Optimizing communication and computation for multi-UAV information gathering applications

Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme

Effect of leader placement on robotic swarm control

Human control of a robotic swarm entails selecting a few in-fluential leaders who can steer the collective efficiently and robustly. However, a clear measure of influence with respect to leader position is not adequately studied. Studies with animal systems have shown that leaders who exert strong couplings may be located in front, where they provide energy benefits, or in the middle, where they can be seen by a larger section of the group. In this paper, we systematically vary number of leaders and leader positions in simulated robotic swarms of two different sizes, and assess their effect on steering effectiveness and energy expenditure. In particular, we analyze the effect of placing leaders in the front, middle, and periphery, on the time to converge and lateral acceleration of a swarm of robotic agents as it performs a single turn to reach the desired goal direction. Our results show that swarms with leaders in the middle and periphery take less time to converge than swarms with leaders in the front, while the lateral acceleration between the three placement strategies is not different. We also find that the time to converge towards the goal direction reduces with the increase in percentage of leaders in the swarm, although this value decays slowly beyond the percentage of leaders at 30%. As the swarm size is increased, we find that the leaders in the periphery become less effective in reducing the time to converge. Finally, closer analysis of leader placement and coverage reveals that front leaders within the swarm tend to expand their coverage and move towards the center as the maneuver is performed. Results from this study are expected to inform leader placement strategies towards more effective human swarm interaction systems

Optimal rendezvous trajectory for unmanned aerial-ground vehicles

Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low cost aerial surveillance and mapping applications in remote regions. There is however a key limitation, which is the fact that low cost UAVs have limited fuel capacity and hence require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in 3D taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach