Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

On-line real-time path planning of mobile robots in dynamic uncertain environment

A new path planning method for mobile robots in globally unknown environment with moving obstacles is pre- sented. With an autoregressive (AR) model to predict the future positions of moving obstacles, and the predicted position taken as the next position of moving obstacles, a motion path in dynamic uncertain environment is planned by means of an on-line real-time path planning technique based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. The effectiveness, feasibility, high stability, perfect performance of obstacle avoidance, real-time and optimization capability are demonstrated by simulation examples.

葡萄园避障除草机的设计

基于新疆果园经济作物葡萄种植模式,针对葡萄行株间中耕除草劳动强度大、效率低等问题,设计了一种与小四轮拖拉机配套使用的葡萄园避障除草机。中耕铲刀可以对土壤进行浅层翻倒,对表层土壤起到疏松作用,同时可以将行间的杂草清除;两侧的避障铲刀可以将株间的杂草铲除,同时对株间表层土壤进行浅翻;螺旋镇压辊将浅翻后的大块土壤破碎,并将杂草掩埋,从而完成整个葡萄行株间中耕除草过程,达到增加土壤通气性、调节土壤水分状况及促使葡萄根系生长的目的。

农业机器人避障算法的研究

为了保证农业自主移动机器人能够在非结构的农田环境下灵活自主的行走,一个关键的技术就是能够准确地判断农田中障碍物的方位以及机器人与障碍物之间的相对位置,从而选择正确的路径。为此,提出了一种距离传感器结合图像边缘检测技术的避障算法,通过超声波测距传感器与PSD(位置敏感传感器)红外测距传感器对农田中的障碍物进行检测,利用CCD摄像头采集机器人所处农田环境中的图像信息并在Linux系统中利用OpenCV函数库进行图像处理,得到图像的边缘信息,从而判断出可行的路径。

融合改进A*算法和Morphin算法的移动机器人动态路径规划

在动态未知环境下对机器人进行路径规划,传统A*算法可能出现碰撞或者路径规划失败问题。为了满足移动机器人全局路径规划最优和实时避障的需求,提出一种改进A*算法与Morphin搜索树算法相结合的动态路径规划方法。首先通过改进A*算法减少路径规划过程中关键节点的选取,在规划出一条全局较优路径的同时对路径平滑处理。然后基于移动机器人传感器采集的局部信息,利用Morphin搜索树算法对全局路径进行动态的局部规划,确保更好的全局路径的基础上,实时避开障碍物行驶到目标点。MATLAB仿真实验结果表明,提出的动态路径规划方法在时间和路径上得到提升,在优化全局路径规划的基础上修正局部路径,实现动态避障提高机器人达到目标点的效率。

基于人工蜂鸟算法的机器人避障研究

针对传统避障算法搜索空间复杂、易陷入局部极值、避障效率低下等问题,提出了一种融合多种改进策略的人工蜂鸟算法。首先,利用Liebovitch映射生成初始候选解,从而提升人工蜂鸟种群的丰富性;其次,利用群体上个体间的差异变异对个体进行扰动,从而保留优质个体,引导搜索过程逼近全局最优解,避免过早收敛;然后,在引导觅食阶段引入黄金正弦因子,有利于缩小蜂鸟种群搜索范围,提高收敛精度和速度;最后,进行机器人避障仿真实验。实验结果表明,IAHA算法各项指标均优于其他算法,具有更好的优化性能,同时提高了机器人避障效率。

红外传感原理与应用简析

红外成像技术与红外避障技术是当今红外技术领域非常热门的两大应用。本文从红外开关的原理出发,深入探究红外技术的应用,以主动式红外开关与被动式传感器为主要部件,研究其原理和特性。由不同的传感器产生的主动成像系统与被动式成像系统进行研究,对于机器检测方面具有重要价值,不断提高检测精度。本文提到的避障原理及其应用,对于移动机器人、自动驾驶等当今时代热点研究方向具有一定的参考价值。

Robot Motion Planning in Flexible Manufacturing Systems

This paper presents the approaches to real-time motion planning for robot application inflexible manufacturing systems(FMS).They are based on an algorithm for fast mapping ob-stacles from a workspace(W-space)into a configuration space(C-space)by defining funda-mental obstacles in W-space.To plan a collision-free path in a multiple robot system,sweptvolumes produced during master robot motion are computed as additional moving obstacleswhen a collision-free path for a slave robot is generated.For motion planning with many de-grees of freedom,several types of reactive behavior are defined to coordinate motion conflictsbetween different links.

A dynamic fusion path planning algorithm for mobile robots incorporating improved IB-RRT∗and deep reinforcement learning

Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path planning algorithm incorporating improved IB-RRT∗and deep reinforce-ment learning(DRL)is proposed.Firstly,an improved IB-RRT∗algorithm is proposed for global path planning by combining double elliptic subset sampling and probabilistic central circle target bi-as.Then,to tackle the slow response to dynamic obstacles and inadequate obstacle avoidance of tra-ditional local path planning algorithms,deep reinforcement learning is utilized to predict the move-ment trend of dynamic obstacles,leading to a dynamic fusion path planning.Finally,the simulation and experiment results demonstrate that the proposed improved IB-RRT∗algorithm has higher con-vergence speed and search efficiency compared with traditional Bi-RRT∗,Informed-RRT∗,and IB-RRT∗algorithms.Furthermore,the proposed fusion algorithm can effectively perform real-time obsta-cle avoidance and navigation tasks for mobile robots in unstructured environments.