Journal of Field Robotics最新文献

Enforcement of advanced deep learning methods in hand-object pose estimation is an imperative method for grasping the objects safely during the human–robot collaborative tasks. The position and orientation of a hand-object from a two-dimensional image is still a crucial problem under various circumstances like occlusion, critical lighting, and salient region detection and blur images. In this paper, the proposed method uses an enhanced MobileNetV3 with single shot detection (SSD) and YOLOv5 to ensure the improvement in accuracy and without compromising the latency in the detection of hand-object pose and its orientation. To overcome the limitations of higher computation cost, latency and accuracy, the Network Architecture Search and NetAdapt Algorithm is used in MobileNetV3 that perform the network search for parameter tuning and adaptive learning for multiscale feature extraction and anchor box offset adjustment due to auto-variance of weight in the level of each layers. The squeeze-and-excitation block reduces the computation and latency of the model. Hard-swish activation function and feature pyramid networks are used to prevent over fitting the data and stabilizing the training. Based on the comparative analysis of MobileNetV3 with its predecessor and YOLOV5 are carried out, the obtained results are 92.8% and 89.7% of precision value, recall value of 93.1% and 90.2%, mAP value of 93.3% and 89.2%, respectively. The proposed methods ensure better grasping for robots by providing the pose estimation and orientation of hand-objects with tolerance of −1.9 to 2.15 mm along x, −1.55 to 2.21 mm along y, −0.833 to 1.51 mm along z axis and −0.233° to 0.273° along z-axis.

The operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional two-dimensional localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline Adaptative Monte Carlo Localization (AMCL) approach when the robot is in nonplanar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 cm and 0.05 radians in indoor settings and less than 1.0 m in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to three-dimensional simultaneous localization and mapping algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environments.

The cover image is based on the Research Article ASAH: An arc-surface-adsorption hexapod robot with a motion control scheme by Congjun Ma et al., https://doi.org/10.1002/rob.22296

The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s-CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP-LCNet module respectively to reduce the parameters (Params) and giga floating-point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization-based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real-time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots.

Hyper-redundant manipulators have strong flexibility that benefits from their redundant limb structure. However, a large number of redundant degrees of freedom will also lead the solution of inverse kinematics much more difficult, which restricts their motion performance to some extent. Inspired by the FABRIK (Forward and Backward Reaching Inverse Kinematics) method, an improved inverse kinematics solution method for the hyper-redundant manipulator is proposed. Based on the space vector method, the kinematic model of the manipulator is established to dynamically acquire its endpoint position, and the workspace is further obtained by using the Monte Carlo method. The original search method is optimized, the include angle decoupling mechanism between adjacent links is established to obtain the rotation angles of each joint, and the joint angle limitation is introduced to meet the actual manipulator structural restriction. On this basis, the pose constraint mechanism is established to realize the control of the end-link pose, and the linear degree of freedom is introduced to realize the solution after the directional expansion of the manipulator's workspace. A series of simulation experiments are carried out. In the experiments, the position error of the manipulator's endpoint is always less than 10⁻⁶ mm. Meanwhile, the comparative experimental results show that compared with the original method, the proposed method exhibits higher position accuracy under the condition that the computation time is almost the same. In addition, in the end-link pose constraint experiment and path motion experiments, the pose error of the end-link is always less than 10⁻⁷°, indicating that the end-link pose can also meet the high accuracy requirements under the premise of ensuring high position accuracy. Finally, the prototype experiment further verifies its performance.

Planetary rovers may become stuck due to the soft terrain on Mars and other planetary surface. The escape entrapment control strategy is of great significance for planetary rover traversing loosely consolidated granular terrain. After analyzing the performance of the published quadrupedal rotary sequence gait, a “sweeping-spinning” gait was proposed to improve escape entrapment capability. And the forward distance of planetary rovers with “sweeping-spinning” gait was modeled as a function of six control parameters. An online optimization escape entrapment strategy for planetary rover was proposed based on the Bayesian Optimization algorithm. Single-factor experiments were conducted to investigate the effect of each control parameter on forward distance, and determine the parameter ranges. The average forward distance with randomly selected control parameters is 89.64 cm, while that is 136.93 cm with Bayesian optimized control parameters, which verifies the effectiveness of the escape entrapment strategy. Moreover, compared with the trajectory of a planetary rover prototype with the published quadrupedal rotary sequence gait, the trajectory of a planetary rover prototype with “sweeping-spinning” gait is more accurate. Furthermore, the online estimated equivalent terrain mechanical parameters can be used to determine the running state of the planetary rover prototype, which was verified using experiments.

Focused on the problems of cumbersome operation, low efficiency, and high cost in the traditional manual rebar binding process, we propose a mobile robot vision detection and path-planning method for rebar binding to realize automated rebar binding by combining deep learning and path-planning technology. A MobileNetV3-SSD rebar binding crosspoints recognition model is built based on TensorFlow deep learning framework, and a crosspoints localization method combining control factor α and feature projection curve is introduced to achieve the localization of unbound crosspoints. In addition, A back-and-forth path-planning algorithm with priority constraints combined with dead zone escape algorithm based on improved A* is proposed to achieve complete coverage path planning of the working area and path transfer of the dead zone. In the field test of the robot prototype, the classification accuracy and localization accuracy reached 94.40% and 90.49%, and the robot was able to reach complete coverage path planning successfully. The experimental results show that the visual detection method can achieve fast, noncontact and intelligent recognition of rebar binding crosspoints, which has good robustness and application value. At the same time, the proposed path-planning method has higher efficiency in the execution of robot complete coverage path planning, and meets the basic requirements of path planning for rebar binding process.

With the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta-heuristic algorithms have gained popularity owing to their efficiency and problem-specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF-IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF-IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time-savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta-heuristics, APF-IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF-IMOSO can generate multiple viable collision-free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

The cover image is based on the Research Article The Haidou-1 hybrid underwater vehicle for the Mariana Trench science exploration to 10,908 m depth by Jian Wang et al., https://doi.org/10.1002/rob.22307

Litter nowadays presents a significant threat to the equilibrium of many ecosystems. An example is the sea, where litter coming from coasts and cities via gutters, streets, and waterways, releases toxic chemicals and microplastics during its decomposition. Litter removal is often carried out manually by humans, which inherently lowers the amount of waste that can be effectively collected from the environment. In this paper, we present a novel quadruped robot prototype that, thanks to its natural mobility, is able to collect cigarette butts (CBs) autonomously, the second most common undisposed waste worldwide, in terrains that are hard to reach for wheeled and tracked robots. The core of our approach is a convolutional neural network for litter detection, followed by a time-optimal planner for reducing the time needed to collect all the target objects. Precise litter removal is then performed by a visual-servoing procedure which drives the nozzle of a vacuum cleaner that is attached to one of the robot legs on top of the detected CB. As a result of this particular position of the nozzle, we are able to perform the collection task without even stopping the robot's motion, thus greatly increasing the time-efficiency of the entire procedure. Extensive tests were conducted in six different outdoor scenarios to show the performance of our prototype and method. To the best knowledge of the authors, this is the first time that such a design and method was presented and successfully tested on a legged robot.