Abstract—Cooperation between multiple autonomous vehicles requires inter-vehicle communication, which in many scenarios must be established over an ad-hoc wireless network. This paper proposes an optimization-based approach to the deployment of such mobile robotic networks. A primal-dual gradient descent algorithm jointly optimizes the steady-state positions of the robots based on the specification of a high-level task in the form of a potential field, and routes packets through the network to support the communication rates desired for the application. The motion planning and communication objectives are tightly coupled since the link capacities depend heavily on the relative distances between vehicles. The algorithm decomposes naturally into two components, one for position optimization and one for communication optimization, coupled via a set of Lagrange multipliers. Crucially and in contrast to previous work, our method can rely on on-line evaluation of the channel capacities during deployment instead of a prespecified model. In this case, a randomized sampling scheme along the trajectories allows the robots to implement the algorithm with minimal coordination overhead. Index Terms—Mobile wireless network optimization, unmanned vehicle systems, robot motion planning, primal-dual optimization algorithms. I.

This paper describes new algorithms for mobile backbone network optimization. In this hierarchical communication framework, mobile backbone nodes (MBNs) are deployed to provide communication support for regular nodes (RNs). While previous work has assumed that MBNs are unconstrained in position, this work models constraints in MBN location. This paper develops an exact technique for maximizing the number of RNs that achieve a threshold throughput level, as well as a polynomial-time approximation algorithm for this problem. The approximation algorithm carries a performance guarantee of 12, and we demonstrate that this guarantee is tight in some problem instances.

Abstract — We consider the problem of satisfying commu-nication 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. I.

Abstract — A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to con-sider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure that agents support both data-rate and interconnectivity bit-error-rate requirements during task execution. System performance is characterized through experiments both in simulation and in outdoor flight testing with a team of three UAVs. I.

Abstract—We consider the scenario where a mobile robot needs to visit a number of Points of Interest (POIs) in a workspace, gather their generated bits of information, and successfully transmit them to a remote station, while operating in a realistic communication environment, minimizing its total energy consumption (including both motion and communication costs), and under time and reception quality constraints. We are interested in the co-optimization of the communication and motion strategies of the robot such that it finds the optimal trajectory (the order in which it visits all the POIs) and optimally co-plans its communication and motion strategies, including motion speed, stop times, communication transmission rate and power. By co-optimizing the usage of both communication and motion energy costs and using realistic probabilistic link metrics that go beyond the commonly-used disk models, we show how the overall problem can be posed as a Mixed Integer Linear Program (MILP) and characterize several properties of the co-optimized solution. For instance, we derive conditions under which the optimal trajectory becomes the minimum-length trajectory as well as conditions under which the trajectory deviates from the minimum-length one to visit areas with very high connectivity. We further characterize if/when it is beneficial for the robot to incur motion energy to find a better spot for communication. Moreover, we derive conditions that relate the co-optimized communication and motion strategies and clearly show the interplay between the two. Finally, our simulation results with real channel and motion parameters confirm the analysis and show considerable energy saving. I.