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This paper describes a basic passive vs. aggressive defense model, and analyzes it in terms of defense strategies against an intelligent enemy. In response to varying combinations of passive and aggressive defense, we assume that the enemy can up- or down-regulate recruitment activity. This leads to a differential game formulation of battle scenarios that we analyze for a warfare situation. Specifically,...
In this paper a new strategy for handling the observation information of a bearing-range sensor throughout the filtering process of EKF-SLAM is proposed. This new strategy is advised based on a thorough consistency analysis and aims to improve the process consistency while reducing the computational cost. At first, three different possible observation models are introduced for the EKF-SLAM solution...
In this paper, a global sliding mode control (GSMC) scheme is implemented on a piezo-driven XY parallel micropositioning stage to compensate for the unmodeled hysteresis aiming at a sub-micron accuracy motion tracking control. The GSMC controller is designed with the consideration of all uncertainty bounds. In the controller implementation, a high-gain velocity observer is adopted to estimate the...
This paper introduces a testbed for sensor and robot network systems, currently composed of 10 cameras and 5 mobile wheeled robots equipped with several sensors for self-localization, obstacle avoidance and vision cameras, and wireless communications. The testbed includes a service-oriented middleware to enable fast prototyping and implementation of algorithms previously tested in simulation, as well...
In this paper we propose an approach to SLAM suitable for bathymetric mapping by an autonomous underwater vehicle (AUV). AUVs typically do not have access to GPS while underway and the survey areas of interest are unlikely to contain features that can easily be identified and tracked using bathymetric sonar. We demonstrate how the uncertainty in the vehicle state can be modeled using a particle filter...
This paper describes an algorithm for estimating lane boundaries and curbs from a moving vehicle using noisy observations and a probabilistic model of curvature. The primary contribution of this paper is a curve model we call lateral uncertainty, which describes the uncertainty of a curve estimate along the lateral direction at various points on the curve, and does not attempt to capture uncertainty...
In this paper, we deal with an original advanced driver assistance system (ADAS) based on the use of omnidirectional vision and an evidential fusion architecture. The panoramic perception solution permits us to address efficiently the problem of close vehicles detection but also the monitoring side traffic system. The fusion and integration of this sensorial data stream is assumed by a credibilist...
This article compares several parameterizations and motion models for improving the estimation of the nonlinear uncertainty distribution produced by robot motion. In previous work, we have shown that the use of a modified polar parameterization provides a way to represent nonlinear measurements distributions in the Cartesian space as linear distributions in polar space. Following the same reasoning,...
Visual simultaneous localization and mapping (SLAM) implementations must use feature extraction to reduce the dimensionality of image input, yet no comparison of feature extractors exists in the context of visual SLAM. This paper presents both a method for comparison of visual SLAM performance using several different feature extractors and the first experimental study using this method. Possible evaluation...
Sensorless localization of 3D objects has been a significant research topic for many years. Researchers have focused on this problem from both theoretical and practical perspective where the goal is to reduce uncertainties in the orientation of a 3D object. However, to the best of our knowledge, no effective practical methods have been proposed so far to localize a polyhedron from any initial orientation...
Autonomous mobile robots are deployed in a variety of application domains, resulting in scenario specific implementations. However these systems share common components responsible for perception, path planning and task execution. In order to find a formal way to identify the influence of the environmental complexity to the used methods, an approach for quantitative system interdependence analysis...
A large-scale mapping approach is combined with multiple robots events to achieve cooperative mapping. The mapping approach used is based on hierarchical SLAM -global level and local maps-, which is generalized for the multi-robot case. In particular, the consequences of multi-robot loop closing events (common landmarks detection and relative pose measurement between robots) are analyzed and managed...
Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty...
We address the motion planning problem for a manipulator system with base pose uncertainty, e.g., when the manipulator is mounted on a mobile base. Using a particle based representation for the uncertainty, we extend the PRM (probabilistic roadmap) approach to deal with this base uncertainty. Because of the uncertainty, a path for the manipulator is associated with a probability of being collision-free,...
In this paper we present a novel approach to perform indoor self-localization using reference omnidirectional images. We only need one omnidirectional image of the whole scene stored in the robot memory and a conventional uncalibrated on-board camera. We match the omnidirectional image and the conventional images captured by the on-board camera and compute the hybrid epipolar geometry using lifted...
We present an approach for smooth and collision-free navigation of multiple mobile robots amongst each other. Each robot senses its surroundings and acts independently without central coordination or communication with other robots. Our approach uses both the current position and the velocity of other robots to predict their future trajectory in order to avoid collisions. Moreover, our approach is...
Simultaneous Localization and Mapping (SLAM) suffers from a quadratic space and time complexity per update step. Recent advancements have been made in approximating the posterior by forcing the information matrix to remain sparse as well as exact techniques for generating the posterior in the full SLAM solution to both the trajectory and the map. Current approximate techniques for maintaining an online...
The ability of mobile robots to quickly and accurately analyze their dynamics is critical to their safety and efficient operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this must be considered in an analysis of robot motion. Here a Multi-Element generalized Polynomial Chaos (MEgPC) approach is presented that explicitly considers...
Robotic obstacle avoidance in cluttered and dense environments is an important issue in robotic navigation. Over the past few years a number of techniques has been proposed to deal with safe navigation among obstacles in unknown scenarios. Unfortunately many of these methods do not consider obstacle velocities, which can rise some serious questions concerning their safety. This paper will deal with...
Cameras are popular sensors for robot navigation tasks such as localization as they are inexpensive, lightweight, and provide rich data. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. In this paper, we present a reinforcement learning approach to choose appropriate velocity profiles for vision-based navigation. The...
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