Journal of Field Robotics最新文献

With the advancement of industrial automation, the frequency of human–robot interaction (HRI) has significantly increased, necessitating a paramount focus on ensuring human safety throughout this process. This paper proposes a simulation-assisted neural network for point cloud segmentation in HRI, specifically distinguishing humans from various surrounding objects. During HRI, readily accessible prior information, such as the positions of background objects and the robot's posture, can generate a simulated point cloud and assist in point cloud segmentation. The simulation-assisted neural network utilizes simulated and actual point clouds as dual inputs. A simulation-assisted edge convolution module in the network facilitates the combination of features from the actual and simulated point clouds, updating the features of the actual point cloud to incorporate simulation information. Experiments of point cloud segmentation in industrial environments verify the efficacy of the proposed method.

This paper presents a precise two-robot collaboration method for three-dimensional (3D) self-localization relying on a single rotating camera and onboard accelerometers used to measure the tilt of the robots. This method allows for localization in global positioning system-denied environments and in the presence of magnetic interference or relatively (or totally) dark and unstructured unmarked locations. One robot moves forward on each step while the other remains stationary. The tilt angles of the robots obtained from the accelerometers and the rotational angle of the turret, associated with the video analysis, make it possible to continuously calculate the location of each robot. We describe a hardware setup used for experiments and provide a detailed description of the algorithm that fuses the data obtained by the accelerometers and cameras and runs in real-time on onboard microcomputers. Finally, we present 2D and 3D experimental results, which show that the system achieves 2% accuracy for the total traveled distance (see Supporting Information S1: video).

Soft crawling robots are usually driven by bulky and complex external pneumatic or hydraulic actuators. In this work, we proposed a miniaturized soft crawling caterpillar based on electrohydrodynamic (EHD) pumps. The caterpillar was mainly composed of a flexible EHD pump for providing the driving force, an artificial muscle for performing the crawling, a fluid reservoir, and several stabilizers and auxiliary feet. To achieve better crawling performances for our caterpillar, the flow rate and pressure of the EHD pump were improved by using a curved electrode design. The electrode gap, electrode overlap length, channel height, electrode thickness, and electrode pair number of the EHD pump were further optimized for better performance. Compared with the EHD pumps with conventional straight electrodes, our EHD pump showed a 50% enhancement in driving pressure and a 60% increase in flow rate. The bending capability of the artificial muscles was also characterized, showing a maximum bending angle of over 50°. Then, the crawling ability of the soft crawling caterpillar is also tested. Finally, our caterpillar owns the advantages of simple fabrication, low-cost, fast movement speed, and small footprint, which has robust and wide potential for practical use, especially over various terrains.

Phenotyping robots have the potential to obtain crop phenotypic traits on a large scale with high throughput. Autonomous navigation technology for phenotyping robots can significantly improve the efficiency of phenotypic traits collection. This study developed an autonomous navigation method utilizing an RGB-D camera, specifically designed for phenotyping robots in field environments. The PP-LiteSeg semantic segmentation model was employed due to its real-time and accurate segmentation capabilities, enabling the distinction of crop areas in images captured by the RGB-D camera. Navigation feature points were extracted from these segmented areas, with their three-dimensional coordinates determined from pixel and depth information, facilitating the computation of angle deviation (α) and lateral deviation (d). Fuzzy controllers were designed with α and d as inputs for real-time deviation correction during the walking of phenotyping robot. Additionally, the method includes end-of-row recognition and row spacing calculation, based on both visible and depth data, enabling automatic turning and row transition. The experimental results showed that the adopted PP-LiteSeg semantic segmentation model had a testing accuracy of 95.379% and a mean intersection over union of 90.615%. The robot's navigation demonstrated an average walking deviation of 1.33 cm, with a maximum of 3.82 cm. Additionally, the average error in row spacing measurement was 2.71 cm, while the success rate of row transition at the end of the row was 100%. These findings indicate that the proposed method provides effective support for the autonomous operation of phenotyping robots.

This paper introduces a multi-degree of freedom bionic crocodile robot designed to tackle the challenge of cleaning pollutants and debris from the surfaces of narrow, shallow rivers. The robot mimics the “death roll” motion of crocodiles which is a technique used for object disintegration. First, the design incorporated a swinging tail mechanism using a multi-section oscillating guide-bar mechanism. By analyzing three-, four-, and five-section tail structures, the four-section tail was identified as the most effective structure, offering optimal strength and swing amplitude. Each section of the tail can reach maximum swing angles of 8.05°, 20.95°, 35.09°, and 43.84°, respectively, under a single motor's drive. Next, the robotic legs were designed with a double parallelogram mechanism, facilitating both crawling and retracting movements. In addition, the mouth employed a double-rocker mechanism for efficient closure and locking, achieving an average torque of 5.69 N m with a motor torque of 3.92 N m. Moreover, the robotic body was designed with upper and lower segment structures and waterproofing function was also considered. Besides, the kinematic mechanism and mechanical properties of the bionic crocodile structure were analyzed from the perspectives of modeling and field tests. The results demonstrated an exceptional kinematic performance of the bionic crocodile robot, effectively replicating the authentic movement characteristics of a crocodile.

This paper presents a multiagent cooperative search algorithm for identifying an unknown number of targets. The objective is to determine a collection of observation points and corresponding safe paths for agents, which involves balancing the detection time and the number of targets searched. A Bayesian framework is used to update the local probability density function of the targets when the agents obtain information. We utilize model predictive control and establish utility functions based on the detection probability and decrease in information entropy. A target detection algorithm is implemented to verify the target based on minimum-risk Bayesian decision-making. Then, we improve the search algorithm with the target detection algorithm. Several simulations demonstrate that compared with other existing approaches, the proposed approach can reduce the time needed to detect targets and the number of targets searched. We establish an experimental platform with three unmanned aerial vehicles. The simulation and experimental results verify the satisfactory performance of our algorithm.

Underwater robotic systems have the potential to assist and complement humans in dangerous or remote environments, such as in the monitoring, sampling, or manipulation of sensitive underwater species. Here we present the design, modeling, and development of an underwater manipulator (UM) with a lightweight cable-driven structure that allows for delicate deep-sea reef sampling. The compact and lightweight design of the UM and gripper decreases the coupling effect between the UM and the underwater vehicle (UV) significantly. The UM and gripper are equipped with force sensors, enabling them for soft and sensitive object manipulation and grasping. The accurate force exertion capabilities of the UM ensure efficient operation in the process of localization and approaching reef samples, such as the corals and sponges. The active force control of the tendon-driven gripper ensures gentle/delicate grasping, handling, and transporting of the marine samples without damaging their tissues. A complete simulation of the UM is provided for deriving the required specifications of actuators and sensors to be compatible with the UVs with a speed range of 1–4 Knots. The system's performance for accurate trajectory tracking and delicate grasping of two different types of underwater species (a sponge skeleton and a Neptune's necklace seaweed) is verified using a model-free robust-adaptive position/force controller.

Focusing on severe color deviation, low brightness, and mixed noise caused by inherent scattering and light attenuation effects within underwater environments, an underwater-attention progressive generative adversarial network (UP-GAN) is innovated for underwater image enhancement (UIE). Salient contributions are as follows: (1) By elaborately devising an underwater background light estimation module via an underwater imaging model, the degradation mechanism can be sufficiently integrated to fuse prior information, which in turn saves computational burden on subsequent enhancement; (2) to suppress mixed noise and enhance foreground, simultaneously, an underwater dual-attention module is created to fertilize skip connection from channel and spatial aspects, thereby getting rid of noise amplification within the UIE; and (3) by systematically combining with spatial consistency, exposure control, color constancy, color relative dispersion losses, the entire UP-GAN framework is skillfully optimized by taking into account multidegradation factors. Comprehensive experiments conducted on the UIEB data set demonstrate the effectiveness and superiority of the proposed UP-GAN in terms of both subjective and objective aspects.

Mountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low-cost, consumer-grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability-prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert-informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state-of-the-art robotics and MSAR.

With the aging population and increasing labor costs, traditional manual harvesting methods have become less economically efficient. Consequently, research into fully automated harvesting using selective harvesting robots for cherry tomatoes has become a hot topic. However, most of the current research is focused on individual harvesting of large tomatoes, and there is less research on the development of complete systems for harvesting cherry tomatoes in clusters. The purpose of this study is to develop a harvesting robot system capable of picking tomato clusters by cutting their fruit-bearing pedicels and to evaluate the robot prototype in real greenhouse environments. First, to enhance the grasping stability, a novel end-effector was designed. This end-effector utilizes a cam mechanism to achieve asynchronous actions of cutting and grasping with only one power source. Subsequently, a visual perception system was developed to locate the cutting points of the pedicels. This system is divided into two parts: rough positioning of the fruits in the far-range view and accurate positioning of the cutting points of the pedicels in the close-range view. Furthermore, it possesses the capability to adaptively infer the approaching pose of the end-effector based on point cloud features extracted from fruit-bearing pedicels and stems. Finally, a prototype of the tomato-harvesting robot was assembled for field trials. The test results demonstrate that in tomato clusters with unobstructed pedicels, the localization success rates for the cutting points were 88.5% and 83.7% in the two greenhouses, respectively, while the harvesting success rates reached 57.7% and 55.4%, respectively. The average cycle time to harvest a tomato cluster was 24 s. The experimental results prove the potential for commercial application of the developed tomato-harvesting robot and through the analysis of failure cases, discuss directions for future work.