Communication-Based Motion Planning

Abstract

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.

Relay Positioning for Unmanned Aerial Vehicle Surveillance*

“…It is shown that while certain reconfigurations are possible using the presented algorithm, other require global decision making. While Spanos and Murray view maintaining connectivity as a constraint, others see it as a utility that can be optimized in the case of sparse connectivity (Fridman et al, 2007). When a group of agents must move between two positions and the current motion plan does not take communication constraints into consideration, small changes can be made to the trajectory, thereby improving the connectivity of the network.…”

Optimal placement of UV-based communications relay nodes

“…where the objective function Our interest in this problem is motivated by the communications relay problems common, for instance, in robotics [2][3][4]16,17,19,21,27,[30][31][32]34], where it is required to survey some target located in t and convey the gathered information back to a base station located in s. One or more unmanned aerial or ground vehicles (UAVs or UGVs) are used as relays. The set X is defined by the terrain within the area of interest.…”