Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles

Abstract

Owing to the flexibility and low cost, cooperative Unmanned Aerial Vehicles(UAVs) have been attractive in multi-target positioning recently. Although it is popular and easy to accomplish, positioning based on trilateration method still faces challenges under scenarios with multiple UAVs. First, large accumulated errors will be brought if a single UAV is used to perform trilateration on same targets. Second, due to the mobility of targets, the time interval between UAVs performing twice successive distance measurement on one target cannot be long for positioning precision. Finally, the limited energy provided by onboard battery limits the time for UAVs to perform tasks. Once the energy used by some of the UAVs reaches limitation, the whole positioning mission will fail. Thus, to complete the mission of locating multiple targets, this paper is intended to minimize the maximum energy consumption among all UAVs. We formulate the problem, and decompose it into two subproblems, one of which plans the routes for UAV groups and the other plans the routes for UAVs in a group. To solve the first subproblem, a heuristic algorithm called adjusted genetic algorithm (AGA) is proposed to plan trajectories for all UAV groups under constraints on maximum energy consumption. To guarantee stable performance and reduce computation complexity, we propose an approximation algorithm, Tree Decomposition united with Christofides Algorithm (TDCA), and the approximation ratio is proved to be (3 * N /(2 * (N − 1))), where N denotes the number of UAV groups. For the second subproblem, a two-step greedy heuristic algorithm is proposed to plan trajectories for UAVs in same groups. Extensive simulations show that compared to existing algorithms, the proposed algorithms can reduce up to 26.6% maximum and 26.3% average energy consumption.

Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization and Communications Under Lognormal-Nakagami-$m$ Fading Channels

“…In [29], Shahidian et al proposed two trajectory control approaches by applying the extended Kalman filter to increase the localization accuracy. Trajectory planning was designed by Ji et al [30] for multitarget positioning using multiple UAVs who minimized the energy dissipation of UAVs performing cooperative positioning via trilateration. Demiane et al [31] designed an efficient UAV trajectory that takes into account important levels of objects in disaster scenarios.…”

“…In particular, we calculate the OP and TP for a delay-limited mode. We also derive the exact closedform expressions for the ergodic capacity and throughput [25] 2017 [26] 2018 [27] 2018 [28] 2017 [29] 2017 [30] 2020 [31] in a delay-tolerant transmission. In addition, we derive approximate expressions for both the OP and the TP at high SNRs.…”

“…The OP is defined as the probability that the received SNR at the GS is below a given threshold. When DF-relaying is used by the UAV, it can be calculated as P out (γ th ) = P r(γ ou < γ th )+P r(γ ub < γ th , γ ou ≥ γ th ), (30) where γ th is the SNR to be exceeded at the GS for correct data detection. The closed-form expression of P out is formulated in the following theorem.…”

“…Additionally, the optimality of UAV trajectory might be affected by the distribution of the GUs. Having said that the optimal trajectory design is beyond the scope of this treatise, the readers are referred to [28], [30]- [32], and [42] for design approaches.…”

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

“…In addition, we also used the Minimum Spanning Tree (MST) algorithm [26,27] to formulate the backbone route. The backbone tree construction mechanism is presented in Algorithm 2.…”