Journal of Field Robotics最新文献

Automated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)-based mapping. To address this problem, this paper proposes three-dimensionalized feature-based LiDAR-visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three-dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity-weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions.

This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)-based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV-based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after conducting a two-step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Among eligible studies, 97 were classified into two main categories: algorithm-oriented studies and task-oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

This paper discusses a method of determining the minimum safe altitude of an uncrewed aerial vehicle (UAV) at any point within a designated airspace by conducting a glide reachability analysis. Recently, fixed-wing UAVs are more regularly deployed near population centers and in extreme environments, requiring increasingly robust emergency systems and planning. The long-ranges and adverse terrain associated with monitoring the Volcán de Fuego in Guatemala by a team from the University of Bristol (UoB) increases the likelihood that motor failure would result in the aircraft being unable to Return To Home (RTH) and impossible to retrieve. A method for delineating a boundary representing the minimum safe altitude required for the aircraft to safely glide to the airfield in the event of a motor failure was developed within MATLAB, defined by the UAV's minimum glide angle in wind. This model was subsequently compared with flight data from UoB missions around Fuego to better improve its accuracy and analyze the limitations of the missions.

In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm-feedforward neural network, enhanced unified algorithm-susceptible-infected-removed, and bounded behavior-particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

Due to recent advances in mobile robotics, applications involving multiple robots have become the focus of research in this area. Communication and data exchange among robots are essential features of these systems. In this context, this work presents the conception and the implementation of an application used to share local three-dimensional occupancy grid maps in a network composed of multiple unmanned aerial vehicles (UAVs). This system is based on compression, serialization, packaging, and data transmission whenever the maps receive updates. The simulations demonstrate that the application can significantly reduce the map data size and efficiently share it among all members of the cooperative system. Moreover, a scalability test demonstrates that the system is scalable with a growing number of UAVs.

The cover image is based on the Research Article Whole-body motion planning and tracking of a mobile robot with a gimbal RGB-D camera for outdoor 3D exploration by Zhihao Wang et al., https://doi.org/10.1002/rob.22281

Robotic chefs are a promising technology that can improve the availability of quality food by reducing the time required for cooking, therefore decreasing food's overall cost. This paper clarifies and structures design and benchmarking rules in this new area of research, and provides a comprehensive review of technologies suitable for the construction of cooking robots. The diner is an ultimate judge of the cooking outcome, therefore we put focus on explaining human food preferences and perception of taste and ways to use them for control. Mechanical design of robotic chefs at a practically low cost remains the challenge, but some recently published gripper designs as well as whole robotic systems show the use of cheap materials or off-the-shelf components. Moreover, technologies like taste sensing, machine learning, and computer vision are making their way into robotic cooking enabling smart sensing and therefore improving controllability and autonomy. Furthermore, objective assessment of taste and food palatability is a challenge even for trained humans, therefore the paper provides a list of procedures for benchmarking the robot's tasting and cooking abilities. The paper is written from the point of view of a researcher or engineer building a practical robotic system, therefore there is a strong priority for solutions and technologies that are proven, robust and self-contained enough to be a part of a larger system.

A planetary penetrator is a kind of machine that can use its special structure to independently penetrate an exoplanet and sample it for storage. It is a hot research topic in the world and has important development potential in the field of deep space exploration. Based on the analysis of relevant literature, this paper classifies and compares various representative planetary penetrator projectiles around the current research status. The development progress and key technologies of the planetary penetrator are analyzed, the current technical challenges are summarized, and the development trend of the planetary penetrator has prospected. The planetary probe has a broad application prospect, and its development will provide technical support for humans to establish extraterrestrial habitat, and play an important role in the field of deep space exploration. Currently, planetary penetrator research is still in its infancy, and the field is full of possibilities that need to be explored and improved.

This research focuses on designing and evaluating ergonomic self-propelled machinery seats to reduce whole-body vibration (WBV) exposure among male and female agricultural workers. Subjects without musculoskeletal disorders were selected, and their anthropometric parameters were analyzed. An ergonomically refined seat, considering anthropometric dimensions and vibration reduction, was developed and tested. Vibration isolators using piezoelectric material enhanced operator comfort. In a laboratory experiment, real-time one-third octave band WBV data were collected using various seat types and engine speeds. At 1200 rpm, female operators experienced WBV levels between 3.42 and 13.40 m/s², while males ranged from 3.13 to 12.20 m/s². At 1600 rpm, females (T-1) had WBV levels of 20.20–42.39 m/s², and males recorded 18.90–40.12 m/s². At 2000 rpm (T-1), female operators WBV ranged from 246.71 to 303.45 m/s², and males from 248.10 to 300.13 m/s². At 2400 rpm (T-1), female operators experienced WBV from 385.29 to 457.87 m/s², and males from 381.57 to 445.50 m/s². An integrated approach with artificial neural networks and genetic algorithms optimized machine operating parameters, resulting in minimum WBV levels. The highly accurate Multilayer Feed-Forward Artificial Neural Network model (2-10-1) had a correlation (R) of 0.996 and a low mean-squared error of 0.198. This research underscores the effectiveness of seat isolators in reducing vibrations and highlights the importance of considering both seat design and engine speed, especially concerning gender-specific differences in vibration tolerance. It provides valuable insights for improving the comfort and safety of self-propelled machinery operators in agriculture.

Accurate location information is crucial for ensuring the reliability of service robots, such as field vehicles and agricultural robots. These practical robots often encounter fluctuating or inclined terrains in dynamic conditions during operation. Additionally, the performance of mobile robots depends on the physical size and weight of the localization system–physically heavy systems may degrade performance. In this paper, we present a compact, lightweight RTK-GNSS device weighing under 40 g, which achieves centimeter-level accuracy across different attitudes and dynamic situations. To enhance signal reception and positioning performance, we lessen noise levels in the power circuit and radio-frequency signal conditioning using a low-dropout regulator and low-noise amplifier. Utilizing the configured device, we verified centimeter-level accuracy through experiments of tracking an unmanned ground vehicle. The potential of the device is substantiated via three application scenarios—a localization of a moving human for a wearable device, a heading estimation of a mobile robot in stationary conditions, and practical field tests in various signal-reception environments. Attributable to its compact dimensions and lightweight features, the proposed device can yield a low system strain while maintaining high accuracy and reliable performance, rendering it highly adaptable for various robotic applications.