conducted to evaluate the fault-diagnosis performance. Extensive experimental results and comprehensive analysis demonstrate the superiority of the proposed CDCAN in term of diagnosis correctness and adaptability.

This study presents the method for optimal error tracking in position control for a servo pneumatic actuated robot grasper system using a new adjustable convergence rate prescribed performance control (ACR-PPC). It focuses on improving the feedback controller and the fractional-order proportional-integral-derivative (FOPID) controller used for the position control of each robot's finger. Multiple features were considered such as tracking error, rising time, faster transient response with finite-time convergence, oscillation reduction, and pressure stabilization in the pneumatic system. Experiments were conducted using a single finger of a tri-finger pneumatic gripper (TPG) robot, actuated by a single proportional valve with a double-acting cylinder (PPVDC). Two types of input trajectories were tested: step and sine wave inputs, which are common and critical for pneumatic systems. The results show that the proposed method eliminates oscillation and achieves high tracking performance within the prescribed bounds and minimal overshoot as well. The oscillation was suppressed with minimal overshoot and fast response was achieved by tuning the formulated adjustable prescribe performance function, thus improving the rising time response without significant loss of performance.

The development of imaging technology has allowed people to move beyond the black-and-white era and into the age of color. However, preserved black-and-white historical footage remains a precious memory for people. We propose a coloring method for historical videos that combines historical image coloring methods with temporal consistency methods, thus achieving color editing for historical videos. The temporal consistency technique uses deep video priors to model the video structure and effectively ensure smoothness between frames after video color editing, even with a small amount of training data. Meanwhile, we have collected a historical video dataset named MHMD-Video, which facilitates further research on colorization of historical videos for researchers. Finally, we demonstrate the effectiveness of the proposed method through objective and subjective evaluation.

The proposed research deals with selection of particle swarm optimization (PSO) algorithm parameters for missile gliding trajectory optimization relying on Taguchi design of experiments, analysis of variance (ANOVA) and artificial neural networks (ANN). Population size, inertial weight and acceleration coefficients of PSO were chosen for the present study. The experiments have been designed as per Taguchi's design of experiments using L₂₅ orthogonal array for selection of better PSO parameters. Missile gliding trajectory is optimized by discretizing angle of attack as control parameter, consequent conversion of optimal control problem to nonlinear programming problem (NLP) and finally solving the problem using PSO with optimized parameters to obtain optimum angle of attack and realization of maximum gliding range. Simulation results portrayed that the gliding range is maximized and missile glide distance is enhanced compared to earlier experiments. The efficiency of proposed approach was verified via different test scenarios.

Some problems still exist in health management and application such as insufficient data, limited technology, and lack of professional evaluation methods by physicians with medical theory. In this study, an intelligent method is based on an analysis of physiological big data collected by wearable smartwatches. Firstly, physiological data such as pulse, heart rate, and blood oxygen were collected continuously from individuals by wearing smartwatches, and the data was digitally transmitted. Secondly, the transmitted data was sent to a health management platform by Narrow Band Internet of Things. Analyzing the data, physicians evaluated individual situations via an intelligent math model. Finally, the results were fed back to individuals through a smartphone APP to finish a medical diagnosis, disease prediction, or warning. The intelligent health management method and technology created via years of studies have been verified and will provide a new and effective strategy for health management.

The conventional application of deep reinforcement learning (DRL) to autonomous racing requires the agent to crash during training, thus limiting training to simulation environments. Further, many DRL approaches still exhibit high crash rates after training, making them infeasible for real-world use. This paper addresses the problem of safely training DRL agents for autonomous racing. Firstly, we present a Viability Theory-based supervisor that ensures the vehicle does not crash and remains within the friction limit while maintaining recursive feasibility. Secondly, we use the supervisor to ensure the vehicle does not crash during the training of DRL agents for high-speed racing. The evaluation in the open-source F1Tenth simulator demonstrates that our safety system can ensure the safety of a worst-case scenario planner on four test maps up to speeds of 6 m/s. Training agents to race with the supervisor significantly improves sample efficiency, requiring only 10,000 steps. Our learning formulation leads to learning more conservative, safer policies with slower lap times and a higher success rate, resulting in our method being feasible for physical vehicle racing. Enabling DRL agents to learn to race without ever crashing is a step towards using DRL on physical vehicles.

Road detection remains a captivating and crucial aspect of any form of autonomous driving. In this manuscript, we furnish a comprehensive appraisal of recent advancements in road lane detection, a fundamental component integral to autonomous driving. Despite numerous methodologies being proposed to augment accuracy while expediting speed, various hindrances, including lane marking variations, lighting fluctuations, and shadowy conditions, necessitate the establishment of dependable detection systems. Model-based and learning-based methods represent the two predominant techniques for lane detection. Model-based methods afford rapid computation speeds, while learning-based methods extend robustness amidst complexity. This paper delves into the techniques of lane detection and forecasts upcoming trends in the field. Collectively, this review offers a sturdy foundation for prospective research in the realm of road lane detection.

Robots such as drones, ground rovers, underwater vehicles and industrial robots have increased in popularity in recent years. Many sectors have benefited from this by increasing productivity while also decreasing costs and certain risks to humans. These robots can be controlled individually but are more efficient in a large group, also known as a swarm. However, an increase in the quantity and complexity of robots creates the need for an adequate control system. Reinforcement learning, an artificial intelligence paradigm, is an increasingly popular approach to control a swarm of unmanned vehicles. The quantity of reviews in the field of reinforcement learning-based swarm robotics is limited. We propose reviewing the various applications, algorithms and simulators on the subject to fill this gap. First, we present the current applications on swarm robotics with a focus on reinforcement learning control systems. Subsequently, we define important reinforcement learning terminologies, followed by a review of the current state-of-the-art in the field of swarm robotics utilizing reinforcement learning. Additionally, we review the various simulators used to train, validate and simulate swarms of unmanned vehicles. We finalize our review by discussing our findings and the possible directions for future research. Overall, our review demonstrates the potential and state-of-the-art reinforcement learning-based control systems for swarm robotics.

Aiming at the problem that the panoptic segmentation network based on coding structure can't accurately extract the detailed information of panoptic images, considering that there are some commonalities between semantic segmentation and instance segmentation tasks, this paper proposes a panoptic segmentation model with multi-feature fusion structure, which generates multi-scale fused feature maps for the panoptic segmentation network, uses the Atrous Spatial Pyramid Pooling to preferentially process the high-level features with rich context information, and then uses the cascade method to splice the low-level features to improve the panoptic segmentation performance of the model. By adding coordinate attention to the ASPP module of the corresponding branch, the perception ability of the model to the contour and instance center is enhanced.