In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The electronics of the device is also discussed, since Agri.q should be ready to send-receive data to move autonomously or to be remotely controlled by means of dedicated processing units and transmitter-receiver modules. This paper collects all these elements and shows the advances of the previous works, describing the design process of the mechatronic system and showing the realization phase, whose outcome is the physical prototype.

This work describes a layout to carry out a demonstrative assembly task, during which a collaborative robot performs pick-and-place tasks to supply an operator the parts that he/she has to assemble. In this scenario, the robot and operator share the workspace and a real time collision avoidance algorithm is implemented to modify the planned trajectories of the robot avoiding any collision with the human worker. The movements of the operator are tracked by two Microsoft Kinect v2 sensors to overcome problems related with occlusions and poor perception of a single camera. The data obtained by the two Kinect sensors are combined and then given as input to the collision avoidance algorithm. The experimental results show the effectiveness of the collision avoidance algorithm and the significant gain in terms of task times that the highest level of human-robot collaboration can bring.

This paper presents a collision avoidance system based on vision sensors that is suitable for collaborative robotics scenarios. In fact, collaborative robotics foresees the possibility that humans and robots share the same workspace, so the safety of the human operators must be ensured. The collision avoidance algorithm here presented can modify the trajectory of the robot in order to avoid any collisions with a human operator. A fundamental element for the algorithm is the relative position between robot and human. In this work, the information of the position of a human operator is obtained by Microsoft Kinect sensor in the form of a point cloud. Two Microsoft Kinect are used and their point cloud data is merged to overcome the problems related to the possible occlusions of the sensors, obtaining a more reliable point cloud. Each Kinect works with a dedicated PC and the two PCs communicate via ethernet network in a master-slave mode. The layout of the acquiring system is described and the functions used for the communication between the PCs and the manipulation of the point clouds are presented. Results of simulation tests made to verify the performances of the system and collision avoidance based on point cloud compared with convex mesh are reported and discussed.

Collaborative robotics implies the possibility for robots and humans to cooperate to perform a common task. A fundamental element to make collaboration possible is to ensure the safety of the human operators. In this paper a collision avoidance algorithm designed for a robotic redundant task is presented. This algorithm permits the robot to avoid collisions with obstacles (e.g. human operators) and in the same time to achieve the planned task when possible. The algorithm was tested with a UR3 robot of Universal Robots company, considering only tool centre point (TCP) position in order to obtain kinematic redundancy. The robot is controlled by an external PC that communicates with the controller of the robot via Ethernet network. The communication and control algorithms are written in the Matlab environment. The PC-UR communication functions permit to send joint velocities commands from the PC to the controller of the robot and feedback data from the UR to the PC. Control functions estimate the distances between an obstacle (whose position was acquired by a system of motion capture) and the robot and calculate the needed joints velocities to avoid any collision. The functions developed in Matlab will be explained in detail. Results of the experimental tests conducted to verify the effectiveness of the collision avoidance system are reported.

Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. However, they are limited by the use of only one global attractor at the goal. This paper introduces and evaluates experimentally a novel technique that enhances the artificial potential field method with local attractors. Local attractors can be positioned around the obstacle so as to guide the robot detouring through preferred regions. Thus, the side the robot will pass by can be determined in advance, making the collision-free path predictable. The technique is formulated by modelling local attractors as optimal inflections, i.e., regions that do not show local minima, which coexist with the potential field generated by the obstacle and the global attractor. The method is validated using a laboratory setup that employs a camera and markers to track the poses of the robot, the obstacle, and the target. A series of experiments are conducted to examine the effect of the local attractor under different test conditions, obtained by varying the obstacle pose, the attraction intensity, and the robot velocity. The experimental results demonstrate the effectiveness of the proposed technique and highlight the aspects that require further investigation for its improvement and application.