Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Wang, Maozhen; Luo, Rui; Onol, Aykut Ozgun; Padır, Taşkın

doi:10.1109/iros45743.2020.9341337

Cited by 18 publications

(12 citation statements)

References 26 publications

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“…It was shown that it is possible to reduce the evacuation time by creating new pathways for the humans that are leaving a dangerous environment. In [24] the role of computer vision is explored in order to facilitate the solution of NAMO problems. In particular, the problem of how affordances detection can be used to create open loop NAMO plans.…”

Section: Related Workmentioning

confidence: 99%

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%

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

et al. 2023

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“…The methods have been used on humanoid robots and wheeled ones, up to an extend (either with known environments or via open-loop controllers), as well as for robot arm manipulation [37]. Recently they have been extended for socially-aware navigation [38], [39], navigation using scene affordances [40], or even sim2real navigation [41]. In our paper, we are utilizing an object pushing action, based on the NAMO algorithm, introduced in [35], [38], using the legs of the hybrid robot.…”

Section: A Related Workmentioning

confidence: 99%

Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments With Movable Obstacles

et al. 2022

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Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot.

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“…One of the conventional approaches is the task and motion planning (TAMP) method which combines task planning in symbolic space and motion planning in the configuration space [8]- [10]. The other approach is navigation among movable obstacles (NAMO), which is a navigation method that considers not only avoiding obstacles but also interaction with movable obstacles [11], [12]. The feasibility of actions in real space is verified using these methods based on the following two procedures.…”

Section: Related Work a Task Planning And Motion Planningmentioning

confidence: 99%

“…The conditions of the graph representation methods are shown in Table 8. The comparison method 2.1 represents the action possibility in the current environment as well as the methods to understand the objects to be removed in real space [8], [12]. In this method, the movable object positions were traversable, and the cost of removing the object was added when the robot passes through the position.…”

Section: Experiments 2: Verification Of Action Graph Structure 1) Experimental Conditionsmentioning

confidence: 99%

ASTRON: Action-Based Spatio-Temporal Robot Navigation

2021

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To achieve the tasks provided by a user, it is necessary for robots to have a plan that fully exploits their functionalities in an environment. The objective of this study is to realize robot task planning in real space for effectively use of the robot's functions The plan is formed by deriving a feasible action sequence by interpreting the instructions within the scope of the action possibilities of the robots and the changes in them. In this paper, we first propose an action graph as a novel environmental representation approach to facilitate the understanding of the robot's action possibility in real space. In the action graph, the action possibility is represented by nodes, which describe the spatial position to perform each feasible action, and edges, which describe the feasible actions, based on the subsystem-level affordance and the arrangement of objects in the environment. We also propose an action-based spatio-temporal robot navigation (ASTRON), which focuses on robot navigation tasks. ASTRON enables the robots to determine a feasible action sequence that utilizes their functions by interpreting the instructions based on the action graph. The effectiveness of the proposed method was evaluated through simulations and actual machine experiments in a coffee shop environment. In the actual machine experiments, the proposed method was applied to robots with different subsystem configurations. The experimental results demonstrated that the proposed method could plan the feasible action sequence to complete the tasks by considering the environmental state and the subsystem configurations of the robot.

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Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Cited by 18 publications

References 26 publications

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

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

Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments With Movable Obstacles

ASTRON: Action-Based Spatio-Temporal Robot Navigation

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