Behavioral Mapless Navigation Using Rings

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot

Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera

Kontes Robot Sepak Bola Indonesia (KRSBI) is an annual event for contestants to compete their design and robot engineering in the field of robot soccer. Each contestant tries to win the match by scoring a goal toward the opponent's goal. In order to score a goal, the robot needs to find the ball, locate the goal, then kick the ball toward goal. We employed an omnidirectional vision camera as a visual sensor for a robot to perceive the object’s information. We calibrated streaming images from the camera to remove the mirror distortion. Furthermore, we deployed PeleeNet as our deep learning model for object detection. We fine-tuned PeleeNet on our dataset generated from our image collection. Our experiment result showed PeleeNet had the potential for deep learning mobile platform in KRSBI as the object detection architecture. It had a perfect combination of memory efficiency, speed and accuracy

A DISTRIBUTED VISION INFRASTRUCTURE FOR MULTI-ROBOT LOCALIZATION

ABSTRAC

Monitored and controlled underwater scissor arm manipulator using Pixy camera

1120-1131Underwater vehicle manipulator system (UVMS) generally consists of a camera unit and robotic manipulator. Its main function is to replace human work in underwater manipulation tasks. Most commercially available manipulators are not designed for autonomous underwater vehicle (AUV) because the vehicle does not have sufficient power supply to drive these manipulators which are electro-hydraulically driven. A proposed solution is to invest in development of low power underwater manipulator to deepen studies in AUV. Thus, this research has an objective of developing an underwater manipulator for small scale AUV. In this research, the manipulator is used in an object recovery task. An acrylic scissor arm which is electro-mechanically driven is used as manipulator in this research. Permanent magnets are used as its end effector. A Pixy CMUcam5 vision sensor is paired with this manipulator to navigate the AUV and control the manipulator. The usage of planar pressure housing helps in reducing light refraction effect of underwater environment that may affect the sensor’s accuracy. From the simulation done using Solid Works, it is found out that type 316L stainless steel is the best choice for the manipulator developed. To evaluate the performance of the UVMS developed, a series of tests are carried out. Based on the results obtained, it is known that the system has high speed and consistency with minimum time delay between input and output. As long as an object has distinct colour signature from its background and its surrounding is clear and well illuminated, the Pixy vision sensor can detect that object regardless of the distance between the sensor and the object

Monitored And Controlled Underwater Scissor Arm Manipulator Using Pixy Camera

Underwater vehicle manipulator system (UVMS) generally consists of a camera unit and robotic manipulator. Its main function is to replace human work in underwater manipulation tasks. Most commercially available manipulators are not designed for autonomous underwater vehicle (AUV) because the vehicle does not have sufficient power supply to drive these manipulators which are electro-hydraulically driven. A proposed solution is to invest in development of low power underwater manipulator to deepen studies in AUV. Thus, this research has an objective of developing an underwater manipulator for small scale AUV. In this research, the manipulator is used in an object recovery task. An acrylic scissor arm which is electro-mechanically driven is used as manipulator in this research. Permanent magnets are used as its end effector. A Pixy CMUcam5 vision sensor is paired with this manipulator to navigate the AUV and control the manipulator. The usage of planar pressure housing helps in reducing light refraction effect of underwater environment that may affect the sensor’s accuracy. From the simulation done using Solid Works, it is found out that type 316L stainless steel is the best choice for the manipulator developed. To evaluate the performance of the UVMS developed, a series of tests are carried out. Based on the results obtained, it is known that the system has high speed and consistency with minimum time delay between input and output. As long as an object has distinct colour signature from its background and its surrounding is clear and well illuminated, the Pixy vision sensor can detect that object regardless of the distance between the sensor and the object

Dense disparity estimation for spherical images based on belief propagation

A lot of applications in computer vision are based on a pixel-labelling problem, such as stereo matching, image restoration or object segmentation. In the last years great advances have been achieved in dense disparity estimation, being Graph Cuts and Belief Propagation two of the most outstanding algorithms. Particularly, Belief Propagation has some characteristics which make it very interesting to deal with, i.e. powerful message passing and high flexibility. Furthermore, working with omnidirectional cameras, instead of standard cameras, a smaller number of images would be needed because of their wider field of view and it would allow reconstructing the 3D scene in an easier way. This project aims to adapt the Belief Propagation algorithm to spherical stereo images. In addition, as working with spherical images, we should take into account that these images will be projected on a sphere, being then the pixels at different distances between them. Thus, the project also aims to improve the algorithm adding a weighting function which considers the distance between the points on the sphere. The project contains the general description of the proposed framework as well as an analysis and evaluation of the results obtained after its implementation

Geodesic Active Fields - A Geometric Framework for Image Registration

In this paper we present a novel geometric framework called geodesic active fields for general image registration. In image registration, one looks for the underlying deformation field that best maps one image onto another. This is a classic ill-posed inverse problem, which is usually solved by adding a regularization term. Here, we propose a multiplicative coupling between the registration term and the regularization term, which turns out to be equivalent to embed the deformation field in a weighted minimal surface problem. Then, the deformation field is driven by a minimization flow toward a harmonic map corresponding to the solution of the registration problem. This proposed approach for registration shares close similarities with the well-known geodesic active contours model in image segmentation, where the segmentation term (the edge detector function) is coupled with the regularization term (the length functional) via multiplication as well. As a matter of fact, our proposed geometric model is actually the exact mathematical generalization to vector fields of the weighted length problem for curves and surfaces introduced by Caselles-Kimmel-Sapiro. The energy of the deformation field is measured with the Polyakov energy weighted by a suitable image distance, borrowed from standard registration models. We investigate three different weighting functions, the squared error and the approximated absolute error for monomodal images, and the local joint entropy for multimodal images. As compared to specialized state-of-the-art methods tailored for specific applications, our geometric framework involves important contributions. Firstly, our general formulation for registration works on any parametrizable, smooth and differentiable surface, including non-flat and multiscale images. In the latter case, multiscale images are registered at all scales simultaneously, and the relations between space and scale are intrinsically being accounted for. Secondly, this method is, to the best of our knowledge, the first re-parametrization invariant registration method introduced in the literature. Thirdly, the multiplicative coupling between the registration term, i.e. local image discrepancy, and the regularization term naturally results in a data-dependent tuning of the regularization strength. Finally, by choosing the metric on the deformation field one can freely interpolate between classic Gaussian and more interesting anisotropic, TV-like regularization