An autonomous strawberry‐harvesting robot: Design,development, integration,and field evaluation

This paper presents an autonomous robot capable of picking strawberries continuously in polytunnels. Robotic harvesting in cluttered and unstructured environment remains a challenge. A novel obstacle‐separation algorithm was proposed to enable the harvesting system to pick strawberries that are located in clusters. The algorithm uses the gripper to push aside surrounding leaves, strawberries, and other obstacles. We present the theoretical method to generate pushing paths based on the surrounding obstacles. In addition to manipulation, an improved vision system is more resilient to lighting variations, which was developed based on the modeling of color against light intensity. Further, a low‐cost dual‐arm system was developed with an optimized harvesting sequence that increases its efficiency and minimizes the risk of collision. Improvements were also made to the existing gripper to enable the robot to pick directly into a market punnet, thereby eliminating the need for repacking. During tests on a strawberry farm, the robots first‐attempt success rate for picking partially surrounded or isolated strawberries ranged from 50% to 97.1%, depending on the growth situations. Upon an additional attempt, the pick success rate increased to a range of 75–100%. In the field tests, the system was not able to pick a target that was entirely surrounded by obstacles. This failure was attributed to limitations in the vision system as well as insufficient dexterity in the grippers. However, the picking speed improved upon previous systems, taking just 6.1 s for manipulation operation in the one‐arm mode and 4.6 s in the two‐arm mode.     

Design of an autonomous agricultural robot

This paper presents a state-of-the-art review in the development of autonomous agricultural robots including guidance systems, greenhouse autonomous systems and fruit-harvesting robots. A general concept for a field crops robotic machine to selectively harvest easily bruised fruit and vegetables is designed. Future trends that must be pursued in order to make robots a viable option for agricultural operations are focused upon.A prototype machine which includes part of this design has been implemented for melon harvesting. The machine consists of a Cartesian manipulator mounted on a mobile chassis pulled by a tractor. Two vision sensors are used to locate the fruit and guide the robotic arm toward it. A gripper grasps the melon and detaches it from the vine. The real-time control hardware architecture consists of a blackboard system, with autonomous modules for sensing, planning and control connected through a PC bus. Approximately 85% of the fruit are successfully located and harvested.     

Development of the multifunctional rescue robot FUHGA2 and evaluation at the world robot summit 2018

Abstract

Robots are needed in rescue activities and the inspection of infrastructure. We developed the rescue robot FUHGA2 so that it has various functions. FUHGA2 has high mobility, with main crawlers covering the body and four sub-crawlers, high dexterity using a long six-axis arm, and high searching ability using various sensors. Furthermore, a novel parallel gripper unit with wire-jamming mechanism is mounted at the tip of the arm, which can change its hardness and fit its shape to an object. Additionally, high Maneuvering is achieved by displaying sensor information to the operator clearly and intuitive operation command design. FUHGA2 won the World Robot Summit 2018 Standard Disaster Robotics Challenge, which is a competition for rescue robots. We achieved high scores in all categories of tasks. In this paper, we describe the development of FUHGA2 and the performance evaluation results from the World Robot Summit 2018.     

A micro-adjusting attitude mechanism for autonomous drilling robot end-effector

Drilling end-effector is a key unit in autonomous drilling robot. The perpendicularity of the hole has an important influence on the quality of airplane assembly. Aiming at the robot drilling perpendicularity, a micro-adjusting attitude mechanism and a surface normal measurement algorithm are proposed in this paper. In the mechanism, two rounded eccentric discs are used and the small one is embedded in the big one, which makes the drill＇s point static when adjusting the drill＇s attitude. Thus, removal of drill＇s point position after adjusting the drill attitude can be avoided. Before the micro-adjusting progress, four non-coplanar points in space are used to determine a unique sphere. The normal at the drilling point is measured by four laser ranging sensors. The adjusting angles at which the motors should be rotated to adjust attitude can be calculated by using the deviation between the normal and the drill axis. Finally, the motors will drive the two eccentric discs to achieve micro-adjusting progress. Experiments on drilling robot system and the results demonstrate that the adjusting mechanism and the algorithm for surface normal measurement are effective with high accuracy and efficiency.     

Development of distance recognition using an ocellus camera for an autonomous personal robot

We are attempting to develop an autonomous personal robot that has the ability to perform practical tasks in a human living environment by using information derived from sensors. When a robot operates in a human environment, the issue of safety must be considered in regard to its autonomous movement. Thus, robots absolutely require systems that can recognize the external world and perform correct driving control. We have thus developed a navigation system for an autonomous robot. The system requires only image data captured by an ocellus CCD camera. In this system, we allow the robot to search for obstacles present on the floor. Then, the robot obtains distance recognition necessary for evasion of the object, including data of the obstacle’s width, height, and depth by calculating the angles of images taken by the CCD camera. We applied the system to a robot in an indoor environment and evaluated its performance, and we consider the resulting problems in the discussion of our experimental results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

一种移动机器人的禁忌搜索自主导航算法

纯粹的反应式导航算法在复杂未知环境下易陷入局部极小,为此提出一种基于局部子目标和禁忌搜索的自主导航算法．以当前可视区域内障碍物的关键角点为搜索邻域,利用禁忌搜索算法执行优化操作生成当前子目标,进而采用反应式导航算法对其进行跟踪,最终通过子目标的动态切换引导机器人驶达目标位置．算法可有效克服局部极小,显著提高机器人在复杂环境下的自主性．理论分析和仿真实验验证了算法的可行性和有效性．     

强化学习在移动机器人自主导航中的应用

概述了移动机器人常用的自主导航算法及其优缺点,在此基础上提出了强化学习方法。描述了强化学习算法的原理,并实现了用神经网络解决泛化问题。设计了基于障碍物探测传感器信息的机器人自主导航强化学习方法,给出了学习算法中各要素的数学模型。经仿真验证,算法正确有效,具有良好的收敛性和泛化能力。     

Saliency and spatial information-based landmark selection for mobile robot navigation in natural environments

In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments.     

基于LiDAR/INS的野外移动机器人组合导航方法

移动机器人在地形复杂等野外环境跨区域运动时,机器人运动特性和环境特征变化更为明显,由此引起的点云畸变和特征点稀疏等问题尤为突出,有必要结合传感器标定误差、车轮打滑和车体颠簸等因素进一步改进机器人的位姿估计精度。本文对基于LiDAR/INS的移动机器人环境建模和自主导航方法展开研究,针对LeGO-LOAM等在处理车体姿态快速变化时的性能退化问题,提出一种适用于野外移动机器人运动特性的点云特征分析和多传感融合方法,利用IMU的预积分与LiDAR的scan-to-map构成优化函数,进而迭代更新机器人的位姿。野外环境实验结果表明,当机器人以较高速度做转弯运动或在短时间内多次转向时,本文所提方法仍可以为优化提供良好的初值估计,相比LeGO-LOAM等方法具有更高的位姿估计精度。     

Development of wheel-less snake robot with two distinct gaits and gait transition capability

Snake robots are mostly designed based on single mode locomotion. However, single mode gait most likely could not work effectively when the robot is subject to an unstructured working environment with different measures of terrain complexity. As a solution, mixed mode locomotion is proposed in this paper by synchronizing two types of gaits known as serpentine and wriggler gaits used for non-constricted and narrow space environments, respectively, but for straight line locomotion only. A gait transition algorithm is developed to efficiently change the gait from one to another. This study includes the investigation on kinematics analysis followed by dynamics analysis while considering related structural constraints for both gaits. The approach utilizes the speed of the serpentine gait for open area locomotion and exploits the narrow space access capability of the wriggler gait. Hence, it can increase motion flexibility in view of the fact that the robot is able to change its mode of locomotion according to the working environment.     

Cataglyphis: An autonomous sample return rover

This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth‐specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under‐actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.     

Under canopy light detection and ranging‐based autonomous navigation

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM‐30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row‐based and high‐leaf‐cover semistructured crop plantations, such as corn (Zea mays) and sorghum ( Sorghum bicolor). The key contribution of our work is a LiDAR‐based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low‐hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within‐row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row‐lines based on current and previous information. The proposed LiDAR‐based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR‐based navigation in realistic field environments for small under‐canopy robots.     

视觉传感系统的自主移动机器人路径规划方法

在未知环境下,机器人很难快速获取周边环境信息并建立实时环境地图,实现自主运行.为此提出基于视觉导航的方法,利用全景摄像机作为机器人的视觉传感器系统采集环境信息,将彩色地图进行HSI空间下模糊聚类图像分割,得到环境二值图像;将图像进行栅格化处理来构建环境地图,运用8方向连接的Dijkstra进行全局路径规划,计算出最优路径,从而实现移动机器人的快速、自主运动.经过仿真实验证明,该方法有效且可行.     

Long‐duration fully autonomous operation of rotorcraft unmanned aerial systems for remote‐sensing data acquisition

Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, for example, days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long‐term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision‐based precision landing on a recharging station for automated energy replenishment. High‐level autonomous decision making is implemented as a hierarchy of master and slave state machines. Vision‐based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes. We provide an extensive evaluation of the landing pad pose estimation accuracy as a function of the bundle's geometry. The functionality of the complete system is demonstrated through two indoor experiments with duration of 11 and 10.6 hr, and one outdoor experiment with a duration of 4 hr. The UAS executed 16, 48, and 22 flights, respectively, during these experiments. In the outdoor experiment, the ratio between flying to collect data and charging was 1–10, which is similar to past work in this domain. All flights were fully autonomous with no human in the loop. To our best knowledge, this is the first research publication about the long‐term outdoor operation of a quadrotor system with no human interaction.     

Design,implementation and validation of a stability model for articulated autonomous robotic systems

The use of robots in agriculture and forestry is rapidly growing thanks to the progress in sensors, controllers and mechatronics devices. Especially in hilly and mountainous terrains, the development of (semi-)autonomous systems that could travel safely on uneven terrain and perform many operations is an open field of investigation. One of the most promising mobile robot architectures is the articulated 4-wheeled system that shows an optimal steering capacity, and the possibility to adapt to uneven terrains thanks to a central passive degree of freedom. In this paper, the kinematic and (quasi-)static model for evaluating the phase I instability presented in Baker and Guzzomi(2013) has been firstly extended to allow to threat a generic articulated robotic system and to forecast the instability conditions. Then, the model and the stability conditions have been implemented in a Matlab™ simulator and validated by means of an experimental emulator. Finally, a first prototype for a mechatronic anti-overturning device is discussed.     

Truly autonomous robots: hardware design for an energy trophallactic robot

This article describes the mechanical and electrical design of a mobile robot called the CISSbot. CISSbot is abbreviated from our center’s name in Danish: Center for Indlejrede Software Systemer. The robots are designed toward truly autonomous robots in large populations through energy trophallaxes. Unlike present mobile robots, the CISSbots are energetically autonomous robots because they are not able not only to autonomously refuel by picking up batteries at a charging station, but also to share energy by exchanging batteries with other robots. The CISSbots basically consist of their own processing power, sensors, and actuators. However, to achieve the capability of battery exchange, the CISSbots need a special design of battery exchange mechanism. In this paper, we present the realization of the design in both the mechanics and the electronics of the CISSbot. Details of the battery exchange technique and power management are clarified. Finally, the article gives an outline of our future work on the CISSbots.     

Development of a sweet pepper harvesting robot

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB‐D camera, high‐end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end‐user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4‐week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.     

Development of tethered autonomous mobile robot systems for field works

This paper describes the implementation details, advantages and potential applications of autonomous tethered mobile robot systems using the 'hyper-tether' concept. Hyper-tether is a new research area on tethered connections, which provide tethering among different mobile robot types, such as a robot with the environment and a robot with humans and animals. Its basic function is to actively control the tether's tension and/or length, but it also considers tether launching, anchoring, power delivery, data communication cabling and built-in trajectory command generation capabilities. Many of these features can be efficiently applied to build a tethered mobile robot system which remotely manipulates a working tool that can be useful for land-mine detection and removal, trimming of gardens and grass cutting of wide areas (e.g. golf courses, soccer and baseball fields), spraying of agricultural chemicals, forestry and construction works, etc. In this paper, a simple prototype of hypertether's winch-tether pair and a working tool equipped with a grass cutter was constructed, and basic experiments were performed to demonstrate the validity of the proposed system.     

一种基于情感智能的机器人自主趋光行为研究

针对移动机器人的自主趋光行为问题,提出了一种基于情感智能的内发动机仿生学习机制。该学习机制以生物体感觉运动系统的学习机制为基础,包括评价环节、行为选择环节和取向环节,采用模糊神经网络构建情感模型,情感模型的输出作为评价环节的内部奖赏信号。该学习机制能够使机器人在未知环境下通过自主的学习和训练逐渐形成、发展和完善趋光行为技能,通过情感智能的作用可以增加试探成功次数和减小学习步数,仿真实验证明了该方法的有效性。     

Intelligent control system simulation of an agricultural robot

An intelligent control system for an agricultural robot which performs in an uncertain and unstructured environment was modelled as distributed, autonomous computing modules that communicate through globally accessible blackboard structures. The control architecture was implemented for a robotic harvester of melons. A CAD workstation was used to plan, model, simulate and evaluate the robot and gripper motions using 3-D, real-time animation. The intelligent control structure was verified by simulating the dynamic data flow scenarios of melon harvesting. Control algorithms were evaluated on measured melon locations.Picking time was reduced by 49% by applying the traveling salesman algorithm to define the picking sequence. Picking speeds can be increased by a continuous mode of operation. However, this decreases harvest efficiency. Therefore, an algorithm was developed to attain 100% harvest efficiency by varying the vehicle's forward speed. By comparing different motion control algorithms through animated visual simulation, the best was selected and thereby the performance improved.Journal Paper No. 13043, Agricultural Experiment Station, Purdue University, W. Lafayette, IN 47907, U.S.A. This research was supported by Grants No. US-1254-87 and US-1682-89 from BARD, the United States-Israel Binational Agricultural Research and Development Fund.