Parametrised collision-free optimal motion planning algorithms in Euclidean spaces
This work addresses collision-free motion planning for robotics or autonomous systems, but it is incremental as it builds on existing optimal planning concepts with specific constraints.
The paper tackles the problem of motion planning for point-like objects in even-dimensional Euclidean spaces with up to three unknown obstacles, presenting parametrised algorithms that are optimal in terms of minimal parametrised local planner size.
We describe parametrised motion planning algorithms for systems controlling objects represented by points that move without collisions in an even dimensional Euclidean space and in the presence of up to three obstacles with \emph{a priori} unknown positions. Our algorithms are optimal in the sense that the parametrised local planners have minimal posible size.