BI<sup>2</sup>RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation

Burget, Felix; Bennewitz, Maren; Burgard, Wolfram

doi:10.1109/iros.2016.7759547

Cited by 59 publications

(32 citation statements)

References 17 publications

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“…For generating paths for the mobile base, we apply the sampling-based planning framework BI 2 RRT* [20]. Given a pair of terminal configurations, it performs a bidirectional search using uniform sampling in the configuration space until an initial sub-optimal solution path is found.…”

Section: Robot Motion Generationmentioning

confidence: 99%

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Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception.In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based humanrobot interactions.

show abstract

Section: Robot Motion Generationmentioning

confidence: 99%

“…(a) SNR of the first 4 s of data used to train the hybrid ConvNet. Highest SNR can be observed in the alpha (7-14 Hz) and lower beta(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26) bands. These frequency bands are robust markers of task related mental tasks.…”

mentioning

confidence: 99%

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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“…In addition, to get efficient movement, many manipulation approaches have used to optimal motion planning algorithms such as Informed RRT * [5], BIT * [6], FMT * [7], and LazyPRM * [8], since Karaman et al [9] introduced RRT * and PRM * . Burget et al [10] suggested BI 2 RRT * that extends the Informed RRT * towards a bidirectional search for a task-constrained mobile manipulator. Schmitt et al [11] proposed an optimal manipulation planning for continuous grasps, placements, and actions by connecting the configurations.…”

Section: A Sampling-based Planningmentioning

confidence: 99%

“…To validate our method, we compare our method against two different approaches: the decoupled and coupled approaches. The decoupled approach [2] divides the planning process into two separate planning for the base and manipulator, while the coupled approach [10] performs the planning within the joint C-space representing both base and manipulators and thus having 10 DoFs.…”

Section: Experiments and Analysismentioning

confidence: 99%

Harmonious Sampling for Mobile Manipulation Planning

Kang¹,

Kim²,

Yoon

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5.6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.

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“…For navigation planning of the mobile base, we apply the sampling-based planning framework BI 2 RRT* [44]. Given a pair of terminal configurations, it performs a bidirectional search using uniform sampling in the configuration space until an initial sub-optimal solution path is found.…”

Section: Autonomous Bci-controlled Service Assistantmentioning

confidence: 99%

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

Kuhner

Fiederer

Aldinger

et al. 2018

Preprint

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As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

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BI²RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation

Cited by 59 publications

References 17 publications

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Harmonious Sampling for Mobile Manipulation Planning

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

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BI2RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation

Cited by 59 publications

References 17 publications

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Harmonious Sampling for Mobile Manipulation Planning

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

Contact Info

Product

Resources

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BI²RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation