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A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments | Journal of Intelligent & Robotic Systems Skip to main content
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A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

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Abstract

As the complexity of an unmanned vehicle’s operational environment increases so does the need to consider the obstacle space continually, and this is aided by splitting the motion planning functionality into distinct global and local layers. This paper presents a new continuous local motion planning framework, where the output and control space elements of the traditional receding horizon control problem are separated into distinct layers. This separation reduces the complexity of the local motion trajectory optimisation, enabling faster design and increased horizon length. The focus of this paper is on the output space component of this framework. Bezier polynomial functions are used to describe local motion trajectories which are constrained to vehicle performance limits and optimised to track a global trajectory. Development and testing is in simulation, targeted at a nonlinear model of a quadrotor unmanned air vehicle. The defined framework is used to provide situation-aware tracking of a global trajectory in the presence of static and dynamic obstacles, as well as realistic turbulence and gusts. Also demonstrated is the immediate-term decentralised deconfliction of multiple unmanned vehicles, and multiple formations of unmanned vehicles.

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Authors and Affiliations

  1. QinetiQ, Cody Technology Park, Ively Rd, Farnborough, Hampshire, GU14 0LX, UK

    Andrew J. Berry & Jeremy Howitt

  2. Department of Engineering, University of Leicester, Leicester, LE1 7RH, UK

    Da-Wei Gu

  3. Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

    Ian Postlethwaite

Authors
  1. Andrew J. Berry
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  2. Jeremy Howitt
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  3. Da-Wei Gu
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  4. Ian Postlethwaite
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Corresponding author

Correspondence to Andrew J. Berry.

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Berry, A.J., Howitt, J., Gu, DW. et al. A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments. J Intell Robot Syst 66, 477–494 (2012). https://doi.org/10.1007/s10846-011-9633-x

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  • DOI: https://doi.org/10.1007/s10846-011-9633-x

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