The improved particle swarm optimization algorithm is applied to path planning problem in 3-D space.
将改进粒子群优化算法应用于三维空间路径规划。
To solve the robots path planning problem in dynamic environment, this paper applies adaptive learning to path planning based on reinforcement learning.
本文结合机器人路径规划问题介绍了增强式学习方法，实现了动态环境中基于增强式学习的自适应路径规划。
We construct a whole new algorithm for global path planning problem, and realized a lot of numerical experiments. It reveals the efficiency of this method.
我们构造了移动机器人全局路径规划的一种新算法，并进行了大量的数值实验，验证了方法的有效性。
In this thesis, path planning problem for mobile robots under dynamic environments is comprehensively studied and two methods are presented to deal with the problem.
本文提出了两种新的路径规划方法解决移动机器人在动态环境下的路径规划问题。
The previous control strategy that has been adopted to solve the path planning problem of biped robot can only handle the static or predictable obstacle avoiding problems.
以往的双足机器人路径规划控制策略只能解决静止或以可预测速度运动的障碍物的越障问题。
Global path planning problem for autonomous underwater vehicle (AUV) based on large-scale chart data is investigated by using ant colony optimization (in shorts, ACO) algorithm.
在大范围海洋环境中，应用蚁群算法对自主式水下潜器(AUV)的全局路径规划问题进行了研究。
According to the cutting path planning problem of the coal shearer, the paper provided the particle swarm triple spline optimization for the cutting path planning method of the coal shearer.
针对采煤机截割路径的规划问题，提出了粒子群三次样条优化的采煤机截割路径规划方法。
The route prediction for multiple unmanned aerial vehicles (UAVs) task allocation in dynamic battlefield is a complicated path planning problem characterized by multiple paths and real time demands.
战场环境中多无人机任务分配的航路预估是一个具有多路径和实时性双重要求的路径规划问题。
The route prediction for multiple unmanned aerial vehicles (UAVs) task allocation in dynamic battlefield is a complicated path planning problem characterized by multiple paths and real time demands.
战场环境中多无人机任务分配的航路预估是一个具有多路径和实时性双重要求的路径规划问题。
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