基于环境模型的移动机器人路径规划

介绍了一种新的移动机器人路径规划方法。采用链接图法,对工作空间建模。用Dijkstra算法决策出全局最短路径,然后用遗传算法对此路径进行优化,得到全局最优路径。最后提出了一种对路径几何改进的方法。仿真结果表明,该方法方便简单,对所规划的路径质量有所提高。     

LiAS: a reflexive navigation architecture for an intelligent mobilerobot system

In this paper, a complete hierarchical navigation architecture fur applications in real industrial factory environments is presented. An offline global geometrical planner uses a rough CAD-model of the working environment to plan consecutive via-points which the robot must follow. The navigation between the via-points is performed by a two-level online navigation algorithm. It consists of an online planning module combined with a low-level fuzzy logic avoidance behavior which enables the robot to move to the next goal by only specifying its coordinates in even completely a priori unknown and unstructured factory environments. The system includes a docking motion, based on a dynamic guidance technique. A perception fusion module combines information of three different sensors for accurate modeling of the world. The presented navigation method was tested with the mobile robot Leuven Intelligent Autonomous System (LiAS) and it proved to be useful in real world applications     

基于无线传感器网络的移动机器人导航方法

为移动机器人在无定位信息的无线传感器网络(WSN)中选择路程短、代价低的导航路径,提出了一种基于无线传感器网络的移动机器人导航方法,包括全网络导航路径规划和局部节点趋近算法。该方法通过结合各节点传感器数据,构造代价函数,在网络中建立伪梯度势场,为移动机器人规划最优路径;移动机器人通过探测接收信号强度指示(RSSI),逐一趋近该路径上的传感器节点到达目标节点。仿真结果表明,该方法能够根据移动机器人的导航要求,引导移动机器人迅速沿最优路径到达目标节点。     

机器人局部路径规划算法的研究与实现

通过采用扫描测距法解决了智能机器人系统的局部路径规划能力问题。给出了代价函数，并对函数中的各项加权系数进行了实验分析，具有一定的科学理论基础和重要的实用价值。     

Global minimum-jerk trajectory planning of robot manipulators

A new approach based on interval analysis is developed to find the global minimum-jerk (MJ) trajectory of a robot manipulator within a joint space scheme using cubic splines. MJ trajectories are desirable for their similarity to human joint movements and for their amenability to path tracking and to limit robot vibrations. This makes them attractive choices for robotic applications, in spite of the fact that the manipulator dynamics are not taken into account. Cubic splines are used in a framework that assures overall continuity of velocities and accelerations in the robot movement. The resulting MJ trajectory planning is shown to be a global constrained minimax optimization problem. This is solved by a newly devised algorithm based on interval analysis and proof of convergence with certainty to an arbitrarily good global solution is provided. The proposed planning method is applied to an example regarding a six-joint manipulator and comparisons with an alternative MJ planner are exposed     

Robot path planning using VLSI resistive grids

The resistive grid algorithm for mobile robot path planning is described. A major advantage of the method is that it is capable of a fine-grained parallel analogue VLSI implementation, which offers a fast, low-power solution to the problem of mobile robot navigation. The results from a small-scale test chip are presented, together with their implications for scaling up to a full-sized path-planning chip     

Onboard dynamic RGB-D simultaneous localization and mapping for mobile robot navigation

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.     

An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems

Dynamic power allocation (DPA) is the key technique to improve the system throughput by matching the offered capacity with that required among distributed beams in multibeam satellite systems. Existing power allocation studies tend to adopt the metaheuristic optimization algorithms such as the genetic algorithm. The achieved DPA cannot adapt to the dynamic environments due to the varying traffic demands and the channel conditions. To solve this problem, an online algorithm named deep reinforcement learning‐based dynamic power allocation (DRL‐DPA) algorithm is proposed in this paper. The key idea of the proposed DRL‐DPA lies in the online power allocation decision making other than the offline way of the traditional metaheuristic methods. Simulation results show that the proposed DRL‐DPA algorithm can improve the system performance in terms of system throughput and power consumption in multibeam satellite systems.     

基于双层蚁群算法和动态环境的机器人路径规划方法

针对动态环境未知时变的特点,提出一种机器人路径规划新方法.在该方法中,首先对栅格法建立的环境模型进行凸化处理,以避免机器人沿规划路径移动时陷入U型陷阱,从而加快路径规划的速度;其次,提出双层蚁群算法（DACO）,在每次迭代中先用外层蚁群算法寻找一条路径,然后以该路径为基础构造一个小环境,接着在该环境下用内层蚁群算法重新寻优,若寻得的路径质量更高,则更新路径并执行本文给出的一种新型信息素二次更新策略;最后,针对环境中不同动态障碍物的体积和速度,提出三种避障策略.动态环境下,机器人先由DACO算法规划一条静态环境下从起点到终点的全局最优路径,然后从当前起点开始,通过自带传感器获取动态环境信息,并根据需要执行等待、正碰或追尾避障策略,到达新的起点.仿真实验表明,该方法可以在动态环境下实时地为移动机器人规划出一条安全且最短的路径,是求解移动机器人路径规划问题的一种切实有效的方法.     

免疫算法在移动机器人路径规划中的应用

具体阐述了免疫算法在移动机器人路径规划中的应用,使机器人从给定点到目标点可以有效地躲避障碍物而且找到一条最短的路径;构建了机器人的数学模型和亲和力函数,并且说明了机器人的控制方式,给出了算法的具体实现步骤以及仿真实验。实验结果表明,免疫算法在应用到移动机器人路径规划时具有良好的性能。     

智能车辆最短路径算法的研究与实现

最短路径算法是智能车辆路径规划问题的核心内容。从道路网络拓扑结构的自动构建以及Dijkstra算法中快速搜索技术的实现入手,综合考虑核心算法和数据存储结构两个方面,提出了直线优化Dijkstra算法。该算法能够有效降低时间复杂性,提高系统的效率。     

基于LPA~*算法的无人机三维航迹快速规划研究

针对无人机的快速航迹规划展开研究.首先针对航迹规划的实际需要,将航迹规划划分为二维的航线规划和二维航线点之间的三维航迹规划;然后对二维威胁建模得到的赋权图,用Dijkatra算法产生初始的多任务二维航线;再结合无人机在飞行过程中的约束条件,提出动态权值的三维LPA*的航迹规划方法对二维航线产生的航线点之间进行规划,满足地形回避和威胁回避.仿真表明,上述方法可以有效快速地完成规划任务,同时当数字地图改变时,LPA*算法可以通过检测局部一致性来避免进行重新的全局搜索,大大缩减了搜索空间,从而可以快速地修正航迹.     

Solving a generalized constrained optimization problem with bothlogic AND and OR relationships by a mathematical transformation and itsapplication to robot motion planning

The logic relationship among the equality and inequality constraints in a standard constrained optimization problem (SCOP) is the logical AND. Various efficient, convergent and robust algorithms have been developed for such a SCOP. Motivated by a practical application, a more general constrained optimization problem (GCOP) with not only logic AND but also OR relationships is proposed in this paper. In order to solve such a generalized problem, a mathematical transformation which can transfer a set of inequalities with logic OR into one inequality is developed. This transformation provides a necessary and sufficient condition which enables us to use the algorithms developed for SCOPs to solve the generalized optimization problems. The research is motivated by the requirements of developing an efficient, robust, and reliable navigation algorithm for a mobile robot such as an autonomous underwater vehicle (AUV). The original contributions of the paper include threefold: first, from the viewpoint of optimization theory, this paper, to the authors' best knowledge, is the first one to propose such a GCOP. Second, a method is developed to solve such a GCOP. Third, from the viewpoint of robot path planning, this paper presents a new way of using classical optimization approach to solve robot path planning     

Managing the Dynamics of a Harmonic Potential Field-Guided Robot in a Cluttered Environment

This paper demonstrates the ability of the harmonic potential field (HPF) planning method to generate a well-behaved constrained path for a robot with second order dynamics in a cluttered environment. It is shown that HPF-based controllers may be developed for holonomic, as well as nonholonomic, robots to effectively suppress the effect of inertial forces on the robot's trajectory while maintaining all the attractive features of a purely kinematic HPF planner. The capabilities of the suggested navigation controller are demonstrated using simulation results for the holonomic and nonholonomic cases.      

Collision-free local planner for unknown subterranean navigation

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.     

一种轮式机器人轨迹跟踪控制算法的研究

针对轮式机器人轨迹跟踪控制问题，提出了一种轨迹跟踪控制算法。通过对轮式机器人进行运动学分析，建立了运动学模型来描述其在水平面内的运动规律；提出了轨迹跟踪控制算法，并对迹跟踪控制算法进行仿真试验，直线轨迹跟踪的纵向偏差 0.09m，横向偏差 0.06m， 航向角偏差 3.7°；曲线轨迹跟踪的纵向偏差 0.13m，横向偏差 0.09m，航向角偏差 5.1°。仿真试验结果表明本研究所设计的轨迹跟踪控制算法具有良好的动态响应，可以支撑实际应用。     

改进的A＊算法在机器人路径规划中的应用

文中提出了一种改进的A＊算法并应用于机器人路径规划中。采用基于A＊算法的二次路径规划策略,机器人行走时遇到突然出现的未知障碍物时能有效地进行路径重规划;采用基于优先级的子节点生成策略,考虑到了现实中机器人的体积,使规划路径能在现实中得到执行;最后,通过MATLAB仿真平台进行了仿真,验证了此算法的有效性和可靠性。改进的A＊算法提高了机器人的智能水平和实时路径规划能力。     

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.     

DWDM波长路由网络光链路负载均衡的波长路由算法

提出了一种应用于密集波分复用(DWDM)波长路由网络(WRON)中光链路负荷均衡的思想,并将其应用于优化Dijkstra算法的权值,同时将优化Dijkstra算法用于遗传算法求得了在不同的负荷条件下波长下限的网络所需波长数目。并将优化前后的算法分别对美国自然科学基金(NSF)网络的最优波长分配进行数值分析,发现基于负荷均衡思想的优化Dijkstra算法能够对网络的性能有很大提高：当遗传代数为20代时,采用优化Dijkstra算法阻塞率降低了约36％;当波长使用数为7个时,降低网络阻塞率10％。