Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms

Renault, Benoit; Saraydaryan, Jacques; Simonin, and Olivier

doi:10.1109/iros45743.2020.9340892

Cited by 6 publications

(9 citation statements)

References 23 publications

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“…More recently, the NAMO problem has been extended to novel application contexts and utilized for different robotic platforms. While extending the method introduced in [18], the methods proposed in [21] and [22] introduced the concept of social awareness, while planning for NAMO. In these works, the authors defined new criteria that extend the original NAMO formulation, such as social movability evaluation, social placement choice, and social action planning.…”

Section: Related Workmentioning

confidence: 99%

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Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

et al. 2023

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With the majority of mobile robot path planning methods being focused on obstacle avoidance, this paper, studies the problem of Navigation Among Movable Obstacles (NAMO) in an unknown environment, with static (i.e., that cannot be moved by a robot) and movable (i.e., that can be moved by a robot) objects. In particular, we focus on a specific instance of the NAMO problem in which the obstacles have to be moved to predefined storage zones. To tackle this problem, we propose an online planning algorithm that allows the robot to reach the desired goal position while detecting movable objects with the objective to push them towards storage zones to shorten the planned path. Moreover, we tackle the challenging problem where an obstacle might block the movability of another one, and thus, a combined displacement plan needs to be applied. To demonstrate the new algorithm's correctness and efficiency, we report experimental results on various challenging path planning scenarios. The presented method has significantly better time performance than the baseline, while also introducing multiple novel functionalities for the NAMO problem.INDEX TERMS Motion and path planning, navigation among movable obstacles, mobile robots.

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Section: Related Workmentioning

confidence: 99%

“…Motivated by the work presented recently in [21] and [22], we further extend the framework in multiple directions to tackle the problem of NAMO with storage zones. In this paper, we extend the NAMO state-of-the-art in three directions:…”

Section: Contributionsmentioning

confidence: 99%

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

et al. 2023

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“…A common idea is to move it out of the path [8] or until a feasible detour is found [13], [14], but they do not consider the impact of final position of the obstacle on the future danger. However, [15] searches the stock region by minimizing the displacement of the movable obstacle out of the planned path, and [4], working in dynamic environments, designs a social cost to measure the impact of the stock region: it is expected not to be in the middle of corridors and narrow regions. We propose a new RL based approach to quickly find positions with the same objective.…”

Section: Stock Region Searchingmentioning

confidence: 99%

“…This problem is Website: kai-zhang-er.github.io/namo-time-cost often solved by sampling candidate positions [3] which is time consuming and can present large variation in execution time. Additionally, the target position should free the robot path, but can also take other constraints into account, such as a social cost related to the fact that some positions will impact more on the navigation of humans or other robots and should be avoided [4]. We use Reinforcement Learning (RL) to have a fast decision on where the obstacle should be moved by taking all these constraints into account.…”

Section: Introductionmentioning

confidence: 99%

Navigation Among Movable Obstacles Using Machine Learning Based Total Time Cost Optimization

Zhang,

Lucet,

dit Sandretto

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Most navigation approaches treat obstacles as static objects and choose to bypass them. However, the detour could be costly or could lead to failures in indoor environments. The recently developed navigation among movable obstacles (NAMO) methods prefer to remove all the movable obstacles blocking the way, which might be not the best choice when planning and moving obstacles takes a long time. We propose a pipeline where the robot solves the NAMO problems by optimizing the total time to reach the goal. This is achieved by a supervised learning approach that can predict the time of planning and performing obstacle motion before actually doing it if this leads to faster goal reaching. Besides, a pose generator based on reinforcement learning is proposed to decide where the robot can move the obstacle. The method is evaluated in two kinds of simulation environments and the results demonstrate its advantages compared to the classical bypass and obstacle removal strategies.

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“…Several studies have been presented, in which the human behavior is predicted and reflected in the cost map [17]. The social robot placement not to interfere with human workers could be modeled and reflected in the cost map [18]. Few studies have accurately predicted the human trajectory using deep inverse RL [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS

Jeong

Inhwan³

et al. 2022

Applied Sciences

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A traffic management system can be used to control multiple automated mobile robots (AMRs) effectively. This paper proposes traffic management for multiple AMRs based on a layered cost map in ROS 2 for multiple purposes. Using the layered cost map, the new concepts of a prohibition filter, lane filter, fleet layer, and region filter are proposed and implemented. The prohibition filter can help a user set an area that would prohibit an AMR from trespassing. The lane filter can help set one-way directions based on an angle image. The fleet layer can help AMRs share their locations via the traffic management server. The region filter requests for or receives an exclusive area, which can be occupied by only one AMR from the traffic management server. Multiple AMRs communicate via a data distribution service (DDS), which is shared by topics in the same DDS domain. The traffic management server in the domain sends or receives topics to each of the AMRs. The experiments of AMRs under the proposed traffic management show the effectiveness of our approach.

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Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms

Cited by 6 publications

References 23 publications

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

Navigation Among Movable Obstacles Using Machine Learning Based Total Time Cost Optimization

Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS

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