Nearly time-optimal paths for a ground vehicle

It is well known that the sufficient family of time-optimal paths for both Dubins' as well as Reeds-Shepp' s car models consist of the concatenation of circular arcs with maximum curvature and straight line segments, all tangentially connected. These time-optimal solutions suffer from some drawbacks. Their discontinuous curvature profile, together with the wear and impairment on the control equipment that the bang-bang solutions induce, calls for ' smoother' and more supple reference paths to follow. Avoiding the bang-bang solutions also raises the robustness with respect to any possible uncertainties. In this paper, our main tool for generating these “nearly time-optimal” , but nevertheless continuous-curvature paths, is to use the Pontryagin Maximum Principle (PMP) and make an appropriate and cunning choice of the Lagrangian function. Despite some rewarding simulation results, this concept turns out to be numerically divergent at some instances. Upon a more careful investigation, it can be concluded that the problem at hand is nearly singular. This is seen by applying the PMP to Dubins car and studying the corresponding two point boundary value problem, which turn out to be singular. Realizing this, one is able to contradict the widespread belief that all the information about the motion of a mobile platform lies in the initial values of the auxiliary variables associated with the PMP. Keywords Time-optimal paths - Motion planning - Optimal control - Pontryagin maximum principle - UGV

基于队形控制的多机器人时间最优搬运方法(英文)

从多机器人队形控制的角度出发,并以组群机器人系统结构为基础,分析设计了一类多机器人协作搬运方法——时间最优搬运方法,主要解决了在被搬运物体形状未知的情况下如何寻找最优搬运点以及各机器人时间最短到达最优搬运点的路径规划问题,并证明了稳定时间最优路径规划算法(STOPP)满足队形变换时间最优且保证队形转换过程中系统内部避碰。

基于粒子群免疫优化算法的足球机器人路径规划

为提高PSO算法的性能,引入免疫算法中浓度调节机制的"抗体浓度选择"策略形成粒子群免疫算法,利用该算法对足球机器人进行路径规划,使足球机器人从给定点到目标点可以有效地躲避障碍物并且得到一条最短路径。实验结果表明,该算法具有高效的全局搜索能力和良好的收敛稳定性。

基于姿态的轨道焊接机器人智能越障控制方法

为了机器人能更好地完成轨道焊接任务,减少障碍物对工作的影响,提出了基于姿态的轨道焊接机器人智能越障控制方法。分析越障时机器人姿态稳定性,判定机身质心位置,明确焊接臂角度和碰触点,估算出姿态支持点位置,同时将实时运动数据传输到受力锥模型中,计算最小偏移势能推测越障倾翻方向,得出最佳稳定锥。初始化蚁群遗传算法、设定主程序,迭代出全局越障路径;考虑到户外工作环境存在路径规划阻力,结合卡尔曼滤波的状态转移矩阵,减少误差完成智能越障控制。仿真实验表明,所提方法在恶劣路况下也能实现精准越障,且可以智能调节姿态,使机器人恢复稳定。

基于稳定风场下的无人机电力巡检路径规划研究

为研究多旋翼无人机在稳定风场下电力巡检遇障时的路径规划问题,本文构建稳定风场和多个静态障碍区双重影响的无人机电力巡检路径规划模型,运用强化学习的思想改进遗传算法更新种群,提高种群的收敛速度,实现在短时间内规划出巡检时间代价最小的路径。仿真结果表明:在稳定风场下进行电力巡检任务,多旋翼无人机会优先选择顺风方向巡检临近的杆塔点,减少逆风巡检的路径,从而规划出完成任务的最小巡检时间代价的路径。除顺风方向外,风速越大,无人机矢量合成的地速越小,无人机完成巡检任务所需的时间代价越大。无人机在不同障碍区下避障巡检时,障碍区的半径大小会影响无人机绕开障碍区的概率。