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Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder–decoder...
We present an anomaly detection system based on an autonomous robot performing a patrol task. Using a generative adversarial network (GAN), we compare the robot's current view with a learned model of normality. Our preliminary experimental results show that the approach is well suited for anomaly detection, providing efficient results with a low false positive rate.
Due to its ability to learn complex behaviors in high-dimensional state-action spaces, deep reinforcement learning algorithms have attracted much interest in the robotics community. For a practical reinforcement learning implementation on a robot, it has to be provided with an informative reward signal that makes it easy to discriminate the values of nearby states. To address this issue, prior information,...
Though the classical robotics is highly proficient in accomplishing a lot of complex tasks, still it is far from exhibiting the human-like natural intelligence in terms of flexibility and reliability to work in dynamic scenarios. In order to render these qualities in the robots, reinforcement learning could prove to be quite effective. By employing learning based training provided by reinforcement...
Recognition of dominant planes is an important task used in areas such as robot navigation, augmented reality, 3D reconstruction, among others. There are several approaches for recognizing planar structures, however, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds associated with the camera...
Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict...
In recent decades, face recognition has been a hot topic in image processing, pattern recognition and other areas. To solve the issue of the lack of feature information brought by single training face sample, this paper proposed a method on homography transformation, which is about producing virtual multi-pose face by a front view image of face. First, assume that the single sample face is positive...
Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific...
Dividing the control authority of the slave robot among operators, based on the available information to them and their controlling strategy, is absolutely essential for cooperative teleoperation systems. Although there have been many studies on this, most of the analysis were limited to fixed authority and its effect on stability. None of the research has discussed how to allocate the control authority...
The LDA-based face recognition using face images by actual distance as training images showed good performance. However, it causes user inconvenience as it requires the user to move multiple distance in person to acquire face images for initial user registration. In this paper, LDA-based face recognition applicable to robotic environments is proposed. The proposed method can get face images by distance...
In this paper, we present a method to apply intrinsic motivation for improving visuomotor learning of robot's arm with external object in view. Multiple Timescales Recurrent Neural Network (MTRNN) is utilized for learning the robot arm/external object dynamics. Training of MTRNN is done using the Back Propagation Through Time (BPTT) algorithm. BPTT algorithm is modified as follows. 1. Evaluate predictability...
An electric wheelchair is basically acknowledged for mobility improvement in disability patients. In some cases, their hand could not well function. They may tire easy before reaching to the desired destination. Furthermore, the safety is the most concerned issue for wheelchair control in disability patients. Therefore, this work tries to develop the prototype of the automated navigation system that...
An unsupervised attention-path planning algorithm is proposed and applied to large unknown area classification with small field-of-view cameras. Attention-path planning is formulated as the sequential feature selection problem that greedily finds a sequence of attentions to obtain more informative observations, yielding faster training and higher accuracies. In order to find the near-optimal attention-path,...
In this paper, we present a novel interface for teleoperating ground vehicles. Obstacle avoidance with ground vehicles demands a high level of operator attention, typically distracting from the primary mission. The Ambient Obstacle Avoidance (AOA) was designed to allow operators to effectively perform a primary task, such as search, while still effectively avoiding obstacles. The AOA wraps around...
Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g...
Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points,...
Continuum robots offer significant advantages for surgical intervention due to their down-scalability, dexterity, and structural flexibility. While structural compliance offers a passive way to guard against trauma, it necessitates robust methods for online estimation of the robot configuration in order to enable precise position and manipulation control. In this paper, we address the pose estimation...
Detecting visual changes in environments is an important computation with many applications in robotics and computer vision. Security cameras, remotely operated vehicles, and sentry robots could all benefit from robust change detection capability. We conjecture that if one has a mobile camera system the number of visual scenes that are experienced is limited (compared to the space of all possible...
The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation...
This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as...
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