Multi-Goal Path Planning for Robotic Agents With Discrete-Step Locomotion

Sagar, Keerthi; Zlatanov, Dimiter; Zoppi, Matteo; Nattero, Cristiano; Muthuswamy, Sreekumar

doi:10.1115/detc2017-68011

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The authors in Edelkamp and Lee (2019) and Warsame et al (2020) addressed the multi-robot multi-goal motion planning to solve the vehicle routing problem for mobile robots by using Monte-Carlo search. Additionally, new path planning and collision-avoidance methods were introduced by Sagar et al (2017) to solve multi-robot multi-goal path planning problems. The motion planning problem for multi-robot spot-welding cells in the construction of car doors studied by Pellegrinelli et al (2017).…”

Section: Related Workmentioning

confidence: 99%

The boundary node method for multi‐robot multi‐goal path planning problems

2021

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In this paper, we investigate the multi-goal path planning problem to find the shortest collision-free path connecting a given set of goal points located in the robot working environment. This problem combines two sub-problems: first, optimize the sequence of the goal points located in the free workspace; second, compute the shortest collision-free path between goal points. In this study, a genetic algorithm is used to optimize the sequence of the goal points that the robot should visit. Once the sequence of the goal points is available, our developed method called Boundary Node Method (BNM) is applied to generate an initial collision-free path between every pair of the sequenced goal points. Subsequently, an additional developed method called Path Enhancement Method (PEM) is used to find an optimal or nearoptimal path by reducing the overall initial path length. In the BNM, the robot and its immediate surroundings are simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves between goals with boundary nodes in the working environment depending on the boundary node's potential value. The potential value of each point in the working environment is calculated based on the proposed potential function. Additionally, this article investigates the multi-goal path planning problem for multi-robot systems, when each goal reached by several robots. Finally, simulations and experiments are performed on a real mobile robot to demonstrate the effectiveness of the developed methods.

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Section: Related Workmentioning

confidence: 99%

The boundary node method for multi‐robot multi‐goal path planning problems

2021

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Artificial intelligence planners for multi-head path planning of SwarmItFIX agents

et al. 2019

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Orientation Planning for Multi-Agents With Discrete-Step Locomotion and Multiple Goals

Sagar

Zlatanov

Zoppi

et al. 2018

Volume 5A: 42nd Mechanisms and Robotics Conference

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The paper addresses the coordinated path planning of mobile agents with multiple goal positions and orientations in a plane. The targeted multi-robot system uses discrete locomotion ensuring uncertainty-free localization and mapping as well as simple and robust control. It is suitable for material-handling, reconfigurable-fixturing, and mobile-manipulation tasks in a flexible-manufacturing environment. Using its three leg, and matching pin-socket couplings with the base surface, each agent either stands fixed or strides along via “Swing and Dock” (SaD) locomotion. Each mounting pin can serve both as a connecting-locking device and as a pivot of a planar rotation. Previous work offered planning solutions only for the agents’ positions. In reality, the orientation in which the agent arrives at the goal is very important because neither robot workspaces nor workcell geometries have axial symmetry. Herein, we provide for the required orientational planning by labelling the agent’s legs to keep track of its rotation. Integer Linear Programming (ILP) is used to model the path planning problem in the so augmented configuration space. The mathematical formulations are implemented and tested using a GUROBI solver. Computational results display the effectiveness of the approach.

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Multi-Goal Path Planning for Robotic Agents With Discrete-Step Locomotion

Cited by 3 publications

References 17 publications

The boundary node method for multi‐robot multi‐goal path planning problems

The boundary node method for multi‐robot multi‐goal path planning problems

Artificial intelligence planners for multi-head path planning of SwarmItFIX agents

Orientation Planning for Multi-Agents With Discrete-Step Locomotion and Multiple Goals

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