Within 9 months de-epithelialized grafts appeared histologically as normal bladder, surprisingly faster than an equivalent model with full-thickness grafts. The safety and function of an epithelium-free graft must be determined in a large animal model. These early data in a small animal model substantiate the feasibility and equivalency of using grafts without epithelium, which would allow application in patients with cancer.

The conventional philosophy in designing mobile networks is that network node movement should be independent of network state. However, there are practical situations where movement decisions may be modified to ensure connectivity. For example, emergency responders in a crisis region relying upon an ad hoc network may need constant reliable communications and therefore adjust their search plan to stay connected, though aspects of their mission may override their objective of staying connected. We present a discrete formulation for this problem and a method for solving it optimally. We propose a cooperative and a noncooperative algorithm, showing that the run-time of the latter is drastically more efficient with a minimal performance cost relative to optimality. I. INTRODUCTION Path planning is used for a range of applications from optimal-path navigation [1], [2] to exploration [3], [4] to the movement of robotic arms in manufacturing plants [5], [6]. Many different constraints have been explored, including formations of robots, kinematic properties of vehicles, and robotic sensors and communication.This paper focuses specifically on the movement of a team of nodes through an obstacle laden terrain with the objective of maintaining communication. The nodes can be vehicles, people, robots, or anything else that moves. The communication objective is defined as the number of strongly connected components in the network. The optimization problem seeks to minimize the average number of such components. The terrain contains obstacles which obstruct both movement and communication.Both the terrain and the physical paths of the nodes are known a priori. We wish to move our nodes in such a way that minimizes the total time to scenario completion but also minimizes the time they spend disconnected.In multi-agent systems there often appears a tradeoff between centralized optimality and distributed efficiency. This paper proposes two methods, cooperative and noncooperative, which embody the respective classes of optimization techniques. Furthermore, the distributed method is shown to be near-optimal while being low-cost computationally.

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