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.

This paper presents a novel control architecture for controlling a moving remote center of motion in addition to the end-effector motion during robotic surgery. In minimally invasive surgery, it is common to require that the point at which the robot enters the body, called the incision point or the trocar, does not allow for any lateral motion. It is generally considered that no motion should be applied to this point in order to avoid inflicting damage to the patient's skin. However, in surgery, the patient's body may be moving, for example due to breathing or the beating of the heart. In order to compensate for this motion-or if we for some other reason want to leverage the possible motion of the incision point to improve performance in any other way-we derive a new framework which allows us to actively control the motion both at the incision point and the end effector. The novelty of the approach lies in the possibility of controlling both the incision point and the end effector to follow a trajectory, and that we find a Jacobian matrix that satisfies the velocity constraints in both the end-effector and the incision point frames. This allows us to formulate a framework that is not only suited for control, but also for analyzing the condition number of the Jacobian and avoid any singular configurations that may arise either as a result of the constrained motion or the manipulator geometry. The approach is verified experimentally on a redundant industrial manipulator.

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