多约束条件下无人机航迹规划

针对多约束条件下的无人机航迹快速规划问题,建立了导航精度约束下无人机航迹规划模型,并设计了“基于Dijkstra算法的航迹规划法”求解模型。通过校正策略优选、校正方案优选和O-D邻接矩阵处理方式,简化搜索路径,降低计算量,提高执行效率,从而实现对传统Dijkstra算法的改进。在满足导航精度约束条件的前提下,以航迹长度最短和经过校正点数量最少为研究目标进行仿真实验,并将所得结果与传统Dijkstra算法和遗传算法所得结果分别进行对比,发现此算法在精度与复杂度方面均优于传统算法和遗传算法。此结果表明,导航精度约束下无人机航迹规划模型和“基于Dijkstra算法的航迹规划法”在解决多约束下无人机航迹规划问题方面具有一定的正确性、有效性和先进性。     

An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.     

电力巡检中的无人机群路径规划算法

随着无人机技术的飞速发展, 无人机被广泛用于各种领域的巡检任务. 近年来, 电力网络的规模和长度都在快速增长, 无人机因其独特的性能和优势成为了电力巡检的首选, 无人机巡检不仅能保证安全性, 还能有效地提高巡检效率, 而路径规划是其在实际应用中的关键一步. 本文提出了一种新的混合元启发式方法, 用于解决电力巡检中带有多...     

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。     

Biologically Inspired Control Of A Fleet Of Uavs With Threat Evasion Strategy

In this paper, navigation algorithms for a fleet of multiple nonholonomic UAVs capable of evading a chasing predator and also pursuing a desired target are proposed. The proposed biologically‐inspired navigation algorithms are used to define path planning trajectories which are tracked by a designed backstepping tracking controller. We implement the group of nonholonomic UAVs in an adaptive network, specifically inspired by the relationship between a school of fish and a predator. This approach approximately simulates an air combat field. To put this in context, the aim is to use a biologically inspired algorithm along with a designed controller to achieve both target pursuance and effective evasion from a predator. This is equivalent to having multiple UAVs on the same mission of attacking a target, while also aware of a predator on pursuit. The UAVs aim to maneuver and evade the predator while also coordinating their movement and behaviors in a cooperative and coherent manner.     

头脑风暴优化算法求解带转角能耗多无人机路径规划问题

多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption, MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space, DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。     

基于改进多目标蚁群算法的无人机路径规划

针对无人机SEAD任务的路径规划问题,利用VORONOI图构建初始路径,分析了路径代价计算方法,并使用改进的多目标蚁群算法对路径进行优化选择。针对该特殊应用场景,引入了各路径段与起始点—目标点连线的夹角信息作为新的启发信息,加快了算法的搜索速度,同时改进启发信息的计算公式,适当缩小各可选路径段启发信息量的差异,加强了蚁群算法的全局搜索能力。仿真结果显示,与基本多目标蚁群算法相比,改进后的算法有效提高了路径搜索的效率和质量。     

Deliberative On-Line Local Path Planning for Autonomous Mobile Robots

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.     

多无人机空中加油的最优会合航路规划

为了空中加油能面向多架无人机,本文提出了空中加油的三维最优会合航路规划算法.多架无人机分布在不同区域,需要加油机沿预定的规划航路飞行会合,以完成空中加油任务.由于加油机可同时服务的受油机数量有限,需要寻找最优分配策略将无人机预分配至不同加油区域与之会合.本文首先根据加、受油机在各加油区域的最短会合时间,将最优分配问题建模为整数线性规划问题,求解得到加油机与各无人机的最优会合点.随后,本文提出了三维空间Dubins路径延长算法,保证各无人机按照分配结果与加油机同时到达会合点.最后,分别针对二维和三维多架无人机空中加油任务进行仿真.仿真结果表明本文提出的最优会合航路规划算法得到的Dubins航路,可以保证空中加油会合任务在最短时间内完成.     

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

The path planning of autonomous mobile robots (PPoAMR) is a very complex multi-constraint problem. The main goal is to find the shortest collision-free path from the starting point to the target point. By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered. This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR, which includes the fitness value calculation method and the prior knowledge particle swarm optimization (PKPSO) algorithm. The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient. The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy, which improves the local optima evasion capability. In addition, the quintic polynomial trajectory optimization approach is devised to generate a smooth path. Finally, some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.     

导航系统中多目标路径平滑化规划的研究

路径规划是车辆、机器人出行、无人机航路推荐和计算机游戏等许多应用中的关键任务。现有的大多路径规划常简化为单目标优化问题进行求解。但在现实生活中,还需要同时考虑多种规划目标,且用于规划路径的目标之间还存在着彼此不能变换的问题。在熟知的路径规划算法（D*Lite）上提出了一种新的多目标路径平滑化规划算法-平滑多目标D*Lite算法。通过构造一条初始多目标平滑路径,当检测到环境变化时采用增量搜索思想,仅更新受影响结点并从当前结点重新进行规划得到一条新的多目标平滑路径。仿真结果表明,该算法不但能有效躲避突发障碍物,规划路径拐点较少,还能提高搜索效率,可有效应用于具有不同非交互规划目标的导航系统。     

路网约束下异构机器人系统路径规划方法

由无人机(Unmanned aerial vehicles, UAV)和地面移动机器人组成的异构机器人系统在协作执行任务时,可以充分发挥两类机器人各自的优势.无人机运动灵活,但通常续航能力有限;地面机器人载荷多,适合作为无人机的着陆平台和移动补给站,但运动受路网约束.本文研究这类异构机器人系统协作路径规划问题.为了降低完成任务的时间代价,提出一种由蚁群算法(Ant colony optimization, ACO)和遗传算法(Genetic algorithm, GA)相结合的两步法对地面机器人和无人机的路线进行解耦,同时规划地面机器人和无人机的路线.第1步使用蚁群算法为地面机器人搜索可行路线.第2步对无人机的最优路径建模,采用遗传算法求解并将无人机路径长度返回至第1步中,用于更新路网的信息素参数,从而实现异构协作系统路径的整体优化.另外,为了进一步降低无人机的飞行时间代价,研究了无人机在其续航能力内连续完成多任务的协作路径规划问题.最后,通过大量仿真实验验证了所提方法的有效性.     

Real-time path planning with limited information for autonomous unmanned air vehicles

We propose real-time path planning schemes employing limited information for fully autonomous unmanned air vehicles (UAVs) in a hostile environment. Two main algorithms are proposed under different assumptions on the information used and the threats involved. They consist of several simple (computationally tractable) deterministic rules for real-time applications. The first algorithm uses extremely limited information (only the probabilistic risk in the surrounding area with respect to the UAV's current position) and memory, and the second utilizes more knowledge (the location and strength of threats within the UAV's sensory range) and memory. Both algorithms provably converge to a given target point and produce a series of safe waypoints whose risk is almost less than a given threshold value. In particular, we characterize a class of dynamic threats (so-called, static-dependent threats) so that the second algorithm can efficiently handle such dynamic threats while guaranteeing its convergence to a given target. Challenging scenarios are used to test the proposed algorithms.     

基于多智能体强化学习的无人机群室内辅助救援

本文主要研究了在室内场景中使用多台无人机设备对受害者进行合作搜索的问题.在室内场景中,依赖全球定位系统获取受害者位置信息可能是不可靠的.为此,本文提出一种基于多智能体强化学习(MARL)方案,该方案着重对无人机团队辅助救援时的路径规划问题进行研究.相比于传统方案,所提方案在大型室内救援场景中更具优势,例如部署多台救援无...     

基于视觉引导多AGV系统的改进A*路径规划算法

研究基于视觉引导自动引导车(AGV)的改进A*路径规划算法.首先,设计一种包含导航、定位和任务信息的图形编码标志方法,AGV通过识别位于车身前方网格型路径中有序排布的编码标志进行快速定位和下一位置预判,为多AGV规划奠定基础;其次,根据网格型路径构成的动态随机网络,提出一种改进A*算法,将AGV在运动时产生的动态时间耗费作为参考指标,以实现多AGV在路径网络中的路径规划和冲突避让策略,提高固定路网资源的利用效率;最后,对多AGV在网格型路径中协同工作的场景进行仿真,实验结果表明,所提出的改进算法可以有效应用于多AGV系统,并且提升整体系统的工作效率.     

无人机三维航迹规划方法研究

航迹规划算法是无人机关键技术之一,同时也是任务规划系统（Mission Planning System）核心之一。针对固定目标规划问题,提出一种voronoi图改进算法和动态稀疏A*算法融合的三维航迹规划方法。该方法针对固定威胁目标,通过改进voronoi图规划算法快速求解二维航迹路径,然后在该路径参考下,用动态稀疏A*算法求解符合无人机飞行动力学约束的三维航迹。试验表明,该算法比动态稀疏A*算法规划速度快,并保证了航迹最优性。     

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。     

无人机三维路径规划及避障方法

针对无人机(UAV)在三维环境中如何由起始点到目标点合理地规划路径避开障碍物,提出了一种基于改进粒子群算法与滚动策略相结合的UAV路径规划与避障方法.该方法首先以UAV为中心,通过传感器建立UAV的可视区域模型;其次结合滚动策略滚动探知UAV周围环境信息;最后,利用改进的粒子群算法进行路径搜索,并加入综合转角控制提高路径的平滑性.在传统粒子群算法中加入信息素与启发函数,增强算法的全局搜索能力,并对参数进行特定设计提高算法的收敛速度.仿真结果表明,该方法可以实现实时避障,所规划的路径相对平滑,且改进算法比传统算法具有较高的收敛性.     

基于深度强化学习的多无人机电力巡检任务规划

无人机因其成本低、操控性强等优势,在电网线路与电塔的巡检任务中取得了广泛的应用。在大范围电网巡检任务中,单台无人机由于其续航半径有限,需要多架无人机协作完成巡检任务。传统任务规划方法存在计算速度慢、协作效果不突出等问题。针对以上问题,本文提出一种基于多智能体强化学习值混合网络（QMIX）的任务规划算法,采用集中训练、分散执行的框架,为每架无人机建立循环神经网络,并通过混合网络得到联合动作值函数指导训练。该算法通过设计任务奖赏函数以激发多智能体的协作能力,有效解决多无人机任务规划协作效率低的问题。仿真实验结果表明所提算法的任务时间相比于常用的值分解网络（VDN）算法减少了350.4 s。     

改进A*算法在机器人室内路径规划中的应用

传统A^*算法在面向机器人室内多U型障碍的特殊场景下规划路径时，容易忽略机器人实际大小，且计算时间较长。针对这个问题，提出一种改进A^*算法。首先引入邻域矩阵进行障碍搜索以提升路径安全性，然后研究不同类型和尺寸的邻域矩阵对算法性能的影响，最后结合角度信息和分区自适应距离信息对启发函数进行改进以提高计算效率。实验结果表明，改进A^*算法可以通过更改障碍搜索矩阵的尺寸来获得不同的安全间距，以保证不同机器人在不同地图环境下的安全性；而且在复杂大环境中与传统A^*算法相比寻路速度提高了28.07%，搜索范围缩小了66.55%，提高了机器人在遇到动态障碍时二次规划的灵敏性。