Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Zimmermann, Karel; Zuzanek, Petr; Michal, R.; Hlaváč, Václav

doi:10.1109/icra.2014.6907619

Cited by 28 publications

(25 citation statements)

References 22 publications

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“…40 %-50 % of states in which the permitted configuration is selected) for both GP and PWC, while the marginalizing methods preserve high precision. The figure also demonstrates that the proposed Regression Forests provide better success rate than the previous methods [2], [3].…”

Section: Robustness To Missing Exteroceptive Datamentioning

confidence: 75%

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Controlling Robot Morphology From Incomplete Measurements

Pecka

Zimmermann

Michal

et al. 2017

IEEE Trans. Ind. Electron.

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Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions (USAR). Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data.

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Section: Robustness To Missing Exteroceptive Datamentioning

confidence: 75%

“…In this work, we extend and improve the AC pipeline introduced in our previously published work [2], [3] (see Fig. 2 for an overview).…”

Section: S Ince Exploration Of Unknown Disaster Areas During Urbanmentioning

confidence: 99%

Controlling Robot Morphology From Incomplete Measurements

Pecka

Zimmermann

Michal

et al. 2017

IEEE Trans. Ind. Electron.

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“…Initially, the proof of concept was tested and evaluated on a quadruped robot described in [7]. These results were then extended by a new methodology described in [8] and successfully applied to the NIFTi UGV platform [5], [9]. Furthermore, we developed algorithms necessary for processing the raw inertial data; see details in [10].…”

Section: Terrain-adaptive Odometrymentioning

confidence: 99%

Designing, developing, and deploying systems to support human–robot teams in disaster response

et al. 2014

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This paper describes our experience in designing, developing and deploying systems for supporting human-robot teams during disaster response. It is based on R&D performed in the EU-funded project NIFTi. NIFTi aimed at building intelligent, collaborative robots that could work together with humans in exploring a disaster site, to make a situational assessment. To achieve this aim, NIFTi addressed key scientific design aspects in building up situation awareness in a human-robot team, developing systems using a user-centric methodology involving end users throughout the entire R&D cycle, and regularly deploying implemented systems under real-life circumstances for experimentation and testing. This has yielded substantial scientific advances in the state-of-the-art in robot mapping, robot autonomy for operating in harsh terrain, collaborative planning, and human-robot interaction. NIFTi deployed its system in actual disaster response activities in Northern Italy, in July 2012, aiding in structure damage assessment.

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“…Several research efforts in robotics have been made to increase the level of autonomy of articulated tracked vehicles, focusing on adaptation (Helmick et al., ; Mourikis, Trawny, Roumeliotis, Helmick, & Matthies, ; Okada et al., ), stability (Norouzi, Miro, & Dissanayake, ; Papadakis, ), self‐reconfiguration (Iagnemma, Rzepniewski, Dubowsky, & Schenker, ; Li, Ma, Li, Wang, & Wang, ), track‐soil interaction (Liu & Liu, ; Zimmermann et al., ), and control (Burke, ; Endo, Okada, Keiji, & Yoshida, ; Gianni et al., ; Moosavian & Kalantari, ; Steplight et al., ). The main issues raised by these research works are (i) the design of both a kinematic and a dynamic model of the AATV, (ii) the choice of a control strategy that takes into account the kinematic constraints of the robot model, (iii) the AATV state estimation, and finally (iv) modeling the terrain structure.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Adaptive Robust Three-dimensional Trajectory Tracking for Actively Articulated Tracked Vehicles*

et al. 2015

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A new approach is proposed for an adaptive robust three-dimensional (3D) trajectory-tracking controller design. The controller is modeled for actively articulated tracked vehicles (AATVs). These vehicles have active subtracks, called flippers, linked to the ends of the main tracks, to extend the locomotion capabilities in hazardous environments, such as rescue scenarios. The proposed controller adapts the flippers configuration and simultaneously generates the track velocities, to allow the vehicle to autonomously follow a given feasible 3D path. The approach develops both a direct and differential kinematic model of the AATV for traversal task execution correlating the robot body motion to the flippers motion. The benefit of this approach is to allow the controller to flexibly manage all the degrees of freedom of the AATV as well as the steering. The differential kinematic model integrates a differential drive robot model, compensating the slippage between the vehicle tracks and the traversed terrain. The underlying feedback control law dynamically accounts for the kinematic singularities of the mechanical vehicle structure. The designed controller integrates a strategy selector too, which has the role of locally modifying the rail path of the flipper end points. This serves to reduce both the effort of the flipper servo motors and the traction force on the robot body, recognizing when the robot is moving on a horizontal plane surface. Several experiments have been performed, in both virtual and real scenarios, to validate the designed trajectory-tracking controller, while the AATV negotiates rubble, stairs, and complex terrain surfaces. Results are compared with both the performance of an alternative control strategy and the ability of skilled human operators, manually controlling the actively articulated components of the robot. C 2015 Wiley Periodicals, Inc.

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Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Cited by 28 publications

References 22 publications

Controlling Robot Morphology From Incomplete Measurements

Controlling Robot Morphology From Incomplete Measurements

Designing, developing, and deploying systems to support human–robot teams in disaster response

Adaptive Robust Three-dimensional Trajectory Tracking for Actively Articulated Tracked Vehicles*

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