The shortage of workforce and increasing cost of maintenance has forced many farm industrialists to shift towards automated and mechanized approaches. The key component for autonomous systems is the path planning techniques used. Coverage path planning (CPP) algorithm is used for navigating over farmlands to perform various agricultural operations such as seeding, ploughing, or spraying pesticides and fertilizers. This report paper compares novel algorithms for autonomous navigation of farmlands. For reduction of navigational constraints, a high-resolution grid map representation is taken into consideration specific to Indian environments. The free space is covered by distinguishing the grid cells as covered, unexplored, partially explored and presence of an obstacle. The performance of the compared algorithms is evaluated with metrics such as time efficiency, space efficiency, accuracy, and robustness to changes in the environment. Robotic Operating System (ROS), Dassault Systemes Experience Platform (3DS Experience), MATLAB along Python were used for the simulation of the compared algorithms. The results proved the applicability of the algorithms for autonomous field navigation and feasibility with robotic path planning.
Colorado School of Mines（科罗拉多矿业学院）；US Army Research Laboratory（美国陆军研究实验室）
Terrain adaptation is an essential capability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected robot behaviors generated by terrain adaptation methods cannot always be executed accurately due to setbacks such as wheel slip and reduced tire pressure. To address this problem, we propose a novel approach for consistent behavior generation that enables the ground robot's actual behaviors to more accurately match expected behaviors while adapting to a variety of unstructured off-road terrain. Our approac…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Queensland（昆士兰大学）
This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies ke…
University of Technology, Sydney（悉尼科技大学）；ETH Zürich（苏黎世联邦理工学院）
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general Informative Path Planning (IPP) framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori . The approach is capable of learning and focusing on regions of interest via adaptatio…
ETH Zürich（苏黎世联邦理工学院）；University of Technology, Sydney（悉尼科技大学）
Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constan…
Colorado School of Mines（科罗拉多矿业学院）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for va…
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）；TopXGun Robotics（TopXGun机器人）；CARDC；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院）
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…
Colorado School of Mines（科罗拉多矿业学院）；Army Research Laboratory (ARL)（美国陆军研究实验室（ARL））
Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the ch…
Department of Engineering（工程系）；Aarhus University（奥胡斯大学）；Australian Centre for Field Robotics（澳大利亚田野机器人中心）；The University of Sydney（悉尼大学）
Reliable obstacle detection and classification in rough and unstructured terrain such as agricultural fields or orchards remains a challenging problem. These environments involve large variations in both geometry and appearance, challenging perception systems that rely on only a single sensor modality. Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly be perceived as nontraversable or might even obscure actual obstacles. Likewise, traversable grass or dirt roads and obstacles such as trees and bushes might be visually ambiguous. In this paper, we combine appearance-…
CIFASIS, French Argentine International Center for Information and Systems Sciences (CONICET-UNR)（法阿国际信息与系统科学中心（阿根廷国家科学与技术研究理事会-罗萨里奥国立大学））
The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (SLAM) systems, key for mobile robotics, given the visual difficulty due to the highly repetitive scene and the crop leaves movement caused by the wind. In recent years, several Visual-Inertial Odometry (VIO) and SLAM systems have been developed. They have proved to be robust and c…
A ROS2 Benchmarking Framework for Hierarchical Control Strategies in Mobile Robots for Mediterranean Greenhouses
Department of Informatics, CIESOL, ceiA3, Universidad de Almería（信息学院、CIESOL、ceiA3、阿尔梅里亚大学）；Department of Computer Science and Automation, National University of Distance Education (UNED)（计算机科学与自动化系、远程教育国家大学（UNED））；Department of Engineering, CIESOL, ceiA3, Universidad de Almería（工程学院、CIESOL、ceiA3、阿尔梅里亚大学）
TEMPO-VINE: A Multi-Temporal Sensor Fusion Dataset for Localization and Mapping in Vineyards
Department of Electronics and Communications, Politecnico di Torino（电子与通信系，politecnico di torino大学）；University Institute for Engineering Research, Miguel Hernández University（工程研究大学学院，miguel hernández大学）
Design of a six wheel suspension and a three-axis linear actuation mechanism for a laser weeding robot
Robotics and Intelligent Systems Engineering (RISE) Lab（机器人与智能系统工程实验室）；Department of Robotics and Artificial Intelligence（机器人与人工智能系）；School of Mechanical and Manufacturing Engineering (SMME)（机械与制造工程学院）；National University of Sciences and Technology (NUST)（国立科学与技术大学）
Berkeley AI Research (BAIR), University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究中心（BAIR））
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns a…
