Motion planning for disc-shaped robots pushing a polygonal object in the plane

Sudsang, Attawith; Rothganger, Fred; Ponce, Jean

doi:10.1109/tra.2002.801049

Cited by 75 publications

(59 citation statements)

References 38 publications

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“…It has been studied in [19,2,4,18,33,22]. Our work is very similar to [33,13] in application (using circular robots to manipulate polygonal parts). However, geometric abstractions of caging are used in [33, (a) (b) (c) Fig.…”

Section: University Of Pennsylvaniamentioning

confidence: 94%

“…Since the main property of interest is the geometric property of containing or enclosing the manipulated object, one is motivated to derive geometric abstractions for the complex, multi-dimensional dynamics problem. This is the central idea in configuration-space abstractions used to derive algorithms for multi-robot manipulation: motion planning algorithms for caging [33,32], control algorithms for object closure [21], and composition of controllers for multi-robot manipulation [13]. Each robot or finger is abstracted into a geometric model.…”

Section: Introductionmentioning

confidence: 99%

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Abstractions and Algorithms for Cooperative Multiple Robot Planar Manipulation

Cheng

Fink

Kumar

2008

Robotics: Science and Systems IV

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Abstract-In this paper, we will study abstractions and algorithms for planar manipulation systems using two cooperating robots under uncertainties. We propose a formal framework for developing abstractions, which are simpler models of the original systems that preserve properties of interest to facilitate the development of planning and control algorithms. Our abstractions are derived from robust motion primitives that correspond to control inputs leading to system trajectories which preserve the properties of interest under uncertainties. We then use the proposed framework to construct an abstraction and design planning and control algorithms for a multiple robot cooperative manipulation system. Finally, we present experimental results to validate our approach. I. INTRODUCTIONIt is well known that conventional approaches to robotic manipulation, where deliberative planning is augmented by feedback controllers, are difficult to implement except in the simplest of cases. This is primarily because of non smooth dynamics engendered by frictional contacts and uncertainties in the parameters governing the contact dynamics. Experiments in robotic juggling [8], locomotion [11,25], non prehensile manipulation [35], manipulation via caging (Fig. 2) [13], and part-feeding [29] have shown that feedback controllers, behaviors or designs, which are specially designed to preserve a specific set of properties (e.g., convergence to sub manifolds or limit sets), are more robust to uncertainties than those that follow optimally-planned trajectories in the full state space. Indeed, this philosophy of designing components that each drive the system to a state that satisfies a specific property is used extensively in manufacturing operations, where designers carefully structure the environment to ensure that devices like bowl-feeders [14], conveyors [1], traps [5], and pick-andplace arms work in concert to accomplish the given task. Many paradigms in robotics such as caging [7], the onejoint-over-conveyor part positioning [1], and remote-centerof-compliance assembly [12] are also illustrative of this philosophy. While these examples are arguably special-purpose solutions, they illustrate a very important point. By designing planners/controllers that drive the system to submanifolds in the state space, one can derive abstractions of complex processes, i.e., conceptual models that are much simpler than the complex real-world system, that lend themselves to the design of algorithms that can reason about these abstractions and the composition of these complex processes.We use the simple example of multi-fingered or multirobot manipulation in the plane via caging to illustrate the role of abstractions and algorithms (Fig. 2). The modeling

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Section: University Of Pennsylvaniamentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Abstractions and Algorithms for Cooperative Multiple Robot Planar Manipulation

Cheng

Fink

Kumar

2008

Robotics: Science and Systems IV

View full text Add to dashboard Cite

show abstract

“…Rather than alternating transit and transfer actions, robot actions are chosen such that they approach the goal while obeying constraints guaranteeing that the object remain caged. This approach has resulted in complete algorithms for obstacle free environments [22], and moderately cluttered environments [4,14,20]. However, we consider environments with narrow passages where it is not physically possible to cage an object.…”

Section: Related Workmentioning

confidence: 99%

The Feasible Transition Graph: Encoding Topology and Manipulation Constraints for Multirobot Push-Planning

Lindzey

Knepper

Choset

et al. 2015

Springer Tracts in Advanced Robotics

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Abstract. Planning for multirobot manipulation in dense clutter becomes particularly challenging as the motion of the manipulated object causes the connectivity of the robots' free space to change. This paper introduces a data structure, the Feasible Transition Graph (FTG), and algorithms that solve such complex motion planning problems. We define an equivalence relation over object configurations based on the robots' free space connectivity. Within an equivalence class, the homogeneous multirobot motion planning problem is straightforward, which allows us to decouple the problems of composing feasible object motions and planning paths for individual robots. The FTG captures transitions among the equivalence classes and encodes constraints that must be satisfied for the robots to manipulate the object. From this data structure, we readily derive a complete planner to coordinate such motion. Finally, we show how to construct the FTG in some sample environments and discuss future adaptations to general environments.

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“…For more work on orienting parts, see [28,58,67,68,152,190]. For more forms of nonprehensile manipulation, see [1,2,12,56,117,118,175]. A humorous paper, which introduces the concept of the "principle of virtual dirt," is [121]; the idea later appears in [158] and in the Roomba autonomous vacuum cleaner from the iRobot Corporation.…”

Section: Further Readingmentioning

confidence: 99%

Planning Under Sensing Uncertainty

LaValle

2006

Planning Algorithms

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Motion planning for disc-shaped robots pushing a polygonal object in the plane

Cited by 75 publications

References 38 publications

Abstractions and Algorithms for Cooperative Multiple Robot Planar Manipulation

Abstractions and Algorithms for Cooperative Multiple Robot Planar Manipulation

The Feasible Transition Graph: Encoding Topology and Manipulation Constraints for Multirobot Push-Planning

Planning Under Sensing Uncertainty

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