2019
DOI: 10.1007/s12555-018-0176-9
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PSO-based Minimum-time Motion Planning for Multiple Vehicles Under Acceleration and Velocity Limitations

Abstract: This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature three-degree Bezier curves are selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity pr… Show more

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Cited by 38 publications
(11 citation statements)
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“…In comparison with other non-invasive neuroimaging modalities (i.e., fMRI, EEG, and MEG), fNIRS has the advantages of safety, lower cost, portability, tolerance of motion artifacts, good temporal resolution, and moderate spatial resolution [16]. Additionally, the development of initial dip detection [17][18][19], bundled-optodes configurations [20][21][22][23], and adaptive algorithms [24][25][26][27] have offered further opportunities to improve the temporal and spatial resolution of fNIRS.…”
Section: Introductionmentioning
confidence: 99%
“…In comparison with other non-invasive neuroimaging modalities (i.e., fMRI, EEG, and MEG), fNIRS has the advantages of safety, lower cost, portability, tolerance of motion artifacts, good temporal resolution, and moderate spatial resolution [16]. Additionally, the development of initial dip detection [17][18][19], bundled-optodes configurations [20][21][22][23], and adaptive algorithms [24][25][26][27] have offered further opportunities to improve the temporal and spatial resolution of fNIRS.…”
Section: Introductionmentioning
confidence: 99%
“…In contrast, fNIRS is a novel neuroimaging modality with the following advantages: it is non-invasive, safe, less costly, portable, and tolerant of motion artifacts (Perrey, 2008); it also has great temporal resolution and moderate spatial resolution (Ghafoor et al, 2017;Zafar and Hong, 2020). In addition, fNIRS is in progress to improve the spatial and temporal resolutions with the development of bundled-optodes configurations , detection of the initial dip (Zafar and Hong, 2017;Hong and Zafar, 2018), and combination of adaptive method (Iqbal et al, 2018;Hong and Pham, 2019;Pamosoaji et al, 2019) to improve information transfer rate.…”
Section: Introductionmentioning
confidence: 99%
“…The SCKF is a filter for estimating nonlinear models [42,43]. The filter approximates the integration of the product of a nonlinear function and its likelihood density, which is usually approximated by the Gaussian, using the sphericalradial cubature rule or spherical simplex-radial cubature rule [44][45][46][47][48].…”
Section: B Dual Kalman Filtermentioning
confidence: 99%