Mobile Robot

This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map,
and is particularly suitable for online loop closure detection in mobile robotics.

This paper presents an obstacle detection and avoidance of mobile robot using stereo camera for indoor environment. Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors of the camera). The algorithm uses Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping or depth maps with the left coordinate system. A pair of camera or stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool would determine the range of each obstacle detected in disparity mapping. The programming activities are using Matlab software starting from capturing images until navigation of mobile robot.

The objective of the current research is to improve the localization accuracy of a mobile robot during its navigation in known environment. The robot uses GPS receiver to rough positioning. To determine more accurate location panoramic annual image of the omnidirectional imaging tool mounted on the CCExplorer robot is processed continuously. The applied software library algorithm [3] lets us compute optical flow based on the Lucas-Kanade feature tracker in real time. The optical flow computation is implemented in pyramidal fashion, from coarse to fine resolution. Consequently, the algorithm can handle large pixel flows, while keeping a relatively small window of integration, and therefore achieve high local accuracy (of the order of 0.1 pixel). The paper summarizes the features of the PAL imaging tool, the applied computer vision method adapted to panoramic image and the results of the navigation experiment.

In this study, a path smoothing strategy is proposed for sensor-based coverage problems. Smooth paths are generated for the coverage problems considering mobile robot kinematics constraints. An open agent architecture-based control structure is used to implement the proposed approach on real robots. The algorithm is coded with C++ and implemented on P3-DX mobile robots in MobileSim simulation environments. It is

This paper presents a control strategy for the coordination of multiple mobile robots. A combination of the virtual structure and path following approaches is used to derive the formation architecture. A formation controller is proposed for the kinematic model of two-degree-of-freedom unicycle-type mobile robots. The approach is then extended to consider the formation controller by taking into account the physical

Cooperative mobile robots have recently been gaining importance within the field of robotics. It's been proven that collaborative robots can do tasks in a faster and more reliable manner than a single robot can. Additionally, recent advances in distributed coordination algorithms have dramatically improved the feasibility and suitability of cooperative approaches. This has lead researchers to develop Inter-robot communication frameworks. While several frameworks have been introduced, one of the most common is the Service-Oriented Architecture (SOA). However, the suitability of SOA for collaborative robotic communication has not been fully evaluated. In this paper we evaluate the SOA from an overhead perspective against non service-based architectures in a wireless environment. Our evaluation is based on performance suitability, which is determined by measuring throughput and delay for different numbers of mobile robots sharing real time data.

... Sensory information comes from a Laser Range Finder also developed by the author [6]. Range data are made up of hundreds of 2D relative coordinate points of targets in the field of view of the sensor camera, already filtered and grouped ... [2] JC Garcı́a, M. Marrón, JA Garcı́a, J ...

The Scale Invariant Feature Transform, SIFT, has been success- fully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new ap- proach that reduces the number,of features generated by SIFT as well as their extraction and matching,time. With the help of

Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was int...

In the past mobile robot research was often focused to various kinds of point-to-point transportation tasks. Service tasks, such as floor cleaning, require specific approaches for path planning and vehicle guidance in real indoor environments. This article discusses automatic planning of a feasible cleaning path considering a 2D-map as well as kinematic and geometric robot models. Path construction makes use of two typical motion patterns. Each pattern is defined by a sequence of subgoals indicating robot position and orientation. Results of automatic path planning are illustrated by realistic examples of typical robots and cleaning environments. Vehicle guidance includes initialization of robot location, path execution, accurate path tracking and detection of unexpected environmental changes. Path tracking is achieved by subgoal modification during cleaning motion using data from the dead-reckoning and landmark localization systems. If obstacles permanently block the preplanned path, an automatic map update and path replanning is performed. Experimental results with the mobile robot MACROBE confirm the feasibility of the developed planning and guidance system.

Using each other's knowledge and expertise in learning - what we call cooperation in learning- is one of the major existing methods to reduce the number of learning trials, which is quite crucial for real world applications. In situated systems, robots become expert in different areas due to being exposed to different situations and tasks. As a consequence, areas of expertise (AOE) of the other agents must be detected before using their knowledge, especially when the exchanged knowledge is not abstract, and simple information exchange might result in incorrect knowledge, which is the case for Q-learning agents. In this paper we introduce an approach for extraction of AOE of agents for cooperation in learning using their Q-tables. The evaluating robot uses a behavioral measure to evaluate itself, in order to find a set of states it is expert in. That set is used, then, along with a Q-table-based feature for extraction of areas of expertise of other robots by means of a classifier. Extracted areas are merged in the last stage. The proposed method is tested both in extensive simulations and in real world experiments using mobile robots. The results show effectiveness of the introduced approach, both in accurate extraction of areas of expertise and increasing the quality of the combined knowledge, even when, there are uncertainty and perceptual aliasing in the application and the robot