2018
DOI: 10.1109/tcst.2017.2739706
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Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic Environment

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Cited by 104 publications
(57 citation statements)
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“…Often only simple obstacle shapes, such as circular (Wang and Ding, 2014) and rectangular obstacles are considered. Another approach is based on the separating hyperplane theorem (Boyd and Vandenberghe, 2004), and allows for the separation of a convex motion system and convex obstacles, or between convex motion systems, as illustrated by (Debrouwere et al, 2013) and (Mercy et al, 2017). Recently, Sathya et al (2018) have proposed a novel constraint formulation to incorporate general obstacle shapes, described as the intersection of a set of nonlinear inequalities, in the optimization problem.…”
Section: Introductionmentioning
confidence: 99%
“…Often only simple obstacle shapes, such as circular (Wang and Ding, 2014) and rectangular obstacles are considered. Another approach is based on the separating hyperplane theorem (Boyd and Vandenberghe, 2004), and allows for the separation of a convex motion system and convex obstacles, or between convex motion systems, as illustrated by (Debrouwere et al, 2013) and (Mercy et al, 2017). Recently, Sathya et al (2018) have proposed a novel constraint formulation to incorporate general obstacle shapes, described as the intersection of a set of nonlinear inequalities, in the optimization problem.…”
Section: Introductionmentioning
confidence: 99%
“…If we limit the control points of the curve to lie within a convex region, the curve will be entirely contained within that region. This may, however, impose overly conservative bounds [17]. A second option, as suggested in [11], is to not impose the constraints at all and check afterwards for dynamic feasibility; if it is not feasible, the problem needs to be resolved one more time to guarantee constraint satisfaction.…”
Section: Dynamic Feasibilitymentioning
confidence: 99%
“…Other approaches demand the existence of a hyperplane between the vehicle and obstacle at each time instant [5], [6]. This allows the separation of general convex sets.…”
Section: A Background and Contributionsmentioning
confidence: 99%