S. Gil, S. Kumar, D. Katabi, and D. Rus. 12/16/2013. “
Adaptive Communication in Multi-Robot Systems Using Directionality of Signal Strength.” In International Symposium on Robotics Research (ISRR). Singapore.
AbstractWe consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot’s current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while adapting to wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.
Adaptive Communication in Multi-Robot Teams using Directionality of Signal Strength S. Gil, D. Feldman, and D. Rus. 11/3/2013. “
Communication coverage for independently moving robots.” In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo, Japan.
AbstractWe 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.
Communication coverage for independently moving robots D. Feldman, S. Gil, B. Julian, R. Knepper, and D. Rus. 5/6/2013. “
K-robots clustering of moving sensors using clustering .” In IEEE International Conference on Robotics and Automation (ICRA). Sing.
AbstractWe present an approach to position k servers (e.g. mobile robots) to provide a service to n independently moving clients; for example, in mobile ad-hoc networking applications where inter-agent distances need to be minimized, connectivity constraints exist between servers, and no a priori knowledge of the clients' motion can be assumed. Our primary contribution is an algorithm to compute and maintain a small representative set, called a kinematic coreset, of the n moving clients.We prove that, in any given moment, the maximum distance between the clients and any set of k servers is approximated by the coreset up to a factor of (1 ± ε), where ε > 0 is an arbitrarily small constant. We prove that both the size of our coreset and its update time is polynomial in k log(n)/ε. Although our optimization problem is NP-hard (i.e., takes time exponential in the number of servers to solve), solving it on the small coreset instead of the original clients results in a tractable controller. The approach is validated in a small scale hardware experiment using robot servers and human clients, and in a large scale numerical simulation using thousands of clients.
K-robots clustering of moving sensors using clustering