In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.

在本文中，提出了一种用于多种无人机的在线路径规划算法。目的是从目标区域（所需区域）收集信息，同时在从当前时间开始的固定时间窗口内避开禁止区域。在执行任务期间，车辆不得违反禁区。此外，假定收集到的有关目标的信息的重要性和可靠性会随着时间的推移而降低。提出的解决方案通过遵循规划规则解决规划范围内的优化问题来找到每辆车的路径。我们解决方案的基本结构是集中式任务分配问题，它产生了接近最优的解决方案。该解决方案可以处理移动目标，弹出目标和无人飞行器损失。这是一个复杂的优化问题，它的解决方案将在很短的时间内生产出来。为了简化优化问题并几乎实时获得解决方案，我们制定了一些规则。在这些规则中，有一个在路径构造中涉及运动学约束。还有一种使用模仿人类智能的启发式方法来解决实时决策问题。仿真是在MATLAB环境中实现的。规划算法已在各种场景下进行了测试，并给出了结果。还有一种使用模仿人类智能的启发式方法来解决实时决策问题。仿真是在MATLAB环境中实现的。规划算法已在各种场景下进行了测试，并给出了结果。还有一种使用模仿人类智能的启发式方法来解决实时决策问题。仿真是在MATLAB环境中实现的。规划算法已在各种场景下进行了测试，并给出了结果。