The application of agricultural machinery in precision agriculture has experienced an increase in investment and research due to the use of robotics applications in the machinery design and task executions. Precision autonomous farming is the operation, guidance, and control of autonomous machines to carry out agricultural tasks. It motivates agricultural robotics. It is expected that, in the near future, autonomous vehicles will be at the heart of all precision agriculture applications [1]. The goal of agricultural robotics is more than just the application of robotics technologies to agriculture. Currently, most of the automatic agricultural vehicles used for weed detection, agrochemical dispersal, terrain leveling, irrigation, etc. are manned. An autonomous performance of such vehicles will allow for the continuous supervision of the field, since information regarding the environment can be autonomously acquired, and the vehicle can then perform its task accordingly.

In this paper, the control problem of camera-in-hand robotic systems is considered. In this approach, a camera is mounted on the robot, usually at the hand, which provides an image of objects located in the robot environment. The aim of this approach is to move the robot arm in such a way that the image of the objects attains the desired locations. We propose a simple image-based direct visual servo controller which requires knowledge of the objects' depths, but it does not need to use the inverse kinematics and the inverse Jacobian matrix. By invoking the Lyapunov direct method, we show that the overall closed-loop system is stable and, under mild conditions on the Jacobian, local asymptotic stability is guaranteed. Experiments with a two degrees-of-freedom direct-drive manipulator are presented to illustrate the controller's performance.

Background: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Results: Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Conclusion: Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

A new trajectory tracking controller with collision avoidance is proposed in this paper for unmanned aerial vehicle (UAV) navigation. A positive potential function is designed to take into account the movement of obstacles. Thus, the controller with potential function guarantees that the UAV moves through areas of potentials close to zero to ensure safe navigation in dynamic and unknown environments. Such a controller was designed with hierarchical objectives using a behavioral-based approach. A null-space-based controller is adopted, whose main objective is to ensure that the collision avoidance is achieved, whereas other objectives are projected onto the null space. Collision avoidance and trajectory tracking controllers generate reference velocities sent to a dynamic compensator to guarantee the tracking of such velocities, thus characterizing a cascade controller. Stability of the entire closed-loop nonlinear system is demonstrated through Lyapunov's theory. A low-cost indoor framework with just one RGB-D sensor, which is a combination of a RGB (red-green-blue) camera with a depth sensor based on infrared light was used to estimate the positions of the UAV and obstacles. Simulation and experiments are run using a Parrot AR.Drone quadrotor and considering a person as a dynamic obstacle for an AR.Drone quadrotor, and some of their results are reported to validate the proposed controller.