Abstract
Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs.
Highlights
Measuring air quality has been historically performed by ground stations
The combination of Travelling Salesman Problem (TSP) and Knapsack Problem (KP) is know as Orienteering Problems (OP) [22]; defined over different prices of the goals, whereas we limit the total flight range in our problem
The discretization causes the optimal solution to the translated problem does not necessarily corresponds to optimal solution to the original Multi-UAV Sensing Problem (MUSP) the error is bounded by | T |d, where | T | is the number of sensor tasks and d is the distance for one discrete flight “step”, i.e., the discretization factor
Summary
Measuring air quality has been historically performed by ground stations. Later on, manned aircraft and satellites were used to collect necessary measurements. Unlike in the case of making alternative plans for human operators, where the utility function defining the quality of the solution is unknown (or only implicitly known only to the operator) in the case of fully autonomous UAVs, the utility function is known but the optimization problem is too complex to be solved optimally. Our proposed approach here is to use planning of alternatives to provide a diverse set of trajectories out of which final trajectories for all the UAVs are chosen. In MUSP, we can use diverse planning to provide a set of diverse trajectories out of which the most suitable trajectories for the UAVs are chosen This approach allows us to balance the quality of the solution and the required computational time, which is necessary for large-scale applications. We experimentally compare the proposed algorithm with the two base-line approaches in Section 5 and evaluate the algorithm in simulation of a real-world problem
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
