A Topological Approach to Using Cables to Separate and Manipulate Sets of Objects

Kim, Soonkyum; Bhattacharya, Subhrajit; Heidarsson, Hordur; Sukhatme, Gaurav S.; Kumar, Vijay

doi:10.15607/rss.2013.ix.046

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…It also suggests that a longer tether between robots does not necessarily correspond to better efficiency. This finding may impact prior arts wherein the length is not considered [4,14] or is fixed [5,6,17,18,31].…”

Section: B Trajectory Trackingmentioning

confidence: 99%

“…Although cooperative planning has incubated many successful applications, such as search and rescue [8], exploration [9], object manipulation [10,11], payload transportation [12,13], the planning problem of tethered robot duo to date is less explored. Prior work [4,14] mostly formulates it as a separation problem: The goal is to find trajectories for the robot duo to separate all target objects from obstacles. In such a scheme, prior methods assume an infinite separation distance between the robots, unpractical and unrealistic in many cases.…”

Section: A Related Workmentioning

confidence: 99%

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Object Gathering with a Tethered Robot Duo

Su,

Jiang,

Zhu

et al. 2022

Preprint

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We devise a cooperative planning framework to generate optimal trajectories for a robot duo tethered by a flexible net to gather scattered objects spread in a large area. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to gather objects efficiently and avoid obstacles properly. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Controller (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function for maintaining separation distance is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net's length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.

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Section: B Trajectory Trackingmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Object Gathering with a Tethered Robot Duo

Su,

Jiang,

Zhu

et al. 2022

Preprint

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“…A notable exception is that pairs of conjoined robots have been studied for purposes of object manipulation. Kim et al (2013) studied object separation using a pair of robots connected by a cable. Though superficially similar to our problem, as it involves the motion of a mutually connected pair of robots, the separation problem imposes quite a different set of constraints to a shortest path planning problem.…”

Section: Related Workmentioning

confidence: 99%

Motion Planning for a Pair of Tethered Robots

Teshnizi,

Shell

2021

Preprint

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Considering an environment containing polygonal obstacles, we address the problem of planning motions for a pair of planar robots connected to one another via a cable of limited length. Much like prior problems with a single robot connected via a cable to a fixed base, straight line-of-sight visibility plays an important role. The present paper shows how the reduced visibility graph provides a natural discretization and captures the essential topological considerations very effectively for the two robot case as well. Unlike the single robot case, however, the bounded cable length introduces considerations around coordination (or equivalently, when viewed from the point of view of a centralized planner, relative timing) that complicates the matter. Indeed, the paper has to introduce a rather more involved formalization than prior single-robot work in order to establish the core theoretical result-a theorem permitting the problem to be cast as one of finding paths rather than trajectories. Once affirmed, the planning problem reduces to a straightforward graph search with an elegant representation of the connecting cable, demanding only a few extra ancillary checks that ensure sufficiency of cable to guarantee feasibility of the solution. We describe our implementation of A search, and report experimental results. Lastly, we prescribe an optimal execution for the solutions provided by the algorithm.

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“…The Hsignature is not unique and can be designed in several ways including Cauchy-Integration [3], [4]. In this work, we use the H-signature introduced in [15] because this H-signature is designed to count the number of crossing with reference rays, +1 for left-to-right and −1 for right-to-left. This function can be calculated from the homotopy invariant, word, defined above by counting the number of the corresponding letters appearing in the word.…”

Section: The Preliminariesmentioning

confidence: 99%

Path planning for a tethered robot using Multi-Heuristic A* with topology-based heuristics

Kim

Likhachev

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-In this paper, we solve the path planning problem for a tethered mobile robot, which is connected to a fixed base by a cable of length L. The reachable space of the robot is restricted by the length of the cable and obstacles. The reachable space of the tethered robot can be computed by considering the topology class of the cable. However, it is computationally too expensive to compute this space a-priori. Instead, in this paper, we show how we can plan using a recently-developed variant of A* search, called Multi-Heuristic A*. Normally, the Multi-Heuristic A* algorithm takes in a fixed set of heuristic functions. In our problem, however, the heuristics represent length of paths to the goal along different topology classes, and there can be too many of them and not all the topology classes are useful. To deal with this, we adapt Multi-Heuristic A* to work with a dynamically generated set of heuristic functions. It starts out as a normal weighted A*. Whenever the search gets trapped in a local minimum, we find the proper topology class of the path to escape from it and add the corresponding new heuristic function into the set of heuristic functions considered by the search. We present experimental analysis comparing our approach with weighted A* on planning for a tethered robot in simulation.

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A Topological Approach to Using Cables to Separate and Manipulate Sets of Objects

Cited by 9 publications

References 18 publications

Object Gathering with a Tethered Robot Duo

Object Gathering with a Tethered Robot Duo

Motion Planning for a Pair of Tethered Robots

Path planning for a tethered robot using Multi-Heuristic A* with topology-based heuristics

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