Intelligent Mobility

Joyeux, Sylvain; Schwendner, Jakob; Kirchner, Frank; Babu, Ajish; Grimminger, Felix; Machowinski, Janosch; Paranhos, Patrick Merz; Gaudig, Christopher

doi:10.1007/s13218-011-0089-8

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…From a sensor processing point of view, this is very similar to the data provided by a laser range finder, and the same processing chains can be applied (e.g. [7]). Local obstacle maps are generated from the line structured light system (see Figure 2).…”

Section: Autonomymentioning

confidence: 99%

Temporal and spatial benthic data collection via mobile robots: Present and future applications

Thomsen

Purser

Schwendner

et al. 2015

OCEANS 2015 - Genova

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The analogies between space and deep sea motivated the German Helmholtz Association to setup the joint research program ROBEX (ROBotic Exploration under eXtreme conditions). The programme objectives are to identify, develop and verify technological synergies between the robotic exploration of the Moon and of the deep sea. In both space and deep sea research orbiters and AUVs/gliders respectively provide a large set of information on the environment but landers and mobile robots are essential to ground truth the data and validate theories. Within ROBEX the mobility of robots is a vital element for research missions due to valuable science return potential from different sites as opposed to static landers. After 2.5 years of project-time three main mobile systems are prepared for demonstrationmissions, one wheel-based system for planetary missions, one wheel based system for extended deep-sea missions and a caterpillar system for high resolution investigation of small surface regions in the deep sea. In all cases sufficient onboard intelligence was needed. The mobile robots should also be capable of return to a central station for recharging, with the station equipped with enough battery power to allow multiple missions. The intelligent seafloor robot "iCrawler" is presented here, which is based on a tele-operated robot, which has been successfully used within the ONC (Ocean Networks Canada) cabled observatory project since 2010. Keywords-seafloor monitoring, mobile robots, ROBEX project978-1-4799-8736-8/15/$31.00 ©2015 IEEE

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Section: Autonomymentioning

confidence: 99%

Temporal and spatial benthic data collection via mobile robots: Present and future applications

Thomsen

Purser

Schwendner

et al. 2015

OCEANS 2015 - Genova

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Using Embodied Data for Localization and Mapping

Schwendner

Joyeux

Kirchner

2013

Journal of Field Robotics

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Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.

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Adaptive localization and mapping with application to planetary rovers

Hidalgo-Carrió

Poulakis

Kirchner

2018

Journal of Field Robotics

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Future exploration rovers will be equipped with substantial onboard autonomy. SLAM is a fundamental part and has a close connection with robot perception, planning, and control. The community has made great progress in the past decade by enabling real-world solutions and is addressing important challenges in high-level scalability, resources awareness, and domain adaptation.A novel adaptive SLAM system is proposed to accomplish rover navigation and computational demands. It starts from a three-dimensional odometry dead reckoning solution and builds up to a full graph optimization that takes into account rover traction performance. A complete kinematics of the rover locomotion system improves the wheel odometry solution. In addition, an odometry error model is inferred using Gaussian processes (GPs) to predict nonsystematic errors induced by poor traction of the rover with the terrain. The nonparametric GP regression serves to adapt the localization and mapping to the current navigation demands (domain adaptation). The method brings scalability and adaptiveness to modern SLAM. Therefore, an adaptive strategy develops to adjust the image frame rate (active perception) and to influence the optimization backend by including high informative keyframes in the graph (adaptive information gain). The work is experimentally verified on a representative planetary rover under a realistic field test scenario. The results show a modern SLAM systems that adapt to the predicted error. The system maintains accuracy with less number of nodes taking the most benefit of both wheel and visual methods in a consistent graph-based smoothing approach. K E Y W O R D Smapping, planetary robotics, position estimation INTRODUCTIONThe navigation system is a technological key aspect in mobile planetary robots (i.e., rovers). The system allows to know where the rover is, locate the target, and then guide the robot. These three tasks are essential to perform in situ mission operations in the harsh of a remote environment. The navigation system in spacecraft terminology divides into three capabilities as guidance, navigation, and control (GNC). Guidance is the path-planning responsibility. Navigation is the localization and mapping competency, and control is the commanding of the rover locomotion system. These three elements depend on the mission and the requirements affect their design. These requirements are of three types: operational, functional, and resources. Operational imposes the level of autonomy due to a constrained communication bandwidth in space. Functional requirements define the level of performance. Resources establish the sensor type, perception, computational power, and software restrictions.Figure 1a shows a typical GNC system diagram with Simultaneous Localization and Mapping (SLAM) frontend and backend together with the onboard computation demands. Figure 1b depicts the system in two parts. During the first part, the rover acquires images from the navigation cameras, computes a dense map of the surroundings, and calculates t...

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Intelligent Mobility

Cited by 5 publications

References 9 publications

Temporal and spatial benthic data collection via mobile robots: Present and future applications

Temporal and spatial benthic data collection via mobile robots: Present and future applications

Using Embodied Data for Localization and Mapping

Adaptive localization and mapping with application to planetary rovers

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