J Intell Robot Syst (2012) 66:477–494 DOI 10.1007/s10846-011-9633-x A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments Andrew J. Berry · Jeremy Howitt · Da-Wei Gu · Ian Postlethwaite Received: 6 January 2011 / Accepted: 5 September 2011 / Published online: 2 December 2011 © Springer Science+Business Media B.V. 2011 Abstract As the complexity of an unmanned ve- hicle’s operational environment increases so does the need to consider the obstacle space contin- ually, and this is aided by splitting the motion planning functionality into distinct global and lo- cal layers. This paper presents a new continuous local motion planning framework, where the out- put 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 optimi- sation, enabling faster design and increased hori- zon length. The focus of this paper is on the output space component of this framework. Bezier poly- nomial functions are used to describe local motion Electronic supplementary material The online version of this article (doi:10.1007/s10846-011-9633-x) contains supplementary material, which is available to authorized users. A. J. Berry (B ) · J. Howitt QinetiQ, Cody Technology Park, Ively Rd, Farnborough, Hampshire, GU14 0LX, UK e-mail: ajberry@qinetiq.com D. W. Gu Department of Engineering, University of Leicester, Leicester, LE1 7RH, UK I. Postlethwaite Northumbria University, Newcastle upon Tyne, NE1 8ST, UK trajectories which are constrained to vehicle per- formance limits and optimised to track a global trajectory. Development and testing is in simula- tion, 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 dy- namic 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. Keywords Motion planning · Local motion planning · Control · Receding horizon control · Model predictive control · Sense and avoid · Optimization · Unmanned · Autonomous · Unmanned air vehicle · UAV · Quadrotor Nomenclature α Potential function decay rate ϕ Heading angle γ Flight path angle τ Polynomial curve parameter λ Scaling factor for individual cost terms A Potential function scaling factor B Matrix of Bernstein basis functions for the Bezier polynomials C Design coefficients of the Bezier curve d Potential function distance