Unmanned aerial vehicles (UAV) showed great potential in improving the efficiency of parcel delivery applications in the coming smart cities era. Unfortunately, the trustworthy positioning and control algorithms of the UAV are significantly challenged in complex urban areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, for example, the system dynamics of the control can largely help with the positioning, this paper proposed a joint positioning and control method (JPCM) based on factor graph optimization (FGO), which combines sensors' measurements and control intention. In particular, the positioning measurements are formulated as the factors in the factor graph model, such as the positioning from the GNSS. The model predictive control (MPC) is also formulated as the additional factors in the factor graph model. By solving the factor graph contributed by both the positioning factor and the MPC-based factors, the complementariness of positioning and control can be fully explored. To guarantee reliable system dynamic parameters, we validate the effectiveness of the proposed method using a simulated quadrotor system which showed significantly improved trajectory following performance. To benefit the research community, we open-source our code and make it available at https://github.com/RoboticsPolyu/IPN_MPC.

翻译：无人机（UAV）在即将到来的智慧城市时代中展现出提升包裹递送效率的巨大潜力。然而，在复杂的城市区域中，无人机的可信定位与控制算法面临重大挑战。例如，全球导航卫星系统（GNSS）的泛在定位可能因周围高楼的信号反射而退化，导致定位不确定性显著增加。城市峡谷中的复杂风扰动进一步给控制算法带来了额外挑战。鉴于系统定位与控制高度相关（例如，控制的系统动力学可显著辅助定位），本文提出了一种基于因子图优化（FGO）的联合定位与控制方法（JPCM），该方法融合了传感器测量与控制意图。具体而言，定位测量（如GNSS定位）被建模为因子图模型中的因子，同时模型预测控制（MPC）也被构建为因子图模型中的附加因子。通过求解由定位因子和基于MPC的因子共同构成的因子图，可充分探索定位与控制的互补性。为确保可靠的系统动态参数，我们使用仿真四旋翼系统验证了所提方法的有效性，结果表明轨迹跟踪性能显著提升。为回馈研究社区，我们开源了相关代码，并发布于https://github.com/RoboticsPolyu/IPN_MPC。