多约束下多无人机的任务规划研究综述

高度信息化的发展使得无人机作战优势凸显。准确的无人机任务规划技术是完成给定任务的重要保障。任务分配、路径规划是构成无人机任务规划技术的两个核心部分。基于该技术,首先讨论了无人机任务规划的发展状况、分类标准、体系结构。其次,分别详细介绍了影响任务分配、路径规划的重要指标,如分类标准、约束指标、相应模型、代表算法、评价指标等,然后,分别分析对比求解任务分配的启发式算法、数学规划方法、随机智能优化算法的优缺点和求解路径规划的数学规划方法、人工势场法、基于图形学法、智能优化算法的优缺点;最后,总结了无人机任务规划存在的开放性问题、未来发展方向和研究重点。     

面向突发故障的分布式多无人机任务重规划方法

针对分布式多无人机系统执行任务时发生故障的情况,提出一种面向故障的任务重规划方法.首先,依据分布式架构,考虑通信延迟约束,建立多无人机系统遭遇故障时的局部任务重规划问题模型,设计故障无人机、健康无人机的重规划框架.依此框架,考虑无人机调度时所需的空间、时间资源,根据故障后的无人机通信拓扑,制定子系统划分规则;然后,根据子系统内在线无人机与待执行任务间的映射关系,提出基于收益动态调整规则和一致性协调规则的拍卖算法,实现针对不同情况的任务重分配;最后,考虑任务重分配与航迹重规划间的耦合关系,在任务重分配阶段引入RRT*算法预估的航迹代价,使得分配结果更合理.仿真结果表明,在考虑实际环境中无人机会发生故障的情况,该方法能够有效完成任务重规划.     

面向多目标侦察任务的无人机航线规划

针对无人机执行多目标侦察任务的航线规划问题,提出一种改进粒子群算法结合高斯伪谱法的分层航线规划方法.设计改进粒子群算法进行航线预规划,针对传统粒子群优化算法收敛速度慢、易陷入局部最优的问题,通过引入混沌映射初始化和自适应参数调整策略,加快算法收敛速度,提升解的最优性.在此基础上,结合最短路求解策略,完成对侦察任务的时序...     

基于改进强化学习算法的多无人机智能航迹规划

为实现复杂任务环境中多无人机的自主飞行, 本文采用改进的强化学习算法,设计了一种具有避碰避障功能的多无人机智能航迹规划策略。通过改进搜索策略、引入具有近似功能的神经网络函数、构造合理的立即回报函数等方法,提高算法运算的灵活性、降低无人机运算负担, 使得多无人机能够考虑复杂任务环境中风速等随机因素以及静态和动态威胁的影响, 自主规划出从初始位置到指定目标点的安全可行航迹。为了探索所提算法在实际飞行过程的可行性, 本文以四旋翼无人机为实验对象, 在基于ROS的仿真环境中验证了算法的可行性与有效性。     

基于Multi-Agent系统的多飞行器协同路径规划方法的研究

无人多飞行器(UAV)协同技术是当前分布式人工智能的一个热点领域,其中一个关键技术在于如何实现多UAV集群根据复杂环境中目标、威胁、地形变化以及各UAV之间的性能约束动态进行实时性航路规划。提出一种基于Multi-agent系统的多UAV对实时动态多目标进行路径规划的方法。其核心是基于Multi-agent系统的decen-tralized控制方案。在Multi-agent平台上,实现了agent对于环境、目标、任务等路劲规划约束条件的建模,同时提出了多agent动态路径规划方法的实现方案。方案使用DisCSP模型框架,将基于真实复杂战场环境的实时路径规划问题所涉及的多复杂限制条件,抽象成Multi-agent系统中的各个约束条件,通过多agent间Dynamic Programming过程求解多UAV实时动态多目标的路径规划和协同任务分配的ABT算法,并实现在动态威胁和地形以及动态目标下具备集群协同能力的多UAV实时仿真系统。     

Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of 'link bands' to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of human agents in indoor and outdoor scenes.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

基于图形模型动态感知下的多无人机航迹协同

为解决对当前形势估计不足以及对未来情况预测的问题.采用图形模型中动态贝叶斯网络(DBN)建立突发威胁体感知的模型,利用DBN的状态转移网络、观测转移网络及DBN的学习和推理算法,实现了对突发威胁体的动态感知.提出一种基干图形模型动态感知下的多架无人机航迹协同规划方法,并据此实现多无人机飞行航迹协同规划.仿真结果表明了这种基于图形模型动态感知下的多架无人机协同方法的正确性和可行性.     

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.     

基于改进A*算法和动态窗口法的机器人路径规划

针对移动机器人在复杂环境下(包含静态和动态环境)的路径规划效率低的问题,提出了一种改进的A*算法与动态窗口法相结合的混合算法。针对传统A*算法安全性不足的问题,采用障碍规避策略,优化节点的选择方式,增加路径的安全性;针对转折点多的问题,采用递归二分法优化策略,去除冗余节点,减少转弯次数;针对静态环境下路径平滑性不足的问题,采用动态内切圆平滑策略将折线角优化成弧度角,以增加路径的平滑性。对于传统动态窗口法的目标点附近存在障碍物时规划效果不好和容易在凹型槽类障碍物中陷入局部最优的问题,在原有的评价函数中引入了距离偏差和轨迹偏差。最后,对所提的改进A*算法和混合算法分别在静态和动态环境下与其他算法进行仿真比较。从结果可以看出,与传统混合算法相比,临时障碍环境下,路径长度和运行时间分别缩短了13.2%和65.8%;移动障碍环境下,路径长度和运行时间分别缩短了13.9%和44.9%,所提的算法提高了在复杂环境中规划路径的效率。     

3D Path Planning for Multiple UAVs for Maximum Information Collection

This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of “which DR should be visited by which UAV”. It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section 6. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions.     

基于遗传算法的移动机器人路径规划

采用动态可变长编码的方法,以栅格表示环境。针对遗传算法大型障碍物难的问题,采用follow wall行为,较好地解决了基于遗传算法的快速路径规划和大型障碍物避障问题。该算法适应任何形状的障碍物,适用于静态和动态环境中。计算机仿真表明,该算法是一种正确和高效的路径规划方法。     

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations.     

帝国竞争算法在无人机三维航线规划中的应用研究

针对实际飞行环境中无人机的三维航线规划问题,提出了一种创新启发式优化算法——牛顿帝国主义竞争算法(NICA,Newtonian imperialist competitive algorithm).该算法能够根据无人机的飞行轨迹,从起始位置到任务目标位置生成平滑的航线路径,约束航线规划,使得目标完成任务的时间最小化.该算法也能为无人机在真实地形上的航线提供最佳轨迹路径.最后通过与ICA、GA和PSO算法进行比较,验证了改进算法的有效性.结果表明:改进帝国算法提高了全局最优解的搜索能力,在收敛速度和精度上优于其他3种算法,适合用来解决无人机的三维航线规划问题.     

面向未知环境及动态障碍的人工势场路径规划算法

实际战场环境错综复杂,很多隐蔽、动态的障碍无法通过高空手段预先探测得知,因而对智能体执行任务的安全性产生威胁.针对未知且障碍形态多样的战场环境,以躲避动、静障碍,追踪目标为研究对象,提出一种面向未知环境及动态障碍的改进人工势场(Artificial Potential Field,APF)路径规划算法.在该算法中,智能...     

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles (UAVs) or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance, due to their ability to perform repetitive and tedious tasks in hazardous environments. Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional (3D) flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature. However, not a single optimization algorithms can solve all kind of optimization problem effectively. Therefore, there is dire need to integrate metaheuristic for general acceptability. To address this issue, in this paper, a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm (QGA) has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then, utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment. The performance of the developed QGA has been compared against the various metaheuristics. The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.     

Space‐time planning in changing environments : using dynamic objects for accessibility

Navigation is a critical task for agents populating virtual worlds. In the last years, numerous solutions have been proposed to solve the path planning problem in order to enhance the autonomy of virtual agents. Those solutions mainly focused on static environments, eventually populated with dynamic obstacles. However, dynamic objects are usually more than just obstacles as they can be used by an agent to reach new locations. In this paper, we propose an online path planning algorithm in dynamically changing environments with unknown evolution such as physically based‐environments. Our method represents objects in terms of obstacles but also in terms of navigable surfaces. This representation allows our algorithm to find temporal paths through disconnected and moving platforms. We will also show that the proposed method also enables several kinds of adaptations such as avoiding moving obstacles or adapting the agent postures to environmental constraints. Copyright © 2012 John Wiley & Sons, Ltd.     

基于自适应动态窗口改进细菌算法与移动机器人路径规划

针对移动机器人在复杂环境下的路径规划问题,提出一种新的自适应动态窗口改进细菌算法,并将新算法应用于移动机器人路径规划。改进细菌算法继承了细菌算法与动态窗口算法（dynamic window algorithm, DWA）在避障时的优点,能较好实现复杂环境中移动机器人静态和动态避障。该改进算法主要分三步完成移动机器人路径规划。首先,利用改进细菌趋化算法在静态环境中得到初始参考规划路径。接着,基于参考路径,机器人通过自身携带的传感器感知动态障碍物进行动态避障并利用自适应DWA完成局部动态避障路径规划。最后,根据移动机器人局部动态避障完成情况选择算法执行步骤,如果移动机器人能达到最终目标点,结束该算法,否则移动机器人再重回初始路径,直至到达最终目标点。仿真比较实验证明,改进算法无论在收敛速度还是路径规划精确度方面都有明显提升。     

融合多目标与速度控制的AGV全局路径规划

为提升AGV工作效率并改善其躲避障碍物的执行能力,提出在静态与动态环境下的全局路径规划方法——多目标与速度控制法.在静态环境下,以路径最短与平滑度最大建立路径规划的多目标数学模型,采用所提出的改进算法求解并筛选,得到AGV的行驶路径;在动态环境中,根据障碍物的运动情况,提出感应转向算法,使AGV合理躲避障碍物.结合两种环境下的转向特点,设定AGV速度控制规则,应用于静态与动态环境下的转向过程,确保AGV能够行驶得更加平稳与快速.仿真实验表明,所提出方法能够确保AGV在两种环境下自由躲避和灵活转向,提升行驶速度,提高工作效率;与常规算法对比,改进算法的求解效果在时间和精度上都显著提高.     

海流环境下多AUV多目标生物启发任务分配与路径规划算法

针对多障碍物海流环境下多自治水下机器人(AUV)目标任务分配与路径规划问题, 本文在栅格地图构建的 基础上给出了一种基于生物启发神经网络(BINN)模型的新型自主任务分配与路径规划算法, 并考虑海流对路径规 划的影响. 首先建立BINN模型, 利用此模型表示AUV的工作环境, 神经网络中的每一个神经元与栅格地图中的位 置单元一一对应; 接着, 比较每个目标物在BINN地图中所有AUV的活性值, 并选取活性值最大的AUV作为它的获 胜AUV, 实现多AUV任务分配; 最后, 考虑常值海流影响, 根据矢量合成算法确定AUV实际的航行方向, 实现AUV路 径规划与安全避障. 海流环境下仿真实验结果表明了生物启发模型在多AUV水下任务分配与路径规划中的有效性.