面向目标跟踪的多机协同通信保持控制

由于无人机存在通信和测量约束的情况,远程无人机执行持续目标跟踪任务时无法直接与地面站保持通信,需要其他无人机作为通信中继方可与地面站建立可靠的通信连接.基于Dubins曲线,采用最小转弯半径和航向调整相结合的方法对具有初始和终止航向角约束的多无人机进行协同航路规划,确保所有无人机同时到达指定位置,形成多机协同通信保持的初始构型.针对随机移动目标,在多机协同通信保持的动态过程中,考虑平台性能、通信约束、碰撞规避等约束条件,采用非线性模型预测控制(NMPC)实现无人机协同分布式在线优化.在确保无人机通信中继保持的前提下,有效提高了算法的实时性.仿真结果表明了该算法的有效性.     

基于ROS的无人配送车导航系统的设计与实现

针对无人配送车在自主导航过程中存在的寻路效率低、避障能力弱、转折幅度过大等问题,该文采用搭载机器人操作系统(ROS)的Turtlebot3机器人作为无人配送车,设计并实现了高效稳定的无人配送车自主导航系统。ROS是专门用于编写机器人软件的灵活框架,对其集成的SLAM算法进行改进,以完成无人配送车在封闭园区环境中的即时定位与地图构建,同时对ROS导航功能包集成的路径规划算法进行改进,使无人配送车在已知环境地图中规划生成出适合无人配送车工作的路径和有效避开障碍物。最后在Gazebo仿真环境中对无人配送车自主导航系统进行测试与验证。仿真试验结果表明,设计实现的无人配送车导航系统能够很好地满足无人配送车在封闭园区中的自主导航功能。     

自适应斥力系数的无人机路径规划

使用人工势场法进行无人机路径规划时,往往存在目标不可达、运动轨迹迂回反复和路径长度过长等问题.传统的人工势场法不能根据环境具体信息对斥力系数进行调整,而现有的改进方法不能在自适应调整斥力系数的同时兼顾规划效果和规划时长.针对以上问题,提出了一种基于深度学习的无人机自适应斥力系数路径规划方法.首先通过融合遗传算法与人工势场法找出在特定环境下最合适的斥力系数样本集,其次利用该样本集训练残差神经网络,最后通过残差神经网络计算适应环境的斥力系数,进而使用人工势场法进行路径规划.仿真实验表明,该方法在一定程度上解决了人工势场法规划中目标不可达、运动轨迹迂回反复和路径长度过长等问题,规划效果和规划时长方面均有优异表现,能很好地满足无人机路径规划中对当前环境的自适应要求和快速规划的要求.     

无人机动态路径规划可视化仿真系统研究

针对现阶段无人机动态路径规划算法仿真可视性差和飞行环境仿真不足等问题,提出了一种基于Cesium的无人机动态路径规划可视化仿真系统;该系统在基于Client/Server(B/S)架构基础上,使用开源三维虚拟地球Cesium可视化引擎,按照可视化系统的总体设计及流程,利用坐标系转换方法与三维可视化技术,构建了一个无人机在真实地理环境中动态路径规划可视化仿真系统,实现了三维人工势场法路径规划的动态可视化仿真展示;实验表明,该系统不仅能够直观展示无人机在真实地理环境下的动态路径规划过程效果,还能够为相关研究提供直观、全面的可视化分析和评估手段。     

Autonomous Aerial Radio Repeating Using an A*-Based Path Planning Approach

In the event of a disaster, first responders must rapidly gain situational awareness about the environment in order to plan effective response operations. Unmanned ground vehicles are well suited for this task but often require a strong communication link to a remote ground station to effectively relay information. When considering an obstacle-rich environment, non-line-of-sight conditions and naive navigation strategies can cause substantial degradations in radio link quality. Therefore, this paper incorporates an unmanned aerial vehicle as a radio repeating node and presents a path planning strategy to cooperatively navigate the vehicle team so that radio link health is maintained. This navigation technique is formulated as an A*-based search and this paper presents the formulation of this path planner as well as an investigation into strategies that provide computational efficiency to the search process. The path planner uses predictions of radio signal health at different vehicle configurations to effectively navigate the vehicles and simulations have shown that the path planner produces favorable results in comparison to several conceivable naive radio repeating variants. The results also show that the radio repeating path planner has outperformed the naive variants in both simulated environments and in field testing where a Yamaha RMAX unmanned helicopter and a ground vehicle were used as the vehicle team.     

Aerial online mapping on-board system by real-time object detection for UGV path generation in unstructured outdoor environments

An optimal path provides efficient operation of unmanned ground vehicles (UGVs) for many kinds of tasks such as transportation, exploration, surveillance, and search and rescue in unstructured areas that include various unexpected obstacles. Various onboard sensors such as LiDAR, radar, sonar, and cameras are used to detect obstacles around the UGVs. However, their range of view is often limited by movable obstacles or barriers, resulting in inefficient path generation. Here, we present the aerial online mapping system to generate an efficient path for a UGV on a two-dimensional map. The map is updated by projecting obstacles detected in the aerial images taken by an unmanned aerial vehicle through an object detector based on a conventional convolutional neural network. The proposed system is implemented in real-time by a skid steering ground vehicle and a quadcopter with relatively small, low-cost embedded systems. The frameworks and each module of the systems are given in detail to evaluate the performance. The system is also demonstrated in unstructured outdoor environments such as in a football field and a park with unreliable communication links. The results show that the aerial online mapping is effective in path generation for autonomous UGVs in real environments.     

A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing UAVs

This paper discusses the design and software-in-the-loop implementation of adaptive formation controllers for fixed-wing unmanned aerial vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.      

基于激光雷达的机器人改进人工势场路径规划研究

人工势场法路径规划需要建立在已知环境下障碍物分布位置的基础之上,而激光雷达传感器可以用于未知环境下障碍物分布位置的探测和获取,因此可以将两者结合,从而解决移动机器人未知环境下路径规划问题。该课题建立在人工势场法理论基础之上,在Matlab中构建路径规划仿真图形用户界面,利用机载激光雷达传感器探测获取障碍物的位置分布信息,通过串口将数据传输至Matlab软件中并显示。在Matlab软件下编写人工势场路径规划的实现算法,进行仿真实验。实验结果表明,传统人工势场法路径规划存在的两个问题,分析原因后给出一种改进的人工势场法,并在之前的图形用户界面下继续进行仿真实验。仿真结果表明：改进的人工势场法有效地实现了路径优化的目标。     

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.     

Current trends in the development of intelligent unmanned autonomous systems

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.     

无人机反应式扰动流体路径规划

针对复杂三维障碍环境,提出一种基于深度强化学习的无人机(Unmanned aerial vehicles, UAV)反应式扰动流体路径规划架构.该架构以一种受约束扰动流体动态系统算法作为路径规划的基本方法,根据无人机与各障碍的相对状态以及障碍物类型,通过经深度确定性策略梯度算法训练得到的动作网络在线生成对应障碍的反应系数和方向系数,继而可计算相应的总和扰动矩阵并以此修正无人机的飞行路径,实现反应式避障.此外,还研究了与所提路径规划方法相适配的深度强化学习训练环境规范性建模方法.仿真结果表明,在路径质量大致相同的情况下,该方法在实时性方面明显优于基于预测控制的在线路径规划方法.     

基于改进人工势场法的无人机路径规划算法

针对传统的人工势场(APF)法无法适应复杂环境而陷入局部停滞状态、路径不够平滑等不足,提出了改进的人工势场法。首先,该算法对威胁的连通性进行分析,借鉴几何拓扑学思想得到可行解域。其次,该算法在可行解域内进行航迹点预规划。预规划基于威胁分布的全局性信息,弥补人工势场法易陷入局部最小而无法找到可行路径的不足。最后,该算法改进人工势场法引力函数,通过多次迭代,并进行曲率检查以获得足够平滑的可飞路径。仿真结果表明改进算法能够满足无人机路径规划的要求,且简便可行,具有较强寻优能力及适应性。     

基于改进粒子群算法的UAV航迹规划方法

结合当前无人机集群发展趋势,针对航迹规划算法和策略问题开展研究,在分析经典粒子群算法和传统航迹规划方法基础上,提出了一种基于改进粒子群算法的航迹规划方法,将无人机航迹规划分为整体航迹规划和节点间航迹规划两部分,针对两部分对于搜索速度和解的精度的不同需求,结合环境模型及约束条件,分别设计粒子群航迹规划算法的评价函数;对于节点间粒子群航迹规划,通过设计分段式惯性权重调整公式改进粒子群算法,在保证了算法的搜索速度的同时,提高了航迹规划解的精度。通过仿真验证了该方法的正确性和可行性,横向对比其他算法策略分析了该方法的优越性。最后在算法自主实时性方向上对于后续的工作开展提出了期望。     

UAV path planning using artificial potential field method updated by optimal control theory

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.     

On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for a team of autonomous aerial vehicles with holonomic constraints in environments with obstacles. Our approach uses Pythagorean Hodograph (PH) curves to connect vertices of the tree, which makes it possible to generate paths for which the main kinematic constraints of the vehicle are not violated. These paths are converted into trajectories based on feasible speed profiles of the robot. The smoothness of the acceleration profile of the vehicle is indirectly guaranteed between two vertices of the RRT tree. The proposed algorithm provides fast convergence to the final trajectory. We still utilize the properties of the RRT to avoid collisions with static, environment bound obstacles and dynamic obstacles, such as other vehicles in the multi-vehicle planning scenario. We show results for a set of small unmanned aerial vehicles in environments with different configurations.     

Asynchronous double-precision windows based unmanned aerial vehicle real-time path planning

A real-time path planning approach based on asynchronous double-precision windows is proposed for unmanned aerial vehicles (UAVs). In this proposed method, cursory paths and elaborate paths are planned respectively in the global and local windows. Specifically, global cursory path planner and local elaborate path planner are integrated by rolling two windows on different frequencies with different modes. Simulation results demonstrate that the proposed approach is effective for realizing a balance between t...     

基于滚动时域的无人机动态航迹规划

针对带有动力学约束的多旋翼无人机航迹规划问题,提出了一种基于滚动时域控制和快速粒子群优化(RHC-FPSO)方法。该方法引入了基于VORONOI图的代价图方法说明从航迹端点到达目标点的距离估计。根据滚动时域和人工势场法的思想,将路径规划问题转化为优化问题,以最小距离和其他性能指标为代价函数。设计评价函数准则,按照评价准则使用变权重粒子群优化算法求解。针对无人机靠近危险区飞行的问题,将斥力场引入到代价函数中,提升其安全性。仿真实验结果显示,使用文中方法可以有效地在满足约束条件下穿过障碍物区域,以及在复杂环境下可以动态计算。     

基于改进人工势场法的机器人避障及路径规划研究

在不确定和复杂的移动环境中,利用传统的人工势场法进行机器人避障很难满足对环境动态适应性的需要。提出了一种相对速度的改进的人工势场法,针对于传统的路径规划中局部最小值问题,提出设置中间目标点的方法,给机器人一个外力以避免其在局部最小点处停止或者徘徊,确保机器人能够逃出最小值陷阱并顺利到达目标位置。最后在Matlab平台上进行了仿真实验,实验结果表明,改进后的人工势场法能较好地实现动态环境下移动机器人的路径规划。     

A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

Applied Intelligence - The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment is an important part of the entire UAV’s autonomous control system. In the...     

POMDP-APF：一种基于POMDP模型的APF无人机路径规划策略

针对无人机在路径规划过程中会遇到静态或者动态的障碍物,从而导致路径规划失败的问题,提出一种基于部分可观测马尔可夫决策过程（partially observable markov decision process,POMDP）模型的人工势场（artificial potential field,APF）无人机路径规划策略（POMDP-APF）。首先使用传感器获得的障碍物信息结合POMDP模型预测障碍物的未来位置,为无人机的路径规划做准备;其次,提出一种新的基于障碍物的正方体外接球的模型,保障无人机在路径规划过程中的安全性;最后,结合改进的APF算法实现无人机的路径规划。仿真结果表明,POMDP-APF策略在无人机实时路径规划中具有良好的可行性和有效性,使无人机能够有效避开障碍物,同时路径长度以及耗费时间更短。