A New Object Path Planner for the Box Pushing Problem

Parra-González, Ezra Federico; Ramírez-Torres, José Gabriel; Toscano-Pulido, Gregorio

doi:10.1109/cerma.2009.54

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…With our new path planning enhancement, if an obstacle's closest storage zone cannot be accessed by a simple motion along a single world axis, a plan is generated by including an alternating sequence of pushes along the x-and y-axes. This pushing method is based on the route graph algorithm described in [26] and enables a path to be generated for the manipulation phase of the planning, which is not restricted to single axis motions.…”

Section: ) Alternating Push Directionsmentioning

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: ) Alternating Push Directionsmentioning

confidence: 99%

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

et al. 2023

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“…It can also be noted that the results of the Q-learning algorithm are affected by uncooperative actions in a way that is similar to noise. Research has also focused on path planning for box pushing given complete knowledge of the environment by modeling objects in the environment as convex polygons and planning the box trajectory [5]. Having complete knowledge of the environment initially is not a realistic approach in most applications, since the position of each robotic agent can affect the environment, so assuming the environment is dynamic is preferred.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing

Rahimi

Gibb

Shen

et al. 2018

Advances in Engineering Research and Application

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In this paper, a comparison of reinforcement learning algorithms and their performance on a robot box pushing task is provided. The robot box pushing problem is structured as both a singleagent problem and also a multi-agent problem. A Q-learning algorithm is applied to the single-agent box pushing problem, and three different Q-learning algorithms are applied to the multi-agent box pushing problem. Both sets of algorithms are applied on a dynamic environment that is comprised of static objects, a static goal location, a dynamic box location, and dynamic agent positions. A simulation environment is developed to test the four algorithms, and their performance is compared through graphical explanations of test results. The comparison shows that the newly applied reinforcement algorithm out-performs the previously applied algorithms on the robot box pushing problem in a dynamic environment.

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“…Moreover, an MRS presents various attractive advantages in many ways, in particular, those that are linked to the ability of scaling up the systems. All these advantages have given researchers incentives to expand research activities in this direction recently [43][47][2] [48][49] [50]. Furthermore, there are other advantages that make MRS usage more widespread than SRS as follows [48]:…”

Section: Multi-robot Systems Versus Single Robot Systemsmentioning

confidence: 99%

“…All these advantages have given researchers incentives to expand research activities in this direction recently [43][47][2] [48][49] [50]. Furthermore, there are other advantages that make MRS usage more widespread than SRS as follows [48]:…”

Section: Multi-robot Systems Versus Single Robot Systemsmentioning

confidence: 99%

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Social Graphs and Their Applications to Robotics

Alwafi¹

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A New Object Path Planner for the Box Pushing Problem

Cited by 11 publications

References 15 publications

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

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

A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing

Social Graphs and Their Applications to Robotics

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