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In this work, we present a new robust vision-based controller for wheeled mobile robots, equipped with a fixed monocular camera, to perform autonomous navigation in agricultural fields accurately. Here, we consider the existence of uncertainties in the parameters of the robot-camera system and external disturbances caused by high driving velocities, sparse plants, and terrain unevenness. Then, we design a robust image-based visual servoing (rIBVS) approach based on the sliding mode control (SMC) method for robot motion stabilization, even under the presence of such inaccuracies and perturbations. The vision-based controller, based on column and row primitives, is slightly modified to include a robustness term into the original feedback control laws to ensure successful row crop reaching and following tasks. We employ the Lyapunov stability theory to verify the stability and robustness properties of the overall closed-loop system. 3D computer simulations are carried out in the ROS-Gazebo platform, an open-source robotics simulator, using a differential-drive mobile robot (DDMR) in an ad-hoc developed row crop environment to illustrate the effectiveness and feasibility of the proposed control methodology.

Nowadays, legged mobile robots have increased the interest of the robotics community because such mechanisms have higher versatility and autonomy compared to wheeled mobile robots. Although single-leg or multi-leg mechanisms can cross any terrain, some disadvantages are related to their increased complexity in mechanical design, modeling and control, and higher power consumption. A first case study for the balance and motion planning problem is the hopping robot, which is a nonholonomic system whose motion dynamics of each hopping cycle can be split into flight and stance phases. In this work, we consider the modeling and control design of a one-legged hopping robot in the flight phase by using the sliding mode approach, due to its well-known ability to deal with parametric uncertainties and nonlinear disturbances. Then, two nonlinear controllers are designed and implemented to automatically stabilize the robot joints during the flight in the presence of perturbations caused by the neglected high-order nonlinear terms in the modeling process, unmodeled dynamics and measurement noise. The Lyapunov stability theory is used to demonstrate the stability and robustness properties of the overall closed-loop control systems. Numerical simulations and a comparative analysis are provided to illustrate the performance and feasibility of the proposed control methodology.

This work presents a new robust image-based visual servoing (rIBVS) approach for wheeled mobile robots (WMRs) endowed with a single monocular camera to carry out autonomous navigation in row crop fields. Then, we design a robust vision-based controller by using the super-twisting algorithm (STA) approach to stabilize the robot motion in the presence of model inaccuracies caused by imperfect camera calibration, and trajectory perturbations due to different plant distributions and high robot driving velocities. The rIBVS approach switches between column and row visual primitives extracted from the images, allowing WMRs to execute the navigation task in two phases: the crop row reaching and the crop row following. To illustrate the effectiveness and feasibility of the proposed control methodology, 3D computer simulations are executed in the ROS-Gazebo simulator using a differential-drive mobile robot (DDMR) navigating autonomously in an ad-hoc developed row crop agricultural environment.

This paper presents a simulation study of the balancing problem for a monopod robot in which the lower body (the leg) has been modified to include a passively spring-loaded prismatic joint. Such a mechanism can move by hopping but can also stand and balance on a single point. We aim to investigate the extent to which a balance controller can deal with the large values and rapid changes in the spring-damper forces, while controlling the absolute positions and orientations of its parts and balancing on one leg. It can be shown that a good performance is achieved if the spring-loaded joint is instrumented and calibrated so that its position and velocity, as well as the stiffness and damping coefficients, are considered when calculating the controller state variables. We also demonstrate the effectiveness of the balance controller by adding a high-order sliding mode (HOSM) observer based on the finite-time algorithm for robust parameter estimation of the stiffness and damping coefficients. The stability analysis and convergence proofs are presented based on the Lyapunov stability theory. Numerical simulations are included to illustrate the performance and feasibility of the proposed methodology.

In farming systems, harvesting operations are tedious, time- and resource-consuming tasks. Deploying a fleet of autonomous robots to work alongside farmworkers may provide vast productivity and logistics benefits. In this context, an intelligent robotic system should monitor human behavior, identify the ongoing activities and anticipate the worker's needs. Unlike other application areas, such as warehouses and factories, research on human behavior recognition in agriculture is still in its infancy and has few case studies. Thus, there is a need for developing a fully integrated human activity recognition (HAR) methodology applied for agricultural operations in production fields. In this work, the main contribution consists of creating a benchmark framework of video-based human pickers detection, classifying their activities and corresponding motion direction, to serve in harvesting operations in different agricultural scenarios. Our solution uses the combination of a Mask Region-based Convolutional Neural Network (Mask R-CNN) for object detection and optical flow for motion estimation with a newly added statistical attribute of flow motion descriptors, named as Correlation Sensitivity (CS). A classification criterion is defined based on the analysis of the Kernel Density Estimation (KDE) technique and the K-Means clustering algorithm. Both methods are evaluated upon in-house collected datasets from different environments like strawberry polytunnels and apple tree orchards. The proposed benchmark framework is quantitatively analyzed using measures of sensitivity, specificity, and accuracy and shows satisfactory results amidst various dataset challenges such as multi-foreground objects, lighting variation, blur, and occlusions.

Agriculture is considered the economic basis of countries around the globe, and the development of new technologies contributes to the harvesting efficiency. Autonomous vehicles are used in farms for seeding, harvesting and tasks like pesticide application. However, one of the main issues of any plantation is insect pest and disease identification, essential for pest control and maintenance of healthy plants. This work presents and compares three methods for insect pest identification using computer vision: Deep Convolutional Neural Network (DCNN), as a baseline; Hierarchical Deep Convolutional Neural Network (HD-CNN), in order to improve prediction of similar classes; and Pixel-wise Semantic Segmentation Network (SegNet). They were tested for two kinds of culture, soybean and cotton. SegNet outperformed both approaches by a wide margin: the methods had respective accuracies of 70.14% DCNN, 74.70% HD-CNN and 93.30% SegNet.

In recent years, the use of mobile robots in agriculture has increased significantly because of their capability to carry out agricultural tasks in a safe and efficient manner, with limited or without human intervention. Crop monitoring and inspection have become an important part of precision agriculture, supporting farmers in the management of insect pests, weeds and diseases in order to reduce costs and losses. In this work, we present the design and development of an autonomous mobile robot, conceived to perform routine monitoring and inspection tasks on soy and cotton crops. The robot design is similar to a differential-drive vehicle due to its simplicity of construction, modeling and control. The autonomous navigation is successfully carried out via the use of odometry and cameras properly attached to the robot structure. Preliminary field tests with the prototype operating on a cotton farm are presented to show the performance and feasibility of the electro-mechanical design for navigation in row crops.

In this work, we address the modelling and control design of a wheeled mobile robot capable of performing autonomous navigation tasks in agricultural fields. The methodology is based on the kinematics approach due to its wellknown ability to ensure satisfactory performance when the robot motions are carried out with low velocities and slow accelerations. Two control strategies are used for stabilization and trajectory tracking purposes: the first scheme is based on a fuzzy logic algorithm and considers the regulation problem of the robot position in Cartesian space; the second scheme is based on a visual servoing algorithm and considers the tracking problem of a straight line by using a constant linear velocity in Cartesian space and the position error in image space. Simulation results are presented to illustrate the effectiveness and feasibility of the fuzzy logic approach. Experimental tests with an agricultural mobile robot are carried out to verify and validate the visual servoing approach for cotton crop monitoring.

This work proposes a new strategy for autonomous navigation of mobile robots in sugarcane plantations based on thermal imaging. Unlike ordinary agricultural fields, sugarcane farms are generally vast and accommodates numerous arrangements of row crop tunnels, which are very tall, dense and hard-to-access. Moreover, sugarcane crops lie in harsh regions, which hinder the logistics for employing staff and heavy machinery for mapping, monitoring, and sampling. One solution for this problem is TIBA (Tankette for Intelligent BioEnergy Agriculture), a low-cost skid-steering mobile robot capable of infiltrating the crop tunnels with several sensing/sampling systems. The project concept is to reduce the product cost for making the deployment of a robot swarm feasible over a larger area. A prototype was built and tested in a bioenergy farm in order to improve the understanding of the environment and bring about the challenges for the next development steps. The major problem is the navigation through the crop tunnels, since most of the developed systems are suitable for open field operations and employ laser scanners and/or GPS/IMU, which in general are expensive technologies. In this context, we propose a low-cost solution based on infrared (IR) thermal imaging. IR cameras are simple and inexpensive devices, which do not pose risks to the user health, unlike laser-based sensors. This idea was highly motivated by the data collected in the field, which have shown a significant temperature difference between the ground and the crop. From the image analysis, it is possible to clearly visualize a distinguishable corridor and, consequently, generate a straight path for the robot to follow by using computationally efficient approaches. A rigorous analysis of the collected thermal data, numerical simulations and preliminary experiments in the real environment were included to illustrate the efficiency and feasibility of the proposed navigation methodology.

The majority of works in line of sight (LOS) stabilization and tracking using inertially stabilized platforms (ISPs) apply simple linear controllers to achieve the required performance. However, these techniques do not provide ideal disturbance rejection, which is a desired characteristic for these types of systems in the context of high-accuracy applications. In this work, we propose a Sliding Mode Control (SMC) strategy for both stabilization and target tracking for a 3- DoF ISP. Both state and output feedback cases are considered. In the latter case, a High-Order Sliding Mode Observer (HOSMO) is proposed for the estimation of the ISP joint velocities. In each case, two Super-Twisting Controllers (STC) are employed in a cascade topology, providing finite-time convergence to the sliding variables. The inner controller ideally rejects the dynamic disturbances acting on the ISP joints, reducing the system to an ideal double integrator. The outer controller ensures target tracking in quaternion space, ideally rejecting all remaining kinematic disturbances. Numerical simulations show the efficiency and performance of the proposed methodology.

In this work, the bilateral teleoperation of a Multi-Master Multi-Slave (MM/MS) robotic system able to perform object manipulation tasks is presented. A multi-agent formation control strategy is considered to control the object's velocity. The velocity and interaction forces are provided by the master robots while the object is manipulated with a bounded force by the slave robots. Stability and transparency are guaranteed by two-layer passivity strategy using the energy tanks approach. The stability and convergence analysis is carried out by using the Lyapunov stability theory and the passivity formalism. Numerical simulations are included to illustrate the performance and effectiveness of the proposed control scheme.

In this work, we address the inverse kinematics problem for parallel mechanisms using a differential kinematics approach based on the recently proposed Filtered Inverse method. The key idea behind the method is to employ an inversion algorithm which dynamically estimates the inverse of the Jacobian matrix, rather than using the instantaneous calculation of the true inverse. An interesting property of this novel solution is its ability to cope with kinematic singularities as well as the mechanical joint limits. The stability and convergence analysis of the inversion algorithm is carried out by using the Lyapunov stability theory. Simulation results, obtained with a slider-crank mechanism, are shown to illustrate the performance and feasibility of the proposed scheme.

In this work, we address the problem of kinematic modeling and control design of a multifingered robot hand. Each robot finger is modeled as a parallel manipulator and its kinematic constraints are computed from empirical analysis due to the inherent mechanical complexity of the mechanism. The motion control problem for a grasped object is solved by using the kinematic control approach, which is able to ensure the asymptotic stability of the output tracking error and the prehension of the object. The kinematics-based control scheme is designed to include the contact model in the hand Jacobian matrix, allowing for the simultaneous control of the object position as well as the relative position between the fingers. Experiments are carried out with a three-fingered robot hand executing grasping and manipulation tasks of soft objects. Practical results are shown to illustrate the performance and effectiveness of the proposed methodology.

In this work, we present a control and obstacle collision avoidance method for redundant robot manipulators operating in partially structured environments in the presence of humans. The control algorithm is based on the concept of artificial potential fields and it uses the pseudo-inverse of the Jacobian matrix with a weighting factor for the mechanical joint limits, taking advantage of the robot redundancy for the purpose of obstacle avoidance and control goal achievement. The detection algorithm uses a depth sensor based on the structured light to obtain a 2-1/2-D description of the surroundings from a point cloud. Repulsive fields are created around the detected obstacles, allowing for the robot to perform the task of interest without collisions. A filtering methodology based on geometric elements is presented to filter the RGB-D scene captured by the depth sensor, eliminating the robot body and the obstacles located outside its workspace. Experimental results, obtained with a Motoman DIA10 robot and a Microsoft KinectTM, illustrate the feasibility of the proposed scheme.

This paper presents a kinematic control scheme for robot manipulators based on an algorithm that dynamically estimates an inverse of the Jacobian matrix. An interesting property of this algorithm is its ability to deal with the problem of kinematic singularities. The output of the algorithm can be interpreted as the filtered inverse of the Jacobian matrix. A case study of a 3-DoF non-redundant manipulator is presented. Some simulation results illustrate the performance of the proposed methodology.

This work addresses the hybrid position-force control problem for robot manipulators performing inter-action tasks on constraint surfaces with regular curvature. A novel hybrid control law, based on an orientation-dependent term, is proposed to circumvent the performance degradation owing to modeling uncertainty, particularly with respect to the geometry of the constraint surface. As in our previous work, the effect of the robot dynamics can be included by using a cascade control strategy. In contrast, instead of only position, the presented approach provides complete robot posture control. Simulation results illustrate the performance and feasibility of the proposed control scheme.

An output-feedback sliding mode controller using monitoring functions was recently introduced for linear uncertain single-input-single-output (SISO) systems with unknown control direction. Here, a generalization is developed for multivariable systems with strong nonlinearities. The monitoring scheme is extended to handle the uncertainty of the plant high frequency gain matrix K p . Our strategy provides global stability properties and exact output tracking. Experimental results with a robotics visual servoing system, using a fixed but uncalibrated camera, illustrate the robustness and practical viability of the proposed scheme.

This work considers a hybrid force and vision control system for robotic manipulators using a force sensor and a fixed uncalibrated camera. A method is proposed to combine direct force control and adaptive visual servoing to perform tasks on unknown smooth surfaces, in the presence of uncertainties in the camera-robot system parameters. The considered task involves the visual tracking of a moving target, while the end-effector tip exerts a controlled contact force on the surface. Simulation results are presented to illustrate the performance of the proposed scheme