In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.
Near-ground sensing data, such as geospatial measurements of soil apparent electrical conductivity (ECa), are used in precision agriculture to improve farming practices and increase crop yield. Near-ground sensors provide valuable information, yet, the process of collecting, assessing, and interpreting measurements requires significant human labor. Automating parts of this process via the use of mobile robots can help decrease labor burden, and increase the accuracy and frequency of data collections, and overall increase the adoption and use of ECa measurement technology. This paper introduces…
University of Surrey（萨里大学）；UK Atomic Energy Authority（英国原子能管理局）
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments. However, real-time constraints frequently limit the accuracy of online tests especially in scenarios where realistic robot-terrain interactions are complex to model. In this context, we propose a deep learning framework trained in an end-to-end fashion from elevation maps and trajectories to estimate the occurrence of failure events. The network is first trained and tested in simulation over synthetic maps generated by the OpenSimplex algorithm. The prediction performance of the…
Georgia Institute of Technology（佐治亚理工学院）；NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）；University of Goettingen（哥廷根大学）
One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreo…
Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4
Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, Lincoln, UK（林肯自主系统中心（L-CAS）、林肯大学、林肯，英国）；Faculty of Aerospace Engineering, MAVLab, Delft University of Technology (TU Delft), Delft, Netherlands（航空航天工程学院、MAVLab、代尔夫特理工大学（TU Delft）、代尔夫特，荷兰）
Washington State University（华盛顿州立大学）；United States Department of Agriculture（美国农业部）
Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.
A Contrastive Fewshot RGBD Traversability Segmentation Framework for Indoor Robotic Navigation
Department of Computer Science and Engineering, University of Texas at Arlington（计算机科学与工程系，德克萨斯大学阿灵顿分校）；Uber（优步）；Cognitive Robotics Lab, Department of Electrical Engineering and Computer Science, University of Arkansas（认知机器人实验室，电气工程与计算机科学系，阿肯色大学）
A Verification Methodology for Safety Assurance of Robotic Autonomous Systems
Faculty of Science and Technology, Norwegian University Of Life Sciences (NMBU)（科学与技术学院，挪威生命科学大学）；Department of Mechatronics, University of Agder (UiA)（机械电子系，阿格德大学）；Department of Computer Science, University Of York, UK（计算机科学系，约克大学（英国））
Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state-of-the-art non-contact soil moisture sensor that builds moisture maps on the fly and automatically selects the most optimal sampling locations. The robot is guided by an autonomous exploration…
Aarhus University（奥胡斯大学）；Aalborg University（奥尔堡大学）；Conpleks Innovation；University of Southern Denmark（南丹麦大学）
This paper describes a collaborative modelling approach to automated and robotic agricultural vehicle design. The Cresendo technology allows engineers from different disciplines to collaborate and produce system models. The combined models are called co-models and their execution co-simulation. To support future development efforts a template library of different vehicle and controllers types are provided. This paper describes a methodology to developing co-models from initial problem definition to deployment of the actual system. We illustrate the development methodology with an example devel…
Automated and robotic ground-vehicle solutions are gradually becoming part of the agricultural industry, where they are used for performing tasks such as feeding, herding, planting, harvesting, and weed spraying. Agricultural machinery operates in both indoor and outdoor farm environments, resulting in changing operational conditions. Variation in the load transported by ground-vehicles is a common occurrence in the agricultural domain, in tasks such as animal feeding and field spraying. The development of automated and robotic ground-vehicle solutions for conditions and scenarios in the agric…
University of Lincoln（林肯大学）；Saga Robotics（萨迦机器人公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment, however, is a prerequisite to many services offered by fleets of mobile robots collaborating with human workers. Consequently, in this paper, we expand on the concept of a topological particle filter (TPF) to accurately and individually localize and track workers in a farm environment, integrating…
Dual-BEV Nav: Dual-layer BEV-based Heuristic Path Planning for Robotic Navigation in Unstructured Outdoor Environments
Software Engineering Institute, East China Normal University（华东师范大学软件工程学院）；School of Geospatial Information, Information Engineering University（信息工程大学地理空间信息学院）；College of Computer and Cyber Security, Fujian Normal University（福建师范大学计算机与网络空间安全学院）
针对非结构化室外环境路径规划难题，提出 Dual-BEV Nav 方法，通过整合局部与全局 BEV 可通行性建立双层启发式规划范式，显著提升了时间距离预测精度并实现了长距离室外导航。
Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment…
Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to …
University of Tokyo（东京大学）；California Institute of Technology（加州理工学院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertainty-awa…
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Dept. of Computer Science & Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Dept. of Mechanical Engineering, University of Minnesota（机械工程系，明尼苏达大学）
Agri-Sim: Agricultural Simulation Platform for Embodied Intelligence Evaluation in Greenhouse Robotics
School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）；Sino-European School of Technology of Shanghai University（上海大学中欧工程技术学院）；School of Future Technology (Institute of Artificial Intelligence), Shanghai University（上海大学未来技术学院（人工智能研究院））
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Centre for Research and Technology Hellas（希腊研究与技术中心）
This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in pu…
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed "structural" peculiarity on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual signals. In this paper, however, we argue that learning such structure-sensitive representations can be a suboptimal approach to some classes of anomaly (e.g., unhealthy fruits) which could be better recogn…
The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced …
Lincoln Institute for Agri-food Technology, University of Lincoln（林肯大学林肯农业食品技术研究所）
Global food security is an issue that is fast becoming a critical matter in the world today. Global warming, climate change and a range of other impacts caused by humans, such as carbon emissions, sociopolitical and economical challenges (e.g. war), traditional workforce/labour decline and population growth are straining global food security. The need for high-speed and reliable wireless communication in agriculture is becoming more of a necessity rather than a technological demonstration or showing superiority in the field. Governments and industries around the world are seeing more urgency i…
University of Salento（萨伦托大学）；CNR-ISSIA（意大利国家研究委员会智能系统研究所）；IRSTEA（法国国家农业与环境研究院）；Danish Technological Institute（丹麦技术研究所）；Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）；CLAAS Agrosystems GmbH（CLAAS农业系统有限公司）
In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate and robust environmental perception is a critical requirement to address unsolved issues including safe interaction with field workers and animals, obstacle detection in controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness, in general, towards increased process automation. Given the variety of conditions thatmay be encountered in the field, no single se…
National University of Sciences and Technology（巴基斯坦国立科技大学）
Robots have revolutionized our way of life in recent years.One of the domains that has not yet completely benefited from the robotic automation is the agricultural sector. Agricultural Robotics should complement humans in the arduous tasks during different sub-domains of this sector. Extensive research in Agricultural Robotics has been carried out in Japan, USA, Australia and Germany focusing mainly on the heavy agricultural machinery. Pakistan is an agricultural rich country and its economy and food security are closely tied with agriculture in general and wheat in particular. However, agricu…
In this paper we present The Rosario Dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of 6 sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural SLAM/odometry and sensor fusion research. It contains synchr…
