We study optimal Multi-robot Path Planning (MPP) on graphs, in order to improve the efficiency of multi-robot system (MRS) in the warehouse-like environment. We propose a novel algorithm, OMRPP (One-way Multi-robot Path Planning) based on Integer pro
gramming (IP) method. We focus on reducing the cost caused by a set of robots moving from their initial configuration to goal configuration in the warehouse-like environment. The novelty of this work includes: (1) proposing a topological map extraction based on the property of warehouse-like environment to reduce the scale of constructed IP model; (2) proposing one-way passage constraint to prevent the robots from having unsolvable collisions in the passage. (3) developing a heuristic architecture that IP model can always have feasible initial solution to ensure its solvability. Numerous simulations demonstrate the efficiency and performance of the proposed algorithm.
The skew mean curvature flow is an evolution equation for $d$ dimensional manifolds embedded in $mathbb{R}^{d+2}$ (or more generally, in a Riemannian manifold). It can be viewed as a Schrodinger analogue of the mean curvature flow, or alternatively a
s a quasilinear version of the Schrodinger Map equation. In this article, we prove small data local well-posedness in low-regularity Sobolev spaces for the skew mean curvature flow in dimension $dgeq 4$.
We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This com
bination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.
