无人水面航行器全局路径规划方法研究

针对无人水面航行器(unmanned surface vehicle,USV)的路径规划问题,提出了基于粒子群优化算法(particle swarm optimization,PSO)和费马螺旋曲线(Fermat’s spiral,FS)的全局路径规划算法。首先,采用PSO全局路径规划算法,搜索航路点序列并将其顺序连接,以获得可行、最短且绝对安全的折线路径。其次,利用FS曲率可从零变化且连续的特点,设计FS光顺折线路径策略,使得所规划路径的方位和曲率均为连续函数,进而可应用于精确的路径跟踪等运动控制中。考虑到USV的机动性能限制,在FS过渡曲线设计中加入了最小回转半径约束。仿真结果表明,本文所提方法能够生成一条满足USV自身机动性能限制,且方位和曲率均为连续函数的可行光滑路径,从而能够使得USV实现真正意义上的完全自主航行。     

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.     

A task-based hierarchical control strategy for autonomous motion of an unmanned surface vehicle swarm

In this paper, a hierarchical control framework with relevant algorithms is proposed to achieve autonomous navigation for an underactuated unmanned surface vehicle (USV) swarm. In order to implement automatic target tracking, obstacle avoidance and avoid collisions between group members, the control framework is divided into three layers based on task assignments: flocking strategy design, motion planning and control input design. The flocking strategy design transmits some basic orders to swarm members. Motion planning applies the potential function method and then improves it; thus, the issue of autonomous control is transformed into one of designing the velocity vector. In the last layer, the control inputs (surge force and yaw moment) are designed using the sliding mode method, and the problem of underactuation is handled synchronously. The proposed closed-loop controller is shown to be semi-asymptotically stable by applying Lyapunov stability theory, and the effectiveness of the proposed methodology is demonstrated via numeric simulations of a homogeneous USV swarm.     

一种基于有限状态机模型的局部转向避碰路径规划算法

针对多礁石、渔船等障碍物的近海复杂环境下的一些应用,提出了一种基于有限状态机(finite-state machine,FSM)模型的无人船(unmanned surface vehicle,USV)局部转向避碰路径规划算法。首先,基于速度障碍法和障碍物区域分层方法,获取无人船固定航速条件下的航向角约束解析结果。然后,基于该约束条件及障碍物探测情况设计FSM的有限状态及执行动作和状态迁移条件,其中,通过转向控制实现向目标位点或缓冲位点进行导航的状态为FSM的2个重要状态。最终通过FSM的执行实现局部转向避碰路径规划。仿真结果表明提出的多障碍物避碰算法具有可行性和实用性。该方法易于改进和扩展,且容易与当前主流的无人船控制系统结合,有利于无人船避碰系统快速工程化的实现。     

改进的最短路径搜索A*算法的高效实现

在交通领域中,对最短路径搜索问题的效率和应用研究较多,尤其最短路径搜索算法的效率是人们普遍关注的重点。在A 算法的基础上,进一步改进其启发搜索策略,提出了改进的A 算法,极大地减小了算法搜索的规模,提高了算法的效率,实验结果显示了本文提出的搜索策略的合理性和有效性。