轮式移动机器人定点目标控制问题及线性分解方法

提出轮式移动机器人在给定时间内的定点目标控制问题并提出一种解决该类问题的线性分解控制方法。根据轮式移动机器人的非完整性非线性动力学模型的结构特性,将机器人的运动分解为原地旋转和直线运动。按不同时间段分别设计移动机器人的原地旋转和直线运动规律,从而实现了非线性系统的线性解耦分解。利用线性分解控制方法,将轮式移动机器人的原地旋转和直线运动按匀加速起步、匀速行驶和匀减速停车运动规律控制,实现了在给定时间内的定点目标控制。仿真结果验证了所提出方法的有效性。     

动态环境下矢量化路径规划算法

对全方位机器人在完全未知动态环境下实时路径规划和针对移动障碍物的避障问题进行研究。提出矢量化路径描述方法,并将其与Bug算法思想相结合来解决机器人的路径规划问题。机器人的初始路径由其初始位置和目标位置生成,其运动过程是:首先沿初始路径行进,以规定间隔扫描当前环境,判断是否有障碍物阻挡当前路径,并检测障碍物的位置、移动方向和速度等信息;然后根据障碍物信息和机器人安全距离计算路径中间点,并插入中间点更新路径以实现避障。本文的机器人路径规划结果是以矢量形式进行描述及保存的,降低了对路径存储空间的需求,且按规划结果行进时只需要考虑直线移动距离和转动方向,简化了全方位机器人的控制。仿真结果说明本文方法的可行性及有效性。     

移动机器人定点目标控制的线性分解方法

研究移动机器人的定点目标控制问题。针对移动机器人的非线性模型提出一种线性分解控制方法。根据移动机器人的结构特性,将移动机器人的运动控制过程从时间上分解为原地旋转控制和直线运动控制2个阶段,从而实现了非线性系统的线性解耦分解。根据线性分解模型,分别设计了原地旋转和直线运动的控制算法。理论分析表明,利用线性分解控制方法可以方便地实现移动机器人有限时间定点目标控制。     

基于蚁群算法深海采矿机器人工作路径规划

针对深海采矿机器人路径规划问题,提出一种适合于采矿作业的改进蚁群算法。该算法利用位图法建立环境模型,依据构型空间的思想将路径规划问题简化为质点运动问题;利用改进的蚁群算法对问题进行描述,蚁群搜索中采用邻居搜索原则和中线偏移策略。最后通过仿真实验表明,该算法精度高,在海底环境中,能够完成机器人采矿作业的要求。     

基于DIDSON的未知海域覆盖规划算法

针对自主水下机器人的路径规划问题,提出一种基于双频识别侧扫声呐(DIDSON)的全局路径规划算法。根据双频识别侧扫声呐的物理特性对AUV进行数学建模,根据声呐的工作频率不同,将AUV分为高频、低频两种工作模式。高频模式下成像精度高,低频模式下成像范围大。文中提出了一种D2-CPP算法,根据声呐返回的识别结果,算法会自主切换AUV的工作模式,并动态规划出对应的路径点,直到覆盖所有区域。通过与割草机算法的仿真对比,证明了算法的有效性,近海实验证明了算法的可靠性。     

多仿生机器人协同编队捕获策略

机器人的协同编队问题是一个综合性的研究课题,主要包括编队策略及路径规划 2 个阶段。针对单一机器人在未知水下环境中执行任务时能源受限等问题,提出一种基于改进蚁群算法的安全域协商捕捉策略,以解决多仿生机器人系统水下环境中对目标的协同编队捕获问题。机器人随机搜索过程中发现目标后, 利用安全域协商策略,实现目标机器人周围捕获点的分配;采用改进的蚁群算法实现编队过程的路径规划和自适应避障。在不同大小的障碍物环境中进行仿真实验,并与经典的路径规划算法进行对比,实验结果表明： 所提出的策略能够使机器人在复杂的水下障碍物环境中完成高效的协作编队捕捉任务,具备有效性。     

多障碍环境中基于增强式学习的势场优化和机器人路径规划

该文把增强式学习方法应用于多障碍环境中机器人路径规划 ,并将增强式学习和路径规划相结合 ,通过工作空间势场的自适应优化学习 ,实现机器人的全局路径规划 ,即得到从任何初始位置开始的最优路径。与传统的人工势场方法相比 ,该方法避免了势场中局部极小点所引起的陷阱区域 ,并且所得到的路径具有最优特性。计算机仿真实验结果表明 ,这种学习方法能有效的解决多障碍环境中的机器人路径规划问题     

无舵翼水下机器人路径跟踪控制研究

针对无舵翼水下机器人的各种不同任务要求下的路径跟踪控制进行研究。通过模拟人的运动行为,建立了虚拟避碰声纳模型。根据地形跟踪的方法提出基于虚拟声纳的路径跟踪控制方法,并通过考虑纵向速度对于其他各个自由度运动的影响设计了运动控制器。通过海上试验验证了所提出的路径跟踪控制方法对于无舵翼水下机器人是可以满足实际需要的。     

基于强化学习的自动帆船路径规划算法研究

作为自动水面航行器的重要分支之一,自动航行帆船在执行长期海事任务时具有低能耗的优势,但其航行过程受到环境因素的影响很大。针对以上情况,本文考虑了自动帆船的自身运动模型,以及在航行时受到的海风、海流和障碍物的影响,提出了自动帆船从起始点至目标点的路径规划算法。该算法通过帆船的平面运动模型来计算环境因素的影响,再通过强化学习中的Q-learning算法实现对于海上两点间的路径规划并同时实现规避障碍物。通过仿真实验证明了本文提出的自动航行帆船的路径规划算法是可行的。     

基于连续帧间差分的抛弃式探头三维下沉运动处理方法研究

抛弃式探头由无人机装载,能够在较远目标区域和危险海域开展海洋水文环境剖面参数的测量。通过安装不同传感器,可以实现对温度、盐度的剖面测量,其深度的测量采用数学方法计算得到。针对双摄像机水箱实验获得的5个不同攻角实验结果,分析了常用的运动目标的检测方法,最终选择基于连续帧间差分法,确定探头的三维坐标位置,进而得到探头下沉运动的三维运动轨迹和速度曲线等信息。探头从水面释放后攻角在下沉过程中不断调整,改变运动姿态,同时伴随自身的旋转,抵消水平方向阻力作用,初始攻角产生的深度测量误差主要体现在加速过程,探头达到匀速运动后测量误差不变,在不考虑横流的情况下,探头最后以匀速垂直下落运动。     

基于模糊PID的深海采矿机器人路径跟踪控制

深海采矿作业中,由于海底软泥稀软,采矿机器人极易打滑,以及海底地形、海流等干扰,采矿机器人容易偏离预定路径。针对采矿机器人的海底作业过程中路径跟踪问题,设计并分析了深海采矿机器人的路径跟踪控制系统。首先提出了艏向控制实现采矿机器人路径跟踪的控制算法,通过采矿机器人与当前目标点相对位置计算采矿机器人的目标艏向角,后基于运动学模型建立模糊比例积分微分(PID)的控制方法控制采矿机器人两侧转速差值进而控制采矿机器人艏向,从而使机器人按目标路径行走;同时为了防止输入过大引起打滑,基于动力学模型数值分析了采矿机器人主动轮角加速度与打滑率之间的关系,采取限制主动轮角加速度方式防止采矿机器人过度打滑;最后通过Matlab/Simulink建立系统模型对系统进行仿真分析。仿真结果表明,该控制算法能够良好地完成采矿机器人的路径跟踪任务。     

A technique for autonomous underwater vehicle route planning

If an underwater vehicle is to be completely autonomous, it must have the ability to plan paths around obstacles in order to operate safely. Many solutions to the problem of planning the path of a robot around obstacles have been proposed, but all are limited in some way. An algorithm using artificial potential fields to aid in path planning is presented. The planning consists of applying potential fields around obstacles and using these fields to select a safe path. The advantage of using potential fields is that they offer a relatively fast and effective way to solve for safe paths. A trial path is chosen and then modified under the influence of the potential field until an appropriate path is found. By considering the entire path, the problem of being trapped in a local minimum is greatly reduced, allowing the method to be used for global planning. The algorithm was tried with success on many different planning problems. The examples provided illustrate the algorithm's application to two- and three-dimensional planning problems     

Trajectory-cell based method for the unmanned surface vehicle motion planning

A trajectory-cell based method was proposed for unmanned surface vehicle (USV) motion planning to combine the expression of the dynamic constraints and the discretization of the search space. The dynamic constraints were expressed by the USV trajectories produced by the mathematical model. The search space was performed by the discretization rules with the consideration of the path continuity, the search convenience and the maneuvering simplification. Therefore, the trajectory-cells were the discretized trajectories, which made the search space meet the USV dynamic constraints, and guaranteed the final spliced path continuous. After abstracting the characteristics of those cells, the available waypoints and headings were represented as the search indexes. Finally, a trajectory-cell based path searching strategy was proposed by determining the cost function of the A* algorithm. The results showed that the proposed algorithm can plan a practical motion path for the USV.     

Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle

A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV.     

测船非匀速直线测量引起的多波束测深误差分析

多波束测深系统作业的基本前提是测船保持匀速直线运动状态,而实际作业中非匀速运动状态下的多波束测量普遍存在,此时常用的基于加速度测量原理的测姿设备会受到影响。为此,在多波束测姿误差分析的基础上,针对直线加速、U型转弯两种情况下的测姿误差进行研究,通过INS测姿与GNSS三天线测姿的数据比较,对非匀速直线运动状态下姿态误差的影响特点及程度进行了分析。实验证明当测船做直线加速运动时,会使纵摇角产生较大误差;当测船转弯时,会使横摇角产生较大误差,这对指导多波束实际测量具有一定的参考价值。     

Path Planning Method Based on D~* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades, path planning for unmanned surface vehicles(USVs) in complex environments, such as harbours and coastlines, has become an important concern. The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles. In this study,we developed a novel path planning method based on the D* lite algorithm for real-time path planning of USVs in complex environments. The proposed method has the following advantages:(1) the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2) a constrained artificial potential field method is employed to enhance the safety of the planned paths; and(3) the method is practical in terms of vehicle performance. The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms. The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.     

Smoothed A* algorithm for practical unmanned surface vehicle path planning

An effective path planning or route planning algorithm is essential for guiding unmanned surface vehicles (USVs) between way points or along a trajectory. The A* algorithm is one of the most efficient algorithms for calculating a safe route with the shortest distance cost. However, the route generated by the conventional A* algorithm is constrained by the resolution of the map and it may not be compatible with the non-holonomic constraint of the USV. In this paper an improved A* algorithm has been proposed and applied to the Springer USV. A new path smoothing process with three path smoothers has been developed to improve the performance of the generated route, reducing unnecessary ‘jags’, having no redundant waypoints and offering a more continuous route. Both simulation and experimental results show that the smoothed A* algorithm outperforms the conventional algorithm in both sparse and cluttered environments that have been uniformly rasterised. It has been demonstrated that the proposed improved A* route planning algorithm can be applied to the Springer USV providing promising results when tracking trajectories.