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In the paper, we study the motion planning problem of a mobile robot in the plane. The goal is to design output feedback control such that the resulting path of a mobile robot satisfies desired linear temporal logic (LTL) specifications. Our control strategy is divided into a local output feedback control problem and a supervisory control for LTL specifications. For the former one, we design output...
The paper studies reachability problems of autonomous affine systems in polytopes. Our goal is to find in a given polytope both the largest positive invariant set and backward reachable sets (or attraction domains) of facets. Special attention is paid to the stable invariant affine subspace. After presenting several useful properties of these sets, a partition procedure is given so that the polytope...
The paper studies self-deployment and coverage problems in a bounded region with mobile sensors (agents). A distributed self-deployment strategy is designed for the purpose of coverage based on artificial potential methods. It is shown that collections of mobile sensors eventually converge to a steady configuration while avoiding colliding each other and avoiding colliding with the boundary by employing...
The paper proposes a novel approach based on hybrid control and reachability specification for cooperative target tracking with multiple unicycle-type robots. Robots are steered to get closer to the target when they are far and then switch to coordinate their motion in order to cooperatively capture the target by eventually moving on a circle around the target with equal angular distance between each...
The paper studies hierarchical pursuit strategies for groups of mobile agents in the plane. It is shown that fascinating global patterns emerge from simple two-layer pursuit schemes, including rendezvous, uniform circular motion, complex circular motion, concentric circular motion, and concentric logarithmic spiral motion. Both rigorous analysis and simulations are provided.
A double sine basis function neural network for the design of 2D lowpass filters is presented. This neural network is contrived to have an energy function that coincides with the sum-squared error of the approximation problem at hand and by ensuring that the energy is a monotonic decreasing function, the approximation problem can be solved. The training theorem is proposed, and design of the 2D lowpass...
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