3-D Optimal Surveillance Trajectory Planning for UAVs: A Machine Learning Approach

Project Title

3-D Optimal Surveillance Trajectory Planning for UAVs: A Machine Learning Approach

Project Area of Specialization Artificial IntelligenceProject Summary

The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in
both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse
of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner
based on particle swarm optimization and a proposed surveillance area importance updating mechanism
aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones.
We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and
surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner.
Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially
visit important areas while obtaining a high fitness value in various practical situations.

Project Objectives

The objective of this project is to derive the optimal surveillance trajectories for multiple monitoring drones to surveil a certain operational area, and to detect the existence of illegal drones (IDrs). To solve this problem, we propose a trajectory planner based on PSO and surveillance area priority. Moreover, we extend our trajectory planner to a 3D environment. Using the proposed trajectory planner, the optimal trajectories can be obtained from all possible trajectories in accordance with the proposed fitness function. In our proposed multi-objective fitness function, not only the energy consumption (EC) but the UAV maneuverability, flight risk,
and surveillance area priority are also jointly considered costdeterminant. Taking into consideration all these aspects make our approach more practical in UAV trajectory planning. The rest of the project is organized as follows. Section II presents the problem description n, along with explanations of terrain and trajectory representation. A multi-objective fitness function for trajectory optimization is introduced in Section III. Then, the proposed surveillance area priority updating mechanism is presented in Section IV, and the
trajectory planner is described in Section V. In Section VI,
the simulation results and performance analysis of the proposed trajectory planner are illustrated in detail. Finally, we provide concluding remarks in Section VII.

Project Implementation Method

In our implementation, we discretize the whole operational
area into several small unit areas called cells, as shown
in Figure 2, in which the area in red represents the restricted area. We assume that an MDr can cover four cells from a certain position (waypoint) depending on the coverage slope of the camera imaging sensor on board ( the areas marked in blue)In our implementation, the trajectories generated by the optimization algorithm are a sequence of three-dimensional waypoints. Therefore, a feasible path is encoded as a vector where the element wi = (xi, yi,zi) represents the i-th waypoint, as shown in (2):
Trajectory = (w1,w2, . . . ,wNw) (2)
where Nw is the number of waypoints in a feasible trajectory

Benefits of the Project

The main contribution of The objective of this project is to derive the optimal surveillance trajectories for multiple monitoring drones to surveil a certain operational area, and to detect the existence of illegal drones (IDrs). To solve this problem, we propose a trajectory planner based on PSO and surveillance area priority. Moreover, we extend our trajectory planner to a 3D environment. Using the proposed trajectory planner, the optimal trajectories can be obtained from all possible trajectories in accordance with the proposed fitness function. In our proposed multi-objective fitness function, not only the energy

consumption (EC) but the UAV maneuverability, flight risk,
and surveillance area priority are also jointly considered costdeterminant. Taking into consideration all these aspects make our approach more practical in UAV trajectory planning. The rest of the project is organized as follows. Section II presents the problem description n, along with explanations of terrain and trajectory representation. A multi-objective fitness function for trajectory optimization is introduced in Section III. Then, the proposed surveillance area priority updating mechanism is presented in Section IV, and the trajectory planner is described in Section V. In Section VI,
the simulation results and performance analysis of the proposed trajectory planner are illustrated in detail. Finally, we provide concluding remarks in Section VII.

Technical Details of Final Deliverable

we propose a trajectory planner for multiple UAVs and apply it to MDrs to surveil a certain operational area to detect the existence of IDrs. To evaluate the trajectories generated by the proposed trajectory planner, we then introduce a multi-objective fitness function that has
eight optimization indexes in terms of UAV maneuverability, energy consumption, flying risk, and surveillance area priority. The optimal trajectories are obtained by maximizing the fitness function values. Moreover, we also propose a surveillance area importance updating mechanism to effectively consider new events that happen in the operational area.The simulation results prove that our proposals can obtain collision-free trajectories for multiple UAVs with high fitness values, and they show highly dynamic environmental adaptability. Currently, we considered the two MDrs over several
flights times to evaluate the performance of the proposed
multiple-UAV trajectory planner. We meant to figure out
the feasibility of the three dimension optimal surveillance
trajectory planning for multiple UAVs by using PSO with
surveillance area priority. We will consider more than two
MDrs in our future work because our proposal can effectively perform to achieve the collision-free trajectories for multiple UAVs with high fitness values and adapting the highly dynamic environment.

Final Deliverable of the Project HW/SW integrated systemCore Industry TelecommunicationOther Industries IT Core Technology Artificial Intelligence(AI)Other Technologies RoboticsSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
Development of Machine learning algorithm	Equipment	1	40000	40000
Drones	Equipment	2	15000	30000
Other Drone parts	Miscellaneous	1	10000	10000