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Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact...
In this paper we propose a new approach to recovering the relative position and orientation of a pair of platforms moving on the plane by fusing the kinds of estimates provided by visual odometry systems with distance measurements obtained from ranging radio systems. By combining these two complementary sources of information we are able to provide estimates that could not be obtained using either...
Robust, scalable localization unlocks path-planning, obstacle avoidance as well as manipulation and thus is a core competency for many robotic applications. However, as we leave the lab and move out in the world, models of the environment no longer span distances of meters but kilometers in length. Now, gigabytes instead of megabytes of memory are required to hold the model of the environment required...
This paper presents a method to align a virtual plane with a real plane in an augmented reality environment. The method addresses the challenge of configuring a planar virtual fixture during the teleoperation of a robot in a visually challenging environment. The method assists the operator to manually align the virtual plane with the real plane by providing visual cues by dynamically texturing the...
A framework is developed to construct computational models of the human motor system (HMS) using iterative learning control (ILC) update structures. Optimal models of movement are introduced using a cost function that is motivated by learned human motion results. Three general ILC update structures are derived that each generate the required limiting solution using different forms of experimental...
The task of visual vocabulary construction plays an important role in the bag-of-words based pattern analysis and robotic applications. A discriminative vocabulary generation in unsupervised case is an open issue for reducing perceptual aliasing in image matching based applications. In this paper, we present a scheme to evaluate the discriminative power of each visual word quantitatively in terms...
We present a Bayesian approach to calibrating the hand-eye kinematics of an anthropomorphic robot. In our approach, the robot perceives the pose of its end-effector with its head-mounted camera through visual markers attached to its end-effector. It collects training observations at several configurations of its 7-DoF arm and 2-DoF neck which are subsequently used for an optimization in a batch process...
We introduce a way to program adaptive reactive systems, using behavioral, scenario-based programming. Extending the semantics of live sequence charts with reinforcements allows the programmer not only to specify what the system should do or must not do, but also what it should try to do, in an intuitive and incremental way. By integrating scenario-based programs with reinforcement learning methods,...
Domestic tasks such as grasping or navigation for robotic systems can be supported by vision. However, the environment provides a vast amount of visual information and concentrating on the information related to the task being undertaken is an important job. Active vision is an approach that provides such a filtering mechanism by using camera movements to bring relevant information into the focus...
The main aim of this study is managing robot tasks to minimize the deviation between the resource requirements and stated desirable levels. Some improved adaptive methods about step length are proposed in the artificial fish swarm algorithm (AFSA). In this study resource leveling methods are used to solve task scheduling problems in autonomous multi robot group. Robots are considered as resources...
This paper introduces a use of iterative learning control to improve a performance of arm robot in repeatable task. Iterative learning control aims to converge the repeating distance error produced from arm robot by adjusting the control input in the current run to feedback control system based on error observed from the previous run. The feedback signal to the system is experimentally obtained from...
This paper deals with the problem of object reconstruction for visual search by a humanoid robot. Three problems necessary to achieve the behavior autonomously are considered: full-body motion generation according to a camera pose, general object representation for visual recognition and pose estimation, and far-away visual detection of an object. First we deal with the problem of generating full...
The proposed relational fuzzy clustering method called FRFP (fuzzy relational fixed point) is not based on minimizing an objective function, as in traditional methods, but rather on determining a fixed point of a function of the desired membership matrix with the proximity matrix as parameter. The proposed method is compared to other relational clustering methods including NERFCM, Rouben's method...
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