Weed control is important in agriculture and gardening but typically employed manual solutions are both time and labour consuming. By combining advanced developments in digitalization and AI (artificial intelligence), this paper presents two autonomous weed-control related processes: i) for the mapping of rumex weed in an unexplored agricultural field and ii) for the surveillance of the marked herbs. The processes employ a mobile robot equipped with a LIDAR sensor (mainly used for navigation and mapping) and a front stereo camera. A YOLOv8 object detector which is specifically trained for rumex detection is used to locate rumex plants in the camera image. For the first application of mapping, the weeds are marked on the map of the field while for the second task of surveillance, the Ant Colony Optimization looks for an efficient route connecting all those marked weeds which allows the robot to revisit and monitor them. The proposed processes are validated under both real experiments and simulation in realistic Gazebo environments.

Navigation in agricultural fields imposes various constraints on manoeuvrability, which can be tackled by using four-wheel steering (4WS) vehicles which are capable of switching between multiple steering mechanisms with distinct kinematic properties. For example, parallel positive steering (PPS) with four wheels in parallel to each other can maintain the vehicle’s heading when moving along a curve. Symmetric negative steering (SNS) with two wheels on each side sharing the same steering angle can turn with a small radius. This paper presents a controller capable of selecting and switching between the two aforementioned modes autonomously for better trajectory tracking performance with special heading requirements for agricultural applications. The controller is implemented as a Model Predictive Control (MPC) controller formulated as a mixed-integer quadratic programming (MIQP) problem for the 4WS vehicle. Practical constraints, such as limits on wheel velocities, steering angles and their rate-of-changes are taken into account. A Python implementation confirms the real-time execution capability of the controller and simulation results highlight its effectiveness.

For training deep neural networks, large data sets are required. Especially in the medical 3D ultrasound (US) image domain, the amount of available data is limited. A common method to enlarge small data sets is using data augmentation. However, simple geometric augmentation techniques like rotation or sheering can lead to unrealistic US images. In this study, a novel structure-dependent deformable augmentation method for 3D US patches is proposed. The approach is based on learning realistic motion patterns from 3D liver US images using a conditional variational auto encoder (CVAE) where the condition represents the anatomical structure type. The CVAE augmentation performance is compared to a baseline variational auto encoder (VAE). It is shown that the CVAE generates deformable augmentations 18.5 percentage points more similar to realistic deformations than the VAE. Furthermore, the method is evaluated in an expert study where experienced radiologists were asked to rate the realism of US patches. The results show that the experts could not distinguish between CVAE augmented and original US patches. Applying the proposed method in a target detection application improved the performance of a neural network by 41 %. The proposed augmentation method is able to apply realistic deformation to 3D US patches which can be used to enlarge a small data set for usage in deep learning applications.

In this paper, we present a new approach to calculate the B-spline-to-Bézler conversion matrix which converts the control points of a uniform B-spline curve into the control points of an equivalent Bézier curve. It takes into account the order of the curve intervals and hence can provide the conversion of the whole curve at once. The algorithm is implemented so that the computation time only increases proportionally with the number of control points until a constant value before getting saturated. Applications include but are not limited to the efficient usage in different optimal path planning algorithms for navigation in 2D non-convex polytopic region as being presented.

This paper firstly presents our optimal path planning algorithm within a$2\mathrm{D}$non-convex, polytopic region defined as a sequence of connected convex polytopes. The path is a B-spline curve but being parametrized with its equivalent Bézier representation. By doing this, the local convexity bound of each curve's interval is significantly tighter. Thus, it allows many more possibilities for constraining the entire curve to remain inside the region by using only linear constraints on the control points of the curve. We further guarantee the existence of the valid path by pointing out an algebraic solution. We integrate the algorithm, together with our previously published results, into the Navigation with polytopes toolbox which can be used as a global path planner, compatible with ROS navigation tools. It provides a framework for constructing a polytope map from a standard occupancy gridmap, searching for an appropriate sequence of connected polytopes and finally, planning a minimal-length path with different options on B-spline or Bézier parametrizations. The validation and comparison with existing methods are done using gridmaps collected under Gazebo simulations and real experiments.

We propose a 2D path planning algorithm in a non-convex workspace defined as a sequence of connected convex polytopes. The reference path is parameterized as a B-spline curve, which is guaranteed to entirely remain within the workspace by exploiting the local convexity property and by formulating linear constraints on the control points of the B-spline. The novelties of the paper lie in the use of the equivalent Bézier representation of the B-spline curve, which significantly reduces the conservatism in the local convexity bound and in the integration of these constraints into a convex quadratic optimization problem, which minimizes the curve length. The algorithm is successfully validated in both simulations and experiments, by providing obstacle-free reference paths on real occupancy grid maps obtained from the laser scan data of a mobile robot platform.