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.

An Evolutionary Real-Time 3D Route Planner for Aircraft

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅰ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Car-tesian space mainly through increasing the number of knots on the path and the number of the path′s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot′s hand and does not change the number of the path′s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the segments of the path. But, this method resulted in the heavier on line computational burden for the robot controller. In this paper, aiming at this drawback, the authors propose a new kind of real time accurate hand path tracking and joint trajectory planning method for robots. Through selecting some extra knots on the specified hand path by a certain rule, which enables the number of knots on each segment to increase from two to four, and through introducing a sinusoidal function and a cosinoidal function to the joint displacement equation of each segment, this method can raise the path tracking accuracy of robot′s hand greatly but does not increase the computational burden of robot controller markedly.

裁剪优化的Anytime算法

解决路径规划问题时,传统的Anytime算法有时候会遇到花费过多存储空间和计算时间的问题。该文提出的裁剪优化的Anytime算法旨在提高这两方面的性能。该算法在已有的Anytime算法的基础上,通过裁剪非均衡表中的节点来降低存储空间,然后通过分析裁剪后的节点信息来判断是否进入下次循环,以此减少计算时间。最后通过仿真试验,验证了算法的可行性和有效性。

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.

双延迟深度确定性策略的AUV路径规划控制

为了在复杂未知环境中提升水下机器人的实时控制精度,提出一种基于双延迟深度确定性策略的水下机器人路径规划控制方法。在没有环境先验知识的情况下,通过在线测量使用双延迟深度确定性策略算法计算水下无人机的连续控制输入。考虑洋流随机高斯模型,引入一种综合奖励函数,并且提出一种基于阶跃强化学习的六自由度动力学水下无人机自适应运动规划和避障技术。仿真结果表明提出方法能够有效实现位置环境下的高精度实时控制,并且具有一定的泛化能力。

客户分级优先的即时配送路径规划方法

为了使即时配送企业能以较低的成本提高配送准时性,从而维护并发展高价值客户,首先,针对即时配送客户的特点改进RFM模型,基于已有数据使用DBSCAN算法进行客户聚类,根据聚类结果使用GBDT算法构建客户分级预测模型对即时配送客户进行分级预测。在此基础上,以即时配送的固定成本、变动成本及客户超时点种类、数量为优化目标,构建基于客户分级优先的即时配送路径优化模型,再设计遗传算法对该模型进行求解。最后,以沈阳市某一站式冷链即时配送企业为对象进行实例分析。结果显示,相比该企业原配送方案,应用客户分级优先的即时配送路径规划方法规划后的方案在配送总成本仅提高4.8%的情况下,高价值、潜在高价值客户超时点数量由6减少为2,且超时点均为边缘客户,同时配送总时间减少了7.3%,验证了该方法的有效性。采用该配送路径规划方法,企业的配送成本虽然会小幅增加,但因配送准时性提升,可以更好地维护高价值客户,同时发展潜在高价值客户向高价值客户转变,进而保持或提高长期收益。

一种融合改进A^(*)算法与改进动态窗口法的文旅服务机器人路径规划

为满足复杂环境下文旅服务机器人路径规划算法搜索的导向性、静态环境下全局路径的最优性和动态环境下实时避障的安全性的需要,提出了一种基于改进A^(*)算法与动态窗口法相融合的算法。首先,在传统A^(*)算法的基础上,采用更精确的搜索邻域选取策略,并引入障碍物占用栅格率来量化地图信息,动态调节启发函数和权重系数;其次,引入安全距离概念,提出一种三次折线优化方法,剔除冗余节点和拐点,以提高路径的平滑性;针对狭窄通道环境,提出一种自适应圆弧优化方法,使路径更符合机器人的运动学约束。通过加入动态障碍物垂直距离代价函数,有效减少机器人与动态障碍物的冲突和碰撞风险;最后,将改进A^(*)算法与动态窗口法相融合,选取关键路径点作为动态窗口法的临时目标点,分段使用动态窗口法进行局部实时路径修正。实验结果表明,该融合算法同时具备搜索导向性、全局路径最优性和动态避障能力,能够安全快速到达目标点,具有一定的应用价值。

基于深度学习的机器人局部路径规划方法

为了将视觉信息融入到机器人导航过程中,提高机器人对各类障碍物的识别率,减少危险事件的发生,设计了基于二维CNN及LSTM的局部路径规划网络。提出了基于深度学习的局部路径规划方案。利用机器人视觉信息及全局路径信息推理产生机器人在当前时刻完成避障导航任务所需转向角度;搭建了用于对规划器核心神经网络进行训练和验证的室内场景;提出了以路径总长度、平均曲率变化率及机器人与障碍物之间的距离为性能指标的路径评估方案。实验表明:该方案在仿真环境及真实场景中均体现了较优秀的局部路径生成能力。

基于全局优化的列车自动驾驶实时规划算法

[目的]针对现有城市轨道交通列车自动驾驶中目标速度规划算法运算量大、运算时间长而无法实时应对突发状况时线路限速临时改变的问题,提出一种基于全局优化的目标速度实时规划算法,实时生成规划速度曲线。[方法]依据列车当前位置、当前速度、运行前方线路限速等条件,首先通过逐点计算的方式生成最短用时规划速度曲线;接着调整牵引、制动级位使列车保持匀加速/减速运行,优化列车运行的舒适度指标;并结合调整最高限速段的巡航速度减少不必要的牵引和制动时间,优化列车运行的能耗指标,最后输出列车规划速度曲线。[结果及结论]仿真结果表明,采用该算法实时生成的规划速度曲线满足了安全、准时和精准停车等基本约束条件,相比传统规划算法,提高了列车的运行舒适度,降低了列车运行能耗;同时,也可以有效应对突发状况时线路限速临时改变的情况,实现多个运行指标的优化。

面向实时环境的车机协同包裹配送路径规划

针对借助中转点进行跨区域包裹配送的路径规划问题,现有规划方法大多忽略了中转点处实时环境变化对配送效率的影响,本文提出了一种面向中转点实时环境的车机协同配送路径规划方法。该方法考虑了中转点之间的车辆和无人机的流量与简单环境参数,并将这些参数结合各自到中转点的路段距离来分别计算车辆与无人机的配送成本。考虑到无人机能量与配送截止时间等约束,该方法建立了以最小化配送成本为目标的数学模型,并在两阶段求解,在第一阶段使用遗传算法求解包裹由车辆配送的路径,在第二阶段搜索出包裹由无人机配送的路径。仿真实验表明,所提出的方法能够有效地降低包裹配送成本,提高配送成功率。

车间物流车实时配送方案规划研究

针对生产过程中人工参与度高、岗位加工工时波动较大和产线拥堵情况较多的生产车间,在物料消耗无规律的情况下,传统基于经验的物料配送方案难以快速、精准地补给缺料岗位。为提高上述车间的物流配送效率,提出一种车间物流车实时配送方案规划方法。基于车间数字孪生监测系统获取到的实时数据,为每个生产岗位划分岗位配送优先级,并基于优先级得出备选配送序列,再使用A*算法搜索最短的配送路径。通过实例分析可知,相比传统配送方案,所提方案规划时间更短、配送成本更低,验证了所提方法的可行性和优越性。

计及交通事故影响的电动汽车路径规划和充电导航策略

针对交通事故对电动汽车用户驾驶和充电体验的影响尚未得到充分研究这一现状,提出了一种弱化交通事故对用户不良影响的路径规划和充电导航策略。首先,以电动汽车用户重点关注的行驶能耗和行驶时间两项指标为导向,建立了基于实时交通信息的交通网综合道路阻抗模型,实现了交通流拥塞水平的动态表征。然后,考虑交通流量和道路拓扑的耦合作用,建立了基于交通事故“发生-持续-消散”动态过程的后果评估模型,实现了交通事故后果的精准量化。最后,建立了以提升电动汽车用户体验为目标的路径规划和充电导航优化模型,提出了基于Dijkstra算法的滚动优化算法,实现了模型的快速求解。以配电网与交通网组成的耦合系统进行算例分析,结果表明,所提方法能够有效减少交通事故发生后的电动汽车用户综合出行成本,并缓解交通网拥塞。

融合A^(*)与DWA算法的水面船艇动态路径规划

为解决水面船艇路径规划同时要求全局最优、实时避障和航迹安全可靠的问题,提出了一种基于融合A^(*)算法与动态窗口算法(DWA)的水面船艇路径规划方法。首先通过引入启发函数动态加权策略,提高A^(*)算法的搜索效率;然后综合考虑水面船艇的运动特性,采用一种路径转角节点角度削弱策略,减少转角,缩短全局路径长度;最后,基于全局因素影响与航迹安全约束对DWA算法的轨迹评价函数进行改进,并以全局路径提供子目标点引导DWA算法进行局部规划的方式完成算法融合。实验结果表明,融合算法相比于现有算法的总转向角度分别减少了45.6%、46.0%,验证了融合算法的有效性与可行性,并且相较于其他传统算法更具优越性。

融合改进Dijkstra算法和动态窗口法的移动机器人路径规划

为解决移动机器人在智能制造车间的全局路径规划和局部动态避障问题,提出一种融合改进的Dijkstra算法和改进的DWA算法,对传统Dijkstra算法的路径进行平滑优化,使得路径轨迹更加平滑,动态改变DWA算法中速度评价权重函数,提高避障效率。仿真结果表明,改进Dijkstra算法路径平滑优化后,平均路程缩短比例为0.65%,平均偏航角震荡次数减少了67.70%,改进后的DWA算法运行路程缩小9.68%,路径转折次数降低了33%,运行时间缩短3.88%。基于改进的Dijkstra算法和改进的DWA算法提出一种融合算法,仿真和样机实验结果表明:面对静态、动态障碍物,机器人运行线速度平缓,轨迹光滑,角速度波动明显,证明机器人运动稳定,实时调整方位,具有良好的避障能力。并且多次机器人循环定点实验中机器人纵向(X轴方向)平均误差≤30 mm,横向(Y轴)平均误差≤30 mm,定位精度满足工业需求。

面向智能敏捷卫星的自主任务规划与调度

针对卫星机动能力提升和场景需求更加复杂的实际,设计了一种面向智能敏捷卫星的启发式调度方法。基于智能敏捷卫星系统,给出了调度问题的模型化描述;在此基础上,给出了滚动调度的自主任务规划调度框架,并基于系统延时指令和实时指令两个工作模式,在调度时机决策上分别采用混合触发机制和动态触发机制,并在调度算法上分别采用基于规则和优先级的启发式算法。另外,为了验证模型与算法在实际星上环境的表现,在板卡硬件上完成调度系统的设计与实现,并进行仿真实验,用以测试硬件系统的调度效果和延迟时间。

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.

基于改进T-S模糊模型的弧焊机器人轨迹实时跟踪

针对弧焊机器人在实际作业中受到诸多不确定因素的影响,导致弧焊机器人焊接轨迹偏离预期轨迹的问题,提出一种基于改进T-S(Takagi-Sugeno)模糊模型的弧焊机器人轨迹实时跟踪方法。采用图像识别技术识别并定位焊缝,通过坐标转换,确定焊缝中心线在机器人坐标系中的位置坐标。利用蚁群算法根据焊缝中心线在机器人坐标系中的坐标,搜索从起始位置到目标位置的最优焊接路径。设计了一种改进区间二型T-S模糊模型,通过视觉传感器实时获取新的焊缝信息,计算轨迹误差,以此为输入,利用改进T-S模糊模型对机器人的运动轨迹进行实时调整。实验结果表明,采用改进T-S模糊模型后,机器人轨迹跟踪离散度均降至1 mm以下,弧焊机器人轨迹与预期轨迹高度重合,比传统方法跟踪离散度大大降低。

智能电网中的电力系统规划与优化分析

阐述智能电网技术在发电系统、智能固态表针、电力调度、高速实时通信中的应用。探讨基于智能电网的电力系统优化策略,包括传输网优化、光缆优化,提升电力系统科学化与智能化规划。