Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications
Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications
复制标题
用于最大限度地减少持续监控应用中的不确定性的轨迹规划和优化
DOI：
10.1007/s10846-022-01676-3
复制
发表时间：
2022
影响因子：
3.3
通讯作者：
T. Rosing
中科院分区：
文献类型：
--
作者：
M. Ostertag;Nikolay A. Atanasov;T. Rosing
本文考虑使用无人机（UAV）持续监测环境现象。我们的目标是生成周期性的动态可行的无人机轨迹，最大限度地减少在一组感兴趣的点在环境中的估计不确定性。我们开发了一种优化算法，该算法在确定一组有序兴趣点的观测周期和优化连续的无人机轨迹之间进行迭代，以满足所需的观测周期和无人机动力学约束。的兴趣点的访问顺序确定使用旅行商问题（TSP），其次是一个贪婪的优化算法，以确定观测的数量，最大限度地减少卡尔曼滤波器估计的最大稳态特征值。给定兴趣点的观测周期和访问顺序，最小加加速度轨迹生成的双层优化，制定为凸二次约束二次规划。所得到的B样条轨迹满足观测时间、最大速度、最大加速度、区域进入和退出等约束条件。可行的轨迹优于现有的方法，实现可比的可观测性高达47%的旅行速度，从而降低最大估计不确定性。
This paper considers persistent monitoring of environmental phenomena using unmanned aerial vehicles (UAVs). The objective is to generate periodic dynamically feasible UAV trajectories that minimize the estimation uncertainty at a set of points of interest in the environment. We develop an optimization algorithm that iterates between determining the observation periods for a set of ordered points of interest and optimizing a continuous UAV trajectory to meet the required observation periods and UAV dynamics constraints. The interest-point visitation order is determined using a Traveling Salesman Problem (TSP), followed by a greedy optimization algorithm to determine the number of observations that minimizes the maximum steady-state eigenvalue of a Kalman filter estimator. Given the interest-point observation periods and visitation order, a minimum-jerk trajectory is generated from a bi-level optimization, formulated as a convex quadratically constrained quadratic program. The resulting B-spline trajectory is guaranteed to be feasible, meeting the observation duration, maximum velocity and acceleration, region enter and exit constraints. The feasible trajectories outperform existing methods by achieving comparable observability at up to 47% higher travel speeds, resulting in lower maximum estimation uncertainty.
DOI：
10.23919/acc.2019.8814376
发表时间：
2019-07
期刊：
2019 American Control Conference (ACC)
影响因子：
--
作者：
M. Ostertag;Nikolay A. Atanasov;Tajana Simunic
通讯作者：
M. Ostertag;Nikolay A. Atanasov;Tajana Simunic
DOI：
10.23919/acc45564.2020.9147376
发表时间：
2019-09
期刊：
2020 American Control Conference (ACC)
影响因子：
--
作者：
Samuel C. Pinto;S. Andersson;J. Hendrickx;C. Cassandras
通讯作者：
Samuel C. Pinto;S. Andersson;J. Hendrickx;C. Cassandras