## 5 Numerical Results  
In this section, simulation results are presented to validate the performance of the proposed robust joint resource allocation and trajectory planning algorithm, and are compared with three benchmark schemes. (1) Non-robust: In this scheme, we ignore the estimation error and optimize the problem under the estimated locations. (2) No-resource-allocation-optimization: In this scheme, we offload all computation tasks to UAV with fixed offloading power \(P_{o,n}=P_{o,max}\). (3) No-trajectory-planning: In this scheme, we set the UAV trajectory as a circle around users with a radius 200 m.  
According to the parameters adopted in [21,30], we consider a 500 \(\times\) 500 m\({}^{2}\) post-disaster area which includes five ground users. The location estimation errors of the users are \(\varepsilon_{n}=20\) m. The flight altitude of UAV is \(H=50\) m, and the maximum flight speed of UAV is set as \(V_{max}=25\) m/s. The detailed environment settings are given in Table 1.  
In Figure 2, we present the optimized UAV trajectories of the proposed robust joint resource allocation and trajectory planning algorithm and non-robust scheme under different battery capacity. From Figure 2, we can find that with the increase of battery capacity, the UAV can approach each user to provide services. The reason is that on the premise of completing the computation task, the larger the UAV battery capacity, the closer it can be to the users to provide efficient charging services. Note that under the same battery capacity, the proposed robust algorithm is much closer to users than the non-robust scheme. This is because in order to eliminate the impact of the worst-case location error on the users' power supply, the UAV should be close to the user greatly affected by the location error. \begin{table}
\begin{tabular}{c c c} \hline \hline \(B\) & 5 MHz & The channel bandwidth. \\ \hline \(\beta_{0}\) & \(-50\) dB & The channel power gain at distance \(d_{0}=1\) m. \\ \hline \(\sigma^{2}\) & \(10^{-9}\) W & The receiver noise power. \\ \hline \(p_{o,n}^{max}\) & 0.5 W & The maximum WIT transmit power of user. \\ \hline \(p_{u}\) & 50 W & The WPT transmit power of UAV. \\ \hline \(\eta\) & 0.15 & The energy conversion efficiency of user. \\ \hline \(C_{n},C_{u}\) & \(10^{3}\) cycles/bit & The number of CPU cycles. \\ \hline \(\theta_{n},\theta_{u}\) & \(10^{-28}\) & The effective switched capacitance. \\ \hline \(f_{n,max},f_{u,max}\) & 10 GHz & The maximum frequency of CPU. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3 shows the minimum energy gain of users of the proposed robust algorithm with the other three benchmark schemes with different battery capacity of UAV. According to Figure 3, we can find that the proposed robust algorithm obtains the highest energy gain compared with other schemes. The reason is that the proposed robust algorithm can maximize the minimum energy gain of users by jointly optimizing UAV's trajectory and offloading process, and decreasing the influence of estimation location errors, while the other three schemes only optimize two of the three variables. We can also observe that with the increase of battery capacity, the energy gain of users increases rapidly, and then slows down gradually. That is, a larger UAV battery capacity can ensure UAV approach to users to provide much more efficient charging services.As the distance between UAV and users decreases, the impact of distance on energy supply decreases gradually, which leads to the slowing down of energy gain. In addition, the energy gain of the no-trajectory-planning scheme is a constant when battery capacity is bigger than 11,000 mAh. The reason is that when the battery capacity can ensure that the UAV flies according to the fixed trajectory, the distance between UAV and user is a constant. Then, the increase of battery capacity will not affect the energy gain of users.