Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust and safe planning frameworks. This paper proposes a systematic (re)planning framework that can resiliently generate safe trajectories under volatile conditions. Firstly, a front-end kinodynamic path is searched with force-biased motion primitives. Then we develop a nonlinear model predictive control (NMPC) as a local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces. It guarantees collision avoidance by constraining the ellipsoid of the quadrotor body expanded with the forward reachable sets (FRSs) within safe convex polytopes. Our method is validated in simulations and real-world experiments with different sources of external forces.
翻译:在一个复杂和未知的环境中,没有外来扰动知识的自适应自主导航对于在复杂和未知环境中的振动器具有重大意义。考虑外部扰动的主流是实施扰动阻断控制和跟踪路径跟踪。然而,在能源消耗方面,对追踪偏差的有力控制考虑不够周到,甚至参考路径也会变得危险和麻烦。由于最近的外部力量估计进展,有可能纳入实时能量测算器,以开发更稳健和安全的规划框架。本文件提出一个系统(再)规划框架,以便在不稳定条件下以弹性的方式生成安全轨道。首先,前端动力学路径会与强力偏向运动原体一起搜索。然后我们开发一个非线性模型预测控制器(NMPC),作为汉密尔顿-贾科比(HJ)的本地规划器,对外部力量造成的误差动态进行前方可达性分析。通过限制在安全convex聚顶形力的远端可达定型体(FRS)中扩展的二次体体体结构,保证避免碰撞。我们的方法在不同的外部实验中被验证。