Move Base Flex A Highly Flexible Navigation Framework for Mobile Robots

Pütz, Sebastian; Simon, Jorge Santos; Hertzberg, Joachim

doi:10.1109/iros.2018.8593829

Cited by 35 publications

(11 citation statements)

References 9 publications

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“…BTs have also made their way into the popular Robot Operating System (ROS), maintained by Open Robotics 5 [17]. The Move Base Flex navigation system uses Behavior Trees instead of HFSM as a replacement for ROS 1's navigation package [18]. With the introduction of ROS 2, the navigation2 6 package uses behavior trees for task orchestration [19].…”

Section: Table I Summary Of Each Type Of Bt Node and Return Conditionsmentioning

confidence: 99%

Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics

Zutell¹,

Conner²,

Schillinger³

2022

Preprint

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In recent years, the model of computation known as Behavior Trees (BT), first developed in the video game industry, has become more popular in the robotics community for defining discrete behavior switching. BTs are threatening to supplant the venerable Hierarchical Finite State Machine (HFSM) model. In this paper we contrast BT and HFSM, pointing out some potential issues with the BT form, and advocate for a hybrid model of computation that uses both BT and HFSM in ways that leverage their individual strengths. The work introduces a new open-source package for ROS 2 that extends the Flexible Behavior Engine (FlexBE) to enable interaction with BT-based behaviors within a HFSM in a way that supports collaborative autonomy. Simulation and hardware demonstrations illustrate the concepts.

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Section: Table I Summary Of Each Type Of Bt Node and Return Conditionsmentioning

confidence: 99%

Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics

Zutell¹,

Conner²,

Schillinger³

2022

Preprint

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“…The navigation state machine takes care about the path planning and path execution processes. The move base flex [47] framework is implemented to change between different motion behaviors of the mobile platform depending on the current navigation zone. The manipulation state machine is divided into three process steps.…”

Section: Task Managementmentioning

confidence: 99%

A Robot-Assisted Large-Scale Inspection of Wind Turbine Blades in Manufacturing Using an Autonomous Mobile Manipulator

et al. 2021

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Wind energy represents the dominant share of renewable energies. The rotor blades of a wind turbine are typically made from composite material, which withstands high forces during rotation. The huge dimensions of the rotor blades complicate the inspection processes in manufacturing. The automation of inspection processes has a great potential to increase the overall productivity and to create a consistent reliable database for each individual rotor blade. The focus of this paper is set on the process of rotor blade inspection automation by utilizing an autonomous mobile manipulator. The main innovations include a novel path planning strategy for zone-based navigation, which enables an intuitive right-hand or left-hand driving behavior in a shared human–robot workspace. In addition, we introduce a new method for surface orthogonal motion planning in connection with large-scale structures. An overall execution strategy controls the navigation and manipulation processes of the long-running inspection task. The implemented concepts are evaluated in simulation and applied in a real-use case including the tip of a rotor blade form.

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“…ROS2 navigation2 package [3] used BTs to successfully navigate through a crowded building using 2 types of ground robots. The Move Base FLex ROS package [4] are deployed in Magazino [5] warehouse robots and used BTs to navigate 6 commercial settings. In [6] a simulated autonomous driving system for behaviors such as overtaking and obstacle avoidance is presented.…”

Section: Related Work a Behavior Trees(bts)mentioning

confidence: 99%

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

Yi¹,

Nebot²

2021

Preprint

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Fig. 1. Vehicle trajectories of 53 rosbags in the University of Sydney Campus Dataset output from the GPS+lidar main filter are superimposed together onto a satellite image. No manual alignment of trajectory is performed. The color of trajectory points indicates the state of update used by the filter. Green: lidar feature updating. Blue: dead reckoning. Red: GPS updating. The blue dead reckoning area in the middle of the image is an underground carpark.

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Move Base Flex A Highly Flexible Navigation Framework for Mobile Robots

Cited by 35 publications

References 9 publications

Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics

Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics

A Robot-Assisted Large-Scale Inspection of Wind Turbine Blades in Manufacturing Using an Autonomous Mobile Manipulator

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

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