Journal of Field Robotics最新文献

The coal industry has long been troubled by the imbalance between mining and tunneling, and between excavating and support. The main cause of this problem is the inability to perform excavating and permanent support operations in parallel. Additionally, the limited space near the tunneling face hampers the efficiency of permanent support. Temporary support is an effective method to ensure the stability of the surrounding rock and expand the space for parallel operations between excavating and permanent support activities. This work provides a brief overview of the current research status on temporary support, emphasizing that the key to achieving safe, efficient, and rapid excavation lies in the development of temporary support robots. To meet the development needs of temporary support robots, three key technologies are proposed: the construction of a coupling model between the robot and the surrounding rock, spatial layout optimization of the rapid advancement system, and adaptive control of the robot. This work details the methods and approaches for constructing the coupling model, the elements of system spatial layout optimization, and the methods and strategies for the robot's adaptive control. Our team successfully tested the shield robot system at Xiaobaodang Mining Company, verifying the feasibility of these key technologies.

Floor-tiling robotics are increasingly employed in on-site building constructions owing to their remarkable benefits on rising working efficiency and reducing labor costs. In this study, a fluid–structure interaction (FSI) model of robotic tiling was established for the first time, construction parameters and adhesive properties were modified, and their influences on the quality of robotic floor-tiling were systematically investigated by tracking the mechanical behaviors of tiles and adhesive during tiling and the interfacial defects after tiling. Results indicated that the established FSI model was feasible for assessing robotic tiling quality with a deviation of less than 2%. The adhesive extruded horizontally was evenly distributed in cylindrical strips. An increase in the number of extrusion pipes slightly improved the tiling quality. Compared with the leveling loads of compression and vertical vibration, shear vibration could effectively eliminate tile rebounding and enlarge the contact area of tile–adhesive by up to 135.85%. Moderate increases in the amplitude and frequency of shear vibration resulted in lower rebounding and larger contact areas. An appropriate increase of yield stress heightened tiling quality by keeping the extrusive appearance of the adhesive, increasing slightly tile rebounding and enlarging the contact area of tile–adhesive to 0.625 m². As yield stress was excessively high, tremendous elastic deformations of adhesive led to remarkable tile rebounding and small contact areas of 0.375 m².

To make the amphibious robot have a lot of functions while keeping the overall structure relatively simple, this paper proposes a multimodule bionic amphibious robot (MMBAR) inspired by the movement mode of jellyfish. The MMBAR consists of four modules, which are connected by snaps, and can be assembled quickly. The wing–leg structure suitable for swimming in the water is designed, which combines the legs and wings using a flexible hinge. Meanwhile, the integrated design principle is adopted to combine the wing–leg structure with the wheel structure to design a deformable wheel suitable for land movement. The overall structure of the MMBAR is simple, and the wing–legs can be deformed to perform a variety of functions, such as acting as a wheel for land movement, as a claw for grasping objects, and as a propulsion mechanism to power the MMBAR for swimming. Theoretical modeling and simulation analyses are conducted separately for the MMBAR on land and in water, which helps understand the movement characteristics of the MMBAR and to obtain more optimized movement parameters. In addition, we conducted experiments on the MMBAR, such as climbing slopes, climbing steps, walking on snow, swimming in water, grasping objects, and so forth, which confirm that the MMBAR possesses a strong ability to adapt to the environment. These research results add new content to the research of amphibious robots, which are expected to replace humans to fulfill more dangerous jobs.

While visual Simultaneous Localization and Mapping systems are well studied and achieve impressive results in indoor and urban settings, natural, outdoor, and open-field environments are much less explored and still present relevant research challenges. Visual navigation and local mapping have shown a relatively good performance in open-field environments. However, globally consistent mapping and long-term localization still depend on the robustness of loop detection and closure, for which the literature is scarce. In this work, we propose a novel method to pave the way towards robust loop detection in open fields, particularly in agricultural settings, based on local feature search and stereo geometric refinement, with a final stage of relative pose estimation. Our method consistently achieves good loop detections, with a median error of 15 cm. We aim to characterize open fields as a novel environment for loop detection, understanding the limitations and problems that arise when dealing with them. Code is available at: https://github.com/CIFASIS/StereoLoopDetector

In recent years, with the continuous development of human exploration of the natural world, there has been a growing demand across various fields for robots capable of free movement in diverse environments. In this study, we address the issue of compliant control for a hexapod robot in diverse environments and propose a novel control method based on an adaptive impedance model for position control. Our approach enables the hexapod robot to stabilize foot force on complex terrains while preserving balance and body height. Specifically, we analyze the algorithm's parameters and stability by establishing the hexapod robot's structural and impedance control models. To tackle this challenge, we introduce an adaptive impedance control algorithm that estimates environmental parameters using Lyapunov's asymptotic stability theorem and achieves tracking of actual foot-end forces to desired foot forces. Furthermore, to ensure body stability and height, we incorporate attitude feedback and body feedback. Experimental results from foot force control experiments conducted on a multilegged robot demonstrate that our proposed algorithm enhances the adaptability and robustness of the robot. This research holds significant implications for the stable control of hexapod robots in complex environments and has promising practical applications.

Replacing manned ships with unmanned surface vehicle (USV) for oil spill containment can reduce the consumption of manpower and resources. This article studies the formation control method of dual USVs during the process of capturing oil spill, based on the engineering background of towing oil boom by dual USVs oil spill recovery system. To calculate the drag force of the oil boom acting on the USV, the shape of the oil boom is simplified into a catenary, the oil boom is modeled, and the hydrodynamic numerical simulation is carried out. To address the issue of “winding” phenomenon and “towing separation,” the formation behavior is designed when double USVs are towing oil fences to capture oil spill. In response to the problem of low task execution efficiency caused by fixed behavior priority in traditional null-space-based (NSB) behavior fusion methods, a fuzzy-priority NSB (FNSB) behavior fusion formation method is proposed by combining fuzzy control with NSB behavior fusion method. In the FNSB behavior fusion formation control method, a smooth transition rule is introduced to make the behavior priority change, USVs can still maintain good formation performance, ensuring the smooth execution of oil spill recovery tasks. Simulation shows that FNSB behavior fusion formation method based on flexible transition rules can improve the rounding efficiency by 26.6% in the environment without obstacles and 37.2% in the environment with multiple obstacles compared with the NSB method. The effectiveness and practicality of this method have been verified through simulation experiments and field experiments on the “Dolphin” series of small USVs.

In ground mobile robots, effective path planning relies on their ability to assess the types and conditions of the surrounding terrains. Neural network-based methods, which primarily use visual images for terrain classification, are commonly employed for this purpose. However, the reliability of these models can vary due to inherent discrepancies between the training images and the actual environment, leading to erroneous classifications and operational failures. Retraining models with additional images from the actual operating environment may enhance performance, but obtaining these images is often impractical or impossible. Moreover, retraining requires substantial offline processing, which cannot be performed online by the robot within an embedded processor. To address this issue, this paper proposes a neural network-based terrain classification model, trained using an existing data set, with a novel uncertainty rejection filter (URF) for terrain-aware path planning of mobile robots operating in unknown environments. A robot, equipped with a pretrained model, initially collects a small number of images (10 in this work) from its current environment to set the target uncertainty ratio of the URF. The URF then dynamically adjusts its sensitivity parameters to identify uncertain regions and assign associated traversal costs. This process occurs entirely online, without the need for offline procedures. The presented method was evaluated through simulations and physical experiments, comparing the point-to-point trajectories of a mobile robot equipped with (1) the neural network-based terrain classification model combined with the presented adaptive URF, (2) the classification model without the URF, and (3) the classification model combined with a nonadaptive version of the URF. Path planning performance measured the Hausdorff distances between the desired and actual trajectories and revealed that the adaptive URF significantly improved performance in both simulations and physical experiments (conducted 10 times for each setting). Statistical analysis via t-tests confirmed the significance of these results.

Semantic segmentation, a task of assigning class labels to each pixel in an image, has found applications in various real-world scenarios, including autonomous driving and scene understanding. However, its widespread use is hindered by the high computational burden. In this paper, we propose an efficient semantic segmentation method based on Feature Cascade Fusion Network (FCFNet) to address this challenge. FCFNet utilizes a dual-path framework comprising the Spatial Information Path (SIP) and the Context Information Path (CIP). SIP is a shallow structure that captures the local dependencies of each pixel to improve the accuracy of detailed segmentation. CIP is the main branch with a deeper structure that captures sufficient contextual information from input features. Moreover, we design an Efficient Receptive Field Module (ERFM) to enlarge the receptive field in the SIP. Meanwhile, Attention Shuffled Refinement Module is used to refine feature maps from different stages. Finally, we present an Attention-Guided Fusion Module to fuse the low- and high-level feature maps effectively. Experimental results show that our proposed FCFNet achieves 70.7% mean intersection over union (mIoU) on the Cityscapes data set and 68.1% mIoU on the CamVid data set, respectively, with inference speeds of 110 and 100 frames per second (FPS), respectively. Additionally, we evaluated FCFNet on the Nvidia Jetson Xavier embedded device, which demonstrated competitive performance while significantly reducing power consumption.

This paper presents the design and optimization of a climbing robot. The design of a ladder-climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder-climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two-point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved.

Screw-driven robot possesses outstanding omnidirectional mobility. Due to the friction between wheels and the ground, the screw-driven robot can traverse rough terrain flexibly. This paper proposes a screw-driven robot, which is equipped with four screws wheels. The directions of thread between adjacent screws are opposite to each other. By controlling the rotating speed and direction of the wheels, the omnidirectional traction could be generated, so the robot possesses the omnidirectional mobility in the field. The robot can work in two locomotion modes, the screw-driven mode and the wheel-driven mode. In addition, the robot can be used for the forestry information-collecting tasks, which improve the efficiency of the forestry task. Two prototypes are fabricated. A series of experiments and field tests are conducted, and the results verify that the robot can traverse rough terrain with the omnidirectional mobility.