A complete multi-robot path-planning algorithm

Alotaibi, Ebtehal Turki Saho; Al-Rawi, Hisham

doi:10.1007/s10458-018-9391-2

Cited by 18 publications

(3 citation statements)

References 14 publications

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“…Push-and-Swap (Luna & Bekris, 2011) uses "swap" macro to swap locations between two adjacent dependent agents. Its variants Push-and-Rotate (De Wilde et al, 2014), Push-and-Spin (Alotaibi & Al-Rawi, 2018), etc., use more macros to handle complex situations and graphs. However, deadlocks often happen in narrow corridors and inter-agent collision-free may not be guaranteed, so the algorithms may fail to find paths even the paths exist (incompleteness).…”

Section: Sub-optimal Solvers For Centralized Mpfmentioning

confidence: 99%

“…By contrast, when adding the space cost function, the method successfully generates the extra paths ( 4 , 5 ). In this section, we compare the proposed sequential route network planning method with two kinds of state-of-theart MPF algorithms on a 2D standard test scenario: one is a two-level-based optimal method -conflict-based search (CBS) (Sharon et al, 2015), and one is a rule-based sub-optimal method -Push-and-Spin (Alotaibi & Al-Rawi, 2018). The scenario is shown in Figure 14.…”

Section: Illustration With a Toy Examplementioning

confidence: 99%

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A Route Network Planning Method for Urban Air Delivery

He¹,

He²,

Li³

et al. 2022

Preprint

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High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

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Section: Sub-optimal Solvers For Centralized Mpfmentioning

confidence: 99%

Section: Illustration With a Toy Examplementioning

confidence: 99%

A Route Network Planning Method for Urban Air Delivery

He¹,

He²,

Li³

et al. 2022

Preprint

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show abstract

“…(1) Provide framework By introducing the path planning algorithm, it is not difficult to find that the current hotspot project belongs to the intelligent path planning method. The traditional path planning method is the help method of the group intelligent path planning method [12]. Models and other steps, current research on multiple robot path planning methods will simultaneously consider multiple path optimization indexes.…”

Section: Date Mining Statistic Analysis Representationmentioning

confidence: 99%

Real Time Neural Network Path Planning Algorithm for Robot

2021

IJFET

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The emergence of robots not only changed the traditional industrial production mode, but also greatly promoted the progress of social civilization. Whether in daily life or in industrial production practice, the technical level of robots is improving every day, which emphasizes the high level of national science and technology. Robot path planning technology is an important part of robot research. The purpose of this paper is to focus on the research of robot path planning algorithm, learning and in-depth development. This paper introduces the discovery and communication of robot real-time neural network planning algorithm in machine learning, and analyzes and studies it. In order to study the robot real-time neural network path planning algorithm, through the experimental comparison of different algorithms, focus on exploring the effect of different algorithms on path planning. The research results show that the algorithm speed of robot on real-time network path is 24% higher than that of normal network path algorithm, and can be increased to 30% after deep mining, and the efficiency of machine algorithm can be increased to 35% under more complete algorithm. Therefore, the robot real-time neural network path planning algorithm can complete the task more efficiently.

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