Trajectory tracking in the orientation space utilizing unit quaternions yields non linear error dynamics as opposed to Cartesian position. In this work, we study trajectory tracking in the orientation space utilizing the most popular quaternion error representations and angular velocity errors. By selecting error functions carefully we show exponential convergence in a region of attraction containing large initial errors. We further show that under certain conditions frequently encountered in practice, the formulation respecting the geometric characteristics of the quaternion manifold and its tangent space yields linear tracking dynamics allowing us to guarantee a desired tracking performance by gain selection without tuning. Simulation and experimental results are provided.
In the last years, the use of robotics technology in agriculture is constantly increasing. Robotic platforms are the application of automation and robotics in agriculture field to relieve manual and heavy tasks from workers. These devices have already started to transform many aspects of agriculture and are hesitantly finding their way to the market. Therefore, robotic solutions which can provide alternative routes to weed management may provide a transformational enabling technology to mitigate against biotic and abiotic stresses on crop production, for example automatic weeding robots are the preferable substitute for chemical herbicide to remove weeds. One of the most impacting abiotic factors in agriculture are weeds, causing important yield loss in every cultivation. Integrated weed management coupled with the use of robotic platforms (UGVs), allows the effectively weed management, us a beneficial methodology for the environment. The detection of weed spots in a cultivation can be achieved by combining image acquisition by UGV and further processing by specific algorithms. These algorithms can be used to weeds control by autonomous robotic systems via mechanical procedures or herbicide spray.
The weed management is one of the major challenges in viticulture, as long as weeds can cause significant yield losses and severe competition to vines. One of the cheapest and effectiveness method remains the weed control with chemicals; however, several adverse effects and risks may arise. Different methods like tillage, thermal method, mulching and cover crops can be included in weed control strategy, depending on the environmental conditions, soil and crop. As it is known, the mechanical methods are the most cost-effective weed management methods in vineyards.
Monitoring weed in different vineyards will provide a useful database for understanding the weed management practices. In this direction, this paper presents a system for a weeding detection robot. The objective is to be enabling the weed detection robot to navigate autonomously between the inter-row spaces of crop for automatic weed control, reduce labor cost and time. In this paper, various of image processing techniques with the implementation of an RGB-D camera was examined in order to: i) detect the path between two rows of vineyard and ii) allocate the weeds based on various a priori characteristics. As a pre-processing state, the real time data from the RGB-D camera transformed into different color spaces in order to denote the noise that could occur. Subsequently, the examined algorithms and techniques tested in numerous of aggregated data from real vineyards with different levels of weed development. Finally, the developed algorithm tested by implementing it on a UGV platform with promising results.
Many visual scene understanding applications, especially in visual servoing settings, may require high quality object mask predictions for the accurate undertaking of various robotic tasks. In this work we investigate a setting where separate instance labels for all objects under view are required, but the available instance segmentation methods produce object masks inferior to a semantic segmentation algorithm. Motivated by the need to add instance label information to the higher fidelity semantic segmentation output, we propose an anisotropic label diffusion algorithm that propagates instance labels predicted by an instance segmentation algorithm inside the semantic segmentation masks. Our method leverages local topological and color information to propagate the instance labels, and is guaranteed to preserve the semantic segmentation mask. We evaluate our method on a challenging grape bunch detection dataset, and report experimental results that showcase the applicability of our method.
The problem of motion planning in obstacle cluttered environments is an important task in robotics. In the literature several methodologies exist to address the problem. In this work we consider using the feedback-based approach, where the solution comes from designing a controller capable of guaranteeing trajectory tracking with obstacle avoidance. Commonly, all respective studies consider simplified robot dynamics, which is usually insufficient in practical applications. In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot’s end-effector will always operate in a safe distance from the moving obstacle’s surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach. Experimental studies conducted on a KUKA LWR4+ robotic manipulator clarify and verify the proposed control scheme.
Spectroscopy is a widespread technique used in many scientific fields such as in the food production. The use of hyperspectral data and specifically in the visible and near infrared (VNIR) and in the short-wave infrared (SWIR) regions in grape production is of great interest. Due to its fine spectral resolution, hyperspectral analysis can contribute to both fruit monitoring and quality control at all stages of maturity with a simple and inexpensive way. This work presents an application of a contact probe spectrometer that covers the VNIR–SWIR spectrum (350–2500 nm) for the quantitative estimation of the wine grapes’ ripeness. A total of 110 samples of grape vine Syrah (Vitis vinifera Syrah) variety were collected over the 2020 harvest and pre-harvest seasons from Ktima Gerovassiliou located in Northern Greece. Their total soluble solids content (oBrix) was measured in-situ using a refractometer. Two different machine learning algorithms, namely partial least square regression (PLS) and random forest (RF) were applied along with several spectral pre-processing methods in order to predict the oBrix content from the VNIR–SWIR hyperspectral data. Additionally, the most important features of the spectrum were identified, as indicated by the most accurate models. The performance of the different models was examined in terms of the following metrics: coefficient of the determination (R2), root mean square error (RMSE) and ratio of performance to interquartile distance (RPIQ). The values of R2 = 0.90, RMSE =1.51 and RPIQ = 4.41 for PLS and 0.92, 1.34, 4.96 for RF respectively, indicate that by using a portable VNIR–SWIR spectrometer it is possible to estimate the wine grape maturity in-situ.
In this work, we present a comparative analysis of the trajectories estimated from various Simultaneous Localization and Mapping (SLAM) systems in a simulation environment for vineyards. Vineyard environment is challenging for SLAM methods, due to visual appearance changes over time, uneven terrain, and repeated visual patterns. For this reason, we created a simulation environment specifically for vineyards to help studying SLAM systems in such a challenging environment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping, ORBSLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in this study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2). The results show good and encouraging performance of RTAB-MAP in such an environment.
Mobile bimanual manipulation in a dynamic and uncertain environment requires the continuous and fast adjustment of the robot motion for the satisfaction of the constraints imposed by the task, the robot itself and the environment. We formulate the pick-and-place task as a sequence of mobile manipulation tasks with a combination of relative, global and local targets. Distributed distance sensors on the robot are utilized to sense the surroundings and facilitate collision avoidance with dynamic and static obstacles. We propose an approach to kinematically control the robot by solving a priority constrained optimization problem online. Experimental results on the YuMi bimanual robot mounted on the Ridgeback mobile platform validate the performance of the proposed approach.
Prehensile robotic grasping of a target object in clutter is challenging because, in such conditions, the target touches other objects, resulting to the lack of collision free grasp affordances. To address this problem, we propose a modular reinforcement learning method which uses continuous actions to totally singulate the target object from its surrounding clutter. A high level policy selects between pushing primitives, which are learned separately. Prior knowledge is effectively incorporated into learning, through action primitives and feature selection, increasing sample efficiency. Experiments demonstrate that the proposed method considerably outperforms the state-of-the-art methods in the singulation task. Furthermore, although training is performed in simulation the learned policy is robustly transferred to a real environment without a significant drop in success rate. Finally, singulation tasks in different environments are addressed by easily adding a new primitive and by retraining only the high level policy
Extracting a known target object from a pile of other objects in a cluttered environment is a challenging robotic manipulation task encountered in many robotic applications. In such conditions, the target object touches or is covered by adjacent obstacle objects, thus rendering traditional grasping techniques ineffective. In this paper, we propose a pushing policy aiming at singulating the target object from its surrounding clutter, by means of lateral pushing movements of both the neighboring objects and the target object until sufficient 'grasping room' has been achieved. To achieve the above goal we employ reinforcement learning and particularly Deep Q-learning (DQN) to learn optimal push policies by trial and error. A novel Split DQN is proposed to improve the learning rate and increase the modularity of the algorithm. Experiments show that although learning is performed in a simulated environment the transfer of learned policies to a real environment is effective thanks to robust feature selection. Finally we demonstrate that the modularity of the algorithm allows the addition of extra primitives without retraining the model from scratch.