Constrained Prioritized 3T2R Task Control for Robotic Agricultural Spraying

In this paper, we present a solution for robot arm-controlled agricultural spraying, handling the spraying task as a constrained prioritized 3T2R task. 3T2R tasks in robot manipulation consist of three translational and two rotational degrees of freedom, and are frequently used when the end-effector is axis-symmetric. The solution presented in this paper introduces a prioritization between the translational and rotational degrees of freedom of the 3T2R task, and we discuss the utility of this kind of approach for both velocity and positional inverse kinematics, which relate to continuous and selective agricultural spraying applications respectively.

Autonomous, Mobile Manipulation in a Wall-building Scenario: Team LARICS at MBZIRC 2020

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.