基于RRT算法的无人机路径规划应用研究

经典的路径规划算法大都需要在全局已知空间中对环境进行建模,包括人工势场法、遗传算法、启发式算法、仿生学算法等.由于需要预先构建环境,因此这些方法并不适合解决在高维度空间中的路径规划问题.基于快速扩展随机树(RRT)的路径规划方式其优势在于可以避免对全局环境的构建,通过对状态空间进行随机采样,检测碰撞点,能够有效地解决在...     

面向动态障碍物的三维A*优化避障算法研究

针对空间站舱内漂浮物、太空碎片密集区域等复杂场景下的动态避障问题,本文提出了一种面向动态障碍物的三维 A*优化避障算法,主要用于完成空间站舱内飞行器和小型空间机器人在上述环境下的路径规划任务。该算法把传统二维 A*算法推广到了三维,然后通过模糊判断规则对路径进行优化,实现运动路径的实时优化,降低在三维无重力环境下的能量消耗。仿真结果表明,该算法可以对三维环境下动态障碍物进行有效避障,并且优化指标提高将近一倍, 能够有效降低能量的消耗。     

基于遗传算法和人工势场法的路径规划

在移动机器人的研究中,路劲规划是一个最基本也最复杂的问题。为了得到适合于机器人行走,以及全局最优的路径,采用遗传算法与人工势场法相结合的方法进行机器人的路径规划。首先采用遗传算法规划出全局最优或近似最优的无碰撞路径;再通过改进的人工势场法优化路径,增加路径节点,使路径更平滑。仿真实验结果表明,使用该方法所规划的路径是有效的和可行的。     

Mitigating multi-path fading in a mobile mesh network

By using robots as routers, a team of networked robots can provide a communication substrate to establish a wireless mesh network. The mobile mesh network can autonomously optimize its configuration, increasing performance. One of the main sources of radio signal fading in such a network is multi-path propagation, which can be mitigated by moving the senders or the receivers on the distance of the order of a wavelength. In this paper, we measure the performance gain when robots are allowed to make such small movements and find that it may be as much as 270%. Our main contribution is the design of a system that allows robots to cooperate and improve the real-world network throughput via a practical solution. We model the problem of which robots to move as a distributed constraint optimization problem (DCOP). Our study includes four local metrics to estimate global throughput.     

一种改进RRT的多机器人编队路径规划算法

多机器人路径规划是机器人领域的一个热点问题,相比于单机器人路径规划,其算法难度和复杂度都有所增加,在规划时需要兼顾多机避障、相互协作等难点问题.本文提出一种改进快速扩展随机树的多机器人编队路径规划算法,用于解决多机器人在复杂环境下的编队路径规划问题.针对多机器人在编队规划中的位置约束问题,定义机器人之间的领航-跟随结构,并对机器人队形建模.针对规划过程中编队朝向变化问题,建立搜索树扩展方向与队形方向之间的联系,通过调整队形方向改变规划时的编队朝向.针对具有质点模型和非完整约束动力学模型两种不同模型的多机器人系统,分别进行了仿真实验.仿真结果表明该算法在处理多机器人编队路径规划问题时可以取得良好的效果.     

改进的人工势场法在机器人足球路径规划中的应用

传统的人工势场法在静态的路径规划中有较好的效果,对于足球机器人这类对抗性与实时性比较强的动态环境的路径规划往往存在一定的缺陷。由于传统的人工势场法是单纯的考虑势能与距离间的关系,所以在实际的路径规划中有些不必要的避障也考虑在内,无形中就增加了计算的繁琐度。在改进的人工势场法中我们引进了机器人与障碍物的相对位置与相对角度以及机器人与障碍物的距离跟机器人与目标点的距离的联系,根据障碍物所处的相对位置以及与机器人的相对角度来确定障碍物对机器人的威胁程度,从而来确定斥力的大小。在传统的算法中机器人所受的引力随距离减小而减小,斥力随距离减小而增加。所以当障碍物出现在目标点附近且在机器人威胁区域内时,往往会因为斥力相对引力足够大而使机器人无法到达目标点。所以在考虑斥力时我们有必要把机器人与目标点的距离也考虑在内。最后本文用仿真方法证明该方法的可行性。     

An autonomous Wireless Networked Robotics System for backbone deployment in highly-obstructed environments

A Wireless Networked Robotics System can assist in settings that lack infrastructure e.g., urban search and rescue. A team of networked mobile robots can provide a communication substrate in those settings by acting as routers in a wireless mesh network. We study the problem of deploying a few mobile robots, and how to position them, so that all clients using the resulting robotic network are connected and all network links satisfy minimum rate requirements. The key challenge we address is that in an environment with obstacles the strength of a wireless link is a non-monotonic function of the distance between the link end-points. The problem is thus fundamentally one of making router placement decisions in a non-metric space. Our approach to the problem is based on virtual potential fields. Clients and environmental obstacles are modeled as virtual charged particles exerting virtual forces on the robots. We validate our algorithm with physical robots in an indoor environment and demonstrate that we are able to get feasible solutions.     

A genetic technique for robotic trajectory planning

There are many multi-stage optimization problems that are not easily solved through any known direct method when the stages are coupled. For instance, the problem of planning a vehicle's control sequence to negotiate obstacles and reach a goal in minimum time is investigated. The vehicle has a known mass, and the controlling forces have finite limits. A genetic programming technique is developed that finds admissible control trajectories that tend to minimize the vehicle's transit time through the obstacle field. The immediate application is that of a space robot that must rapidly traverse around two or three dimensional structures via application of a rotating thruster or non-rotating on-off thrusters. (An air-bearing floor test-bed for such vehicles is located at the Marshal Space Flight Center in Huntsville, Alabama.) It appears that the developed method is applicable to a general set of optimization problems in which the cost function and the multi-dimensional multi-state system can be any non-linear functions that are continuous in the operating regions. Other applications include: the planning of optimal navigation pathways through a traversability graph, the planning of control input for underwater maneuvering vehicles which have complex control state-space relationships, the planning of control sequences for milling and manufacturing robots, the planning of control and trajectories for automated delivery vehicles, and the optimization of control for racing vehicles and athletic training in slalom sports.     

A path tracking control system for autonomous mobile robots: an experimental investigation

A path tracking control system developed for autonomous mobile robots driven by wheels is described. In conventional approaches, the path is usually planned by smooth curves with curvature-continuity and a path tracking controller is independently designed to compensate the path error occurring in the navigation. However, smooth path planning is difficult to execute on-line due to the computational burden. In addition, the conventional path tracking algorithm often causes unpredictable tracking motion when large path error occurs. In previous work, the present authors presented a bang-bang path tracking algorithm by which smooth and stable tracking motion could be obtained even for the path given by simple combination of straight lines or circles and its effectiveness was proven via preliminary simulation studies. However, there still remained the problem that the design parameter called landing coefficient could not be optimally chosen and performance verification through real system application was not accomplished. In this study, we improve the algorithm which can determine the design parameters analytically and verify its performance by implementing the algorithm in an actual mobile robot control system designed using a personal computer. To investigate the performance of the control system, a series of path tracking experiments was conducted for a two-wheel driven robot developed in the laboratory.     

A novel 3D path following control framework for robots performing surface finishing tasks

We describe a novel 3D path following control framework for articulated robots in applications where constant speed travel along a path is desirable, such as robotic surface finishing tasks. Given the desired robot configuration sequence with a list of waypoints along a path, a trajectory optimization scheme based on direct collocation is proposed to determine the Cartesian path and the maximum constant translation speed that are dynamically feasible. Employing the Hermite–Simpson collocation method, a Cartesian path is developed that not only preserves the characteristics of the original motion sequence but also satisfies the physical requirements of the robot kinematics and dynamics. Since joint velocity control is quite common in many industrial robots, we consider a 3D kinematic control in the robot tool frame with control inputs as rate of change of orientation. The objective for the translation motion is to achieve constant speed along the path tangent direction, and that of the orientation control is to orient the robot properly based on the path provided by a converging path planner. We describe the optimization procedure employed with the direct collocation method to obtain the desired Cartesian path, an arc-length based re-parametrization of the desired path, and a path planner that provides a converging path to the desired path. To perform the surface finishing operation, we further present the joint space control law that is converted from the synthesis of the proposed path following and impedance force control in the tool frame. To verify and evaluate the performance of the proposed framework, we have conducted extensive experiments with a six degrees-of-freedom (DOF) industrial robot for several paths that can be employed for surface finishing of a variety of industrial parts.     

基于模糊算法的移动机器人路径规划

为了解决移动机器人最优路径规划问题,提出一种基于模糊算法的移动机器人路径规划策略.利用超声波传感器对环境进行探测,得到关于障碍物和目标的信息.运用模糊推理将障碍位置信息与目标位置信息模糊化,建立模糊规则并解模糊最终使机器人可以很好的避障,从而实现了移动机器人的路径规划.仿真实验结果表明了模糊算法优于势场法和A*算法,具...     

自适应蚁群算法的无人机航迹规划方法

为了提高无人机完成任务效率,在执行攻击任务前必需规划设计出高效的无人机飞行航路.提出了一种Q-学习的自适应蚁群算法的无人机航路规划方法,建立了基于真实地形数据和火力威胁区的威胁模型;针对传统蚁群算法在搜索过程中出现停滞现象,提出的Q-学习的自适应蚁群算法有效地解决了这一缺陷.并使用该算法对无人机的攻击任务航路进行了仿真计算,仿真结果表明该方法是一种有效的航路规划方法.     

基于改进蚁狮算法的无人机三维航迹规划

无人机3维航迹规划是任务规划中最复杂、重要的部分,针对基本蚁狮算法在解决3维航迹规划时能力不足的问题,首先在蚂蚁的行为中引入混沌调节因子,在蚁狮的行为中引入反调节因子,提高了算法的探索能力和开发能力;其次在建立3维环境模型的基础上,充分利用地形和约束信息,缩减搜索空间;最后将改进后的算法应用于3维航迹规划,并与原算法进行对比, 实现在线局部重规划。仿真实验结果验证了改进方法的可行性和优越性。     

基于稀疏A*算法的无人机航路规划方法

针对多约束条件下三维空间航路规划问题,分析了三维规划空间的划分方法,综合考虑航程代价、爬升代价和威胁代价等因素,针对航路规划任务对各种指标的偏重程度,引入指标的权重系数,设计了代价函数,并编制了稀疏A*算法流程,对算法的有效性进行了仿真验证.验证结果表明:采用稀疏A*算法能够有效地解决多约束条件下的三维空间航路规划问题.     

Anticollision techniques in multirobot environments

The use of multiple assembly robots operating in a shared workspace provides the promise of efficient and flexible assembly operations, but greatly increases the potential for catatrophic damage in the event of robot collisions. The paper describes some of the present techniques used for path planning and collision avoidance and discusses the problems involved in applying them to practical robot workcells. A new path-checking system is described which has been designed to operate in real time and to check for collisions between arbitrarily shaped robots and objects in the workspace, and which will speed path planning by acting as a high-speed validation utility     

分水岭分割算法的飞行器低空突防航路规划

针对军用飞行器路径规划中存在的规划空间维数爆炸问题,重点研究了一种简化初始路径规划空间的方法.首先将数字高程地图灰度图像化并叠加等效威胁,得到综合威胁地形灰度图像,在此基础上,引入控制标记符对其进行分水岭分割,从而得到初始路径规划空间的无向网络图.针对网络图中存在的很多不相关的分枝,采用了数学形态学的剪枝算法进行剪枝处理;针对网络节点之间可能存在的并行路径问题,以路径最短为原则裁剪较长的并行路径从而实现初始路径规划空间的优化.最后,运用A*算法进行航路寻优,并在飞行器性能约束条件下对所得航路进行拟合修正.仿真结果表明了该方法的可行性.     

图规划在路径规划中的应用

随着科学技术的不断发展,机器人研究成为当今的热门话题,人们趋向于用机器人替代人类去完成一些危险的工作。而路径规划是机器人研究的难点之一。本文以机器人的运输问题为例,介绍了图规划的算法,以及如何利用图规划技术对路径规划问题进行求解。     

Shortest path planning of a data mule in wireless sensor networks

Data mules are extensively used for data collection in wireless sensor networks (WSNs), which significantly reduces energy consumption at sensor nodes but increases the data delivery latency. In this paper, we focus on minimizing the length of the traveling path to reduce the data delivery latency. We first model the shortest path planning of a data mule as an optimization problem, and propose an optimal model and corresponding solving algorithm. The optimal model solution has high time complexity, mainly due to the parallel optimization of node visit arrangements and data access point (DAP) settings during the solution process, which is to obtain the shortest path result. In order to improve the computational efficiency, we next give the approximate model and its solving algorithm, which is mainly to decompose the path planning problem into the Traveling Salesman Problem (TSP) and nonlinear optimization problem, and optimize the two parts separately. The proposed approach is capable of expressing the influence of the communication range of each sensor node, which is suitable for more general application scenarios than the existing methods. Theoretical analysis and simulation results show that the solution has good performances in terms of path length and computational efforts.

     

基于改进遗传算法的UAV航迹规划

针对实际作战环境中的UAV航迹规划,提出一种基于改进遗传算法的UAV航迹规划方法;通过骨架化算法生成规划搜索空间,对规划搜索空间中的信息进行提取,求解出规划搜索空间中航迹点的杀伤概率;根据规划搜索空间中的信息,采用特殊的基因编码方式,使用遗传算法为UAV找到K条备选航迹,提高了航迹规划效率;根据设定的航迹选取原则,求出最优航迹并对其按不同步长进行平滑处理,最终得到满足UAV机动性要求的可飞航迹。     

Ant-based routing and QoS-effective data collection for mobile wireless sensor network

Mobility management in mobile wireless sensor networks (MWSNs) is a complex problem that must be taken into account. In MWSN, nodes move in and out of the network randomly. Hence, a path formed between two distant nodes is highly susceptible to changes due to unpredictable node movement. Also, due to the limited resources in WSN, the paths used for data transmission must be tested for the link quality and time consumed for data forwarding. In order to solve these issues, in this paper, an ant-based routing protocol with QoS-effective data collection mechanism is proposed. In this protocol, the link quality and link delay are estimated for each pair of nodes. Link quality is estimated in terms of packet reception rate, received signal strength indicator, and link quality index. A reliable path is chosen from the source to the destination based on the paths traversed by forward ants and backward ants. Then, if the link is found to be defective during data transmission, a link reinforcement technique is used to deliver the data packet at the destination successfully. The mobile robots collect the information with high data utility. In addition, each mobile robot is equipped with multiple antennas, and space division multiple access technique is then applied for effective data collection from multiple mobile robots. Simulation results show that the proposed routing protocol provides reliability by reducing the packet drop and end-to-end delay when compared to the existing protocols.