A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning

Taati, Babak; Greenspan, Michael; Gupta, Kalyan Kali Sen

doi:10.1007/s10846-006-9067-z

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Parallelism of computation involves distributing the planning computation on different processors in order to increase the overall computational speed of the system [33, 34]. In these works, as distinct from our own, the computing nodes have global knowledge of the environment, and the issue is how to optimize the division of computation.…”

Section: Related Workmentioning

confidence: 99%

zePPeLIN: Distributed Path Planning Using an Overhead Camera Network

Reina

Gambardella

Dorigo

et al. 2014

International Journal of Advanced Robotic Systems

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We introduce zePPeLIN, a distributed system designed to address the challenges of path planning in large, cluttered, dynamic environments. The objective is to define a sequence of instructions to precisely move a ground object (e.g., a mobile robot) from an initial to a final configuration in an environment. zePPeLIN is based on a set of wirelessly networked overhead cameras. While each camera only covers a limited environment portion, the camera set fully covers the environment through the union of its fields of view. Path planning is performed in a fully distributed and cooperative way, based on potential diffusion over local Voronoi skeletons and local message exchanging. Additionally, the control of the moving object is fully distributed: it receives movement instructions from each camera when it enters that camera's field of view. The overall task is made particularly challenging by intrinsic errors in the overlap in cameras' fields of view. We study the performance of the system as a function of these errors, as well as its scalability for the size and density of the camera network. We also propose a few heuristics to improve performance and computational and communication efficiency. The reported results include both extensive simulation experiments and validation using a real camera network planning for a two-robot system.

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Section: Related Workmentioning

confidence: 99%

zePPeLIN: Distributed Path Planning Using an Overhead Camera Network

Reina

Gambardella

Dorigo

et al. 2014

International Journal of Advanced Robotic Systems

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“…This paper is motivated by the goal of addressing a new powerful mathematic algorithm for global path planning of UAVs and is called Mesh Adaptive Direct Search (MADS). The path planning problem is to generate a space path between the initial location and the final destination in known or unknown environments, with optimal or near-optimal performance under specific constraint conditions (Taati et al, 2006). One idea for path planning is to introduce a series of waypoint through which a vehicle can go over the terrain to the specified point (Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach for optimal path planning of unmanned aerial vehicles

Baudkoubeh

Farrokhi

2011

IJMMNO

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This paper utilizes a class of Mesh Adaptive Direct Search method to design an optimal path for Unmanned Aerial Vehicles (UAVs). To this end, a multi-objective optimization problem is considered for simultaneous optimization of some conflicting objective functions under different kinds of vehicle and mission constraints. Since the path planning for UAVs in a large geographical area is a typical large-scale optimization problem, to avoid memory and computational intensive issues, different techniques such as constructing an adaptive mesh, polling, and barrier approach are incorporated in the proposed algorithm. The proposed method is tested under different scenarios and various realistic terrain environments. The results show effectiveness of the proposed method in guiding UAVs to the final destination by providing near-optimal feasible paths quickly and effectively. The results will also be compared with the Genetic Algorithm approach, which has been recently used for path planning.

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“…It is worth pointing out that there is another thread of research studying distributed motion planning from the perspective of parallelism [15], [16], [17], [18], which focuses on distributing computation into different processors. All these works, however, assume processors have access to either a shared or a private copy of a global C-space representation, and therefore address a different problem.…”

Section: Introductionmentioning

confidence: 99%

Distributed roadmaps for robot navigation in sensor networks

Yao

Gupta

2010

2010 IEEE International Conference on Robotics and Automation

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This paper studies a distributed path planning problem: how can a sensor network help to navigate a robot to its desired goal in a distributed manner. We consider the case where each sensor node is equipped with sophisticated sensors capable of giving a map for its sensing region. We propose a distributed sampling based planning framework (Distributed PRM), where every sensor node creates a local roadmap in its locally-sensed environment; these local roadmaps are "stitched" together by passing messages among nodes and form a larger implicit roadmap without having a global representation. Based on the implicit roadmap, a feasible path is computed in a distributed manner, and the robot moves along the path by interacting with sensor nodes, each of which gives a portion of the path within the local environment of the node. Preliminary simulations show the proposed framework is able to solve path planning problem with low communication overhead.

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A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning

Cited by 5 publications

References 16 publications

zePPeLIN: Distributed Path Planning Using an Overhead Camera Network

zePPeLIN: Distributed Path Planning Using an Overhead Camera Network

An adaptive approach for optimal path planning of unmanned aerial vehicles

Distributed roadmaps for robot navigation in sensor networks

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