Communication coverage for independently moving robots

Abstract

Abstract-We consider the task of providing communication coverage to a group of sensing robots (sensors) moving independently to collect data. We provide communication via controlled placement of router vehicles that relay messages from any sensor to any other sensor in the system under the assumptions of 1) no cooperation from the sensors, and 2) only sensor-router or routerrouter communication over a maximum distance of R is reliable. We provide a formal framework and design provable exact and approximate (faster) algorithms for finding optimal router vehicle locations that are updated according to sensor movement. Using vehicle limitations, such as bounded control effort and maximum velocities of the sensors, our algorithm approximates areas that each router can reach while preserving connectivity and returns an expiration time window over which these positions are guaranteed to maintain communication of the entire system. The expiration time is compared against computation time required to update positions as a decision variable for choosing either the exact or approximate solution for maintaining connectivity with the sensors on-line.

Adaptive communication in multi-robot systems using directionality of signal strength

“…In reality, signal strength suffers from large variations over small displacements [Goldsmith, 2005;Lindhe et al, 2007] that these models simply do not capture. Yet, the simplicity afforded by these models has led to significant contributions including i) multi-agent coordination for coverage and flocking [Martinez et al, 2007;Schuresko and Cortes, 2009], ii) assignment of routers to clients for attaining a prescribed level of connectivity [Feldman et al, 2013;Gil et al, 2012] or throughput [Craparo et al, 2011], and iii) connectivity maintenance based on graph theoretic approaches [De Gennaro and Jadbabaie, 2006;Michael et al, 2009].…”

“…This can be seen from the observation that Line 8 from Algorithm 4 is the classic k-center solution [Feldman et al, 2013;Gil et al, 2012] under the Mahalanobis distance metric. A k-center solution will assign clients to their closest routers.…”

K-robots clustering of moving sensors using coresets

“…Gonzalez [10] suggested a 2-approximation for the k-center problem that takes time O(nk). An exact solution to this problem generally requires time exponential in k [10] and has given rise to many static approximation methods including static coresets, or sparse representative sets [1], [6], [7], [13]. These coresets can be constructed in time that is linear in both n in k, and returns a small set of size roughly k/ε d , i.e., independent of n. We then run exhaustive search algorithms, approximations, or heuristics on these small representative sets.…”

“…In our previous work [7] we constructed (static) coresets for a class of problems framed as a connected k-center problem, where we wish to compute the positions of k servers that minimize both the maximum distance from any client to its closest server, and the longest edge in the minimum Euclidean spanning tree over the servers (ie. connected centers).…”

“…In particular, we use the coreset to solve the class of connected k-center problems from [7], where centers must maintain connectivity over a Euclidean minimum spanning tree (see Eq. (2)), and are subject to maximum velocity constraints.…”