Meshing and Simplification of High Resolution Urban Surface Data for UAV Path Planning

This work presents an approach to utilize high resolution surface data as a-priori information for three dimensional path planning at very low altitude. The major challenge is preserve features while reducing the amount of data to a minimum. Non significant height points are eliminated by a neighbor search, performed within a data structure generated from pseudo 3D Delaunay meshing. A comparison to an alternatively implemented simplification shows which inherent building features can be preserved. For highlighting the feasibility of the approach, the processing results of real urban surface data from the inner city of Berlin is presented and used for sampling based path planning of an unmanned helicopter.     

The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

The paper presents a novel global path planning approach for mobile robot navigation in two dimensional workspace cluttered by polygonal obstacles. The core of the planning method introduced is based on the approximate cell decomposition method. The advantage of the new method is the employment of novel path refinement procedures of the paths produced by approximate cell decomposition that are based on local characteriscics of the workspace. Furthermore, the refined path is parametrically interpolated by cubic splines via a physical centripetal model, introducing the dynamic constraints of mobile robots' motion to the path construction. The method has been implemented both in a computer graphics simulation and on a real mobile robot cruising at indoor environments. Planned paths on several configurations are presented.     

A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning

This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.     

A decentralized probabilistic approach to articulated body tracking

We present a novel decentralized probabilistic approach to visual tracking of articulated objects. Analyzing articulated motion is challenging because (1) the high degrees of freedom potentially demands tremendous computation, and (2) the solution is confronted by the numerous local optima existed in a high dimensional parametric space. To ease these problems, we propose a decentralized approach that analyzes limbs locally and reinforces the spatial coherence among them at the same time. The computational model of the proposed approach is based on a dynamic Markov network, a generative model which characterizes the dynamics, the image observations of each individual limb, as well as the spatial coherence among them. Probabilistic mean field variational analysis provides an efficient computational diagram to obtain the approximate inference of the motion posteriors. We thus design the mean field Monte Carlo (MFMC) algorithm, where a set of low dimensional particle filters interact with one another and solve the high dimensional problem collaboratively. We also present a variational maximum a posteriori (MAP) algorithm, which has a rigorous theoretic foundation, to approach to the optimal MAP estimate of the articulated motion. Both algorithms achieve linear complexity w.r.t. the number of articulated subparts and have the potential of parallel computing. Experiments on human body tracking demonstrate the significance, effectiveness and efficiency of the proposed methods.     

Coverage path planning with targetted viewpoint sampling for robotic free-form surface inspection

Surface metrology systems are increasingly used for inspecting dimensional quality in manufacturing. The gauge of these measurement systems is often mounted as an end-effector on robotic systems to exploit the robots’ high degrees of freedom to reposition the gauge to different viewpoints. With this repositioning flexibility, a planning methodology becomes necessary in order to carefully plan the viewpoints, as well as the optimal sequence and quickest path to move the gauge to each viewpoint. This paper investigates coverage path planning for robotic single-sided dimensional inspection of free-form surfaces. Reviewing existing feasible state-of-the-art methodologies to solve this problem led to identifying an unexplored opportunity to improve the coverage path planning, specifically by replacing random viewpoint sampling strategy. This study reveals that a non-random targetted viewpoint sampling strategy significantly contributes to solution quality of the resulting planned coverage path. By deploying optimisation during the viewpoint sampling, an optimal set of admissible viewpoints can be obtained, which consequently significantly shortens the cycle-time for the inspection task. Results that evaluate the proposed viewpoint sampling strategy for two industrial sheet metal parts, as well as a comparison with the state-of-the-art are presented. The results show up to 23.8% reduction in cycle-time for the inspection task when using targetted viewpoints sampling.     

Hopfield net generation, encoding and classification of temporaltrajectories

Hopfield network transient dynamics have been exploited for resolving both path planning and temporal pattern classification. For these problems Lagrangian techniques and two well-known learning algorithms for recurrent networks have been used. For path planning, the Williams and Zisper's learning algorithm has been implemented and a set of temporal trajectories which join two points, pass through others, avoid obstacles and jointly form the shortest path possible are discovered and encoded in the weights of the net. The temporal pattern classification is based on an extension of the Pearlmutter's algorithm for the generation of temporal patterns which is obtained by means of variational methods. The algorithm is applied to a simple problem of recognizing five temporal trajectories with satisfactory robustness to distortions.     

Path planning approach in unknown environment

This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning: advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A* path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.     

Task-oriented optimal dimensional synthesis of robotic manipulators with limited mobility

In this article, an optimization method is proposed for the dimensional synthesis of robotic manipulators with limited mobility, i.e. with less than 6 degrees-of-freedom (“DoF”), with a prescribed set of tasks in a constrained environment. Since these manipulators cannot achieve full 6-DoF mobility, they are able to follow only certain paths with prescribed position and orientation in space. While the most common approach to this problem employs pure path-planning algorithms, operations in narrow and complex environments might require changes to the robot design too. For this reason, this paper presents an improved approach which aims to minimize position and orientation error with a dimensional synthesis. First, a novel methodology that combines a path planning algorithm and dimensional synthesis has been proposed in order to optimize both robot geometry and pose for a given set of points. Then, the method is validated with a 4-DoF robot for high-precision laser operations in aeroengines as a case study. The example shows that the proposed procedure provides a stable algorithm with a high convergence rate and a short time to solution for robots with limited mobility in highly constrained scenarios.     

采掘机器人的规划级控制技术研究

本文以反铲作业为对象,研究了采掘机器人的任务规划、路径规划和轨迹规划.在自行设 计研制的一台试验样机上实现了整个作业循环过程、预设路径和力监控挖掘过程的局部自主 式规划级控制,并开发了一个三维动态仿真软件作为检验规划级控制理论的辅助手段.     

A direct variational method for planning monotonically optimal paths for redundant manipulators in constrained workspaces

This paper proposes a path planner for serial manipulators with a large number of degrees of freedom, working in cluttered workspaces. Based on the variational principles, this approach involves formulating the path planning problem as constrained minimization of a functional representing the total joint movement over the complete path. We use modified boundary conditions at both ends of the trajectory to find more suitable start and end configurations. The concept of monotonic optimality is introduced in order to optimize the manipulator paths between the resulting end configurations. For obstacle avoidance, volume and proximity based penalizing schemes are developed and used. The presented planner uses a global approach to search for feasible paths and at the same time involves no pre-processing task. A variety of test cases have been presented to establish the efficacy of the presented scheme in providing good quality paths. The extent of advantage accruing out of the measures of free end-configurations and monotonic optimality are also analyzed quantitatively.     

基于改进Dijkstra算法的机器人路径规划方法

本文提出一种利用栅格法和改进的Dijkstra算法进行机器人路径规划的方法。该方法利用栅格法对机器人的工作环境进行表示,利用改进的Dijkstra算法进行最短路径的搜索。应用该方法在对环境细化到包含10000个栅格节点的情况下，在主频1．7GHZ的计算机上规划路径的时间最长不超过0．3秒。实践证明该方法具有实时性和路径最优性。     

The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces

Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been rested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.     

基于改进Dijkstra算法的机器人路径规划方法

本文提出一种利用栅格法和改进的Dijkstra算法进行机器人路径规划的方法。该方法利用栅格法对机器人的工作环境进行表示,利用改进的Dijkstra算法进行最短路径的搜索。应用该方法在对环境细化到包含10000个栅格节点的情况下,在主频1.7GHZ的计算机上规划路径的时间最长不超过0.3秒。实践证明该方法具有实时性和路径最优性。     

On‐line estimation and path planning for multiple vehicles in an uncertain environment

A unified approach to cooperative target tracking and path planning for multiple vehicles is presented. All vehicles, friendly and adversarial, are assumed to be aircraft. Unlike the typical target tracking problem that uses the linear state and nonlinear output dynamics, a set of aircraft nonlinear dynamics is used in this work. Target state information is estimated in order to integrate into a path planning framework. The objective is to fly from a start point to a goal in a highly dynamic, uncertain environment with multiple friendly and adversarial vehicles, without collision. The estimation architecture proposed is consistent with most path planning methods. Here, the path planning approach is based on evolutionary computation technique which is then combined with a nonlinear extended set membership filter in order to demonstrate a unified approach. A cooperative estimation approach among friendly vehicles is shown to improve speed and routing of the path. Copyright © 2004 John Wiley & Sons, Ltd.     

带拖车的移动机器人包络路径分析与描述

带拖车的移动机器人系统是一类复杂的多车体系统,其路径规划问题难以沿用单 体移动机器人系统的规划方法．本文通过分析这类系统运动时的路径特征,提出包络路径概 念;并根据路径与车体约束的关系,给出包络路径的量化描述,从而把这类多车体系统的路 径规划问题转化为单个车体的规划问题,本质地改变了带拖车移动机器人的运动规划．     

基于概率分析进化算法的飞行航迹规划

在分析传统进化算法解决飞行航迹规划问题缺点的基础上,提出了一种高阶概率分析进化算法-多变量贝叶斯优化算法,用于解决飞行器航迹规划问题。其主要特点是用多变量染色体构造贝叶斯网络,通过设计一个多变量K2评价方法评价得到的贝叶斯网络的优劣。仿真结果验证了算法的有效性。     

A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation

An important concept proposed in the early stage of robot path planning field is the shrinking of a robot to a point and meanwhile the expanding of obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision-free path for a point robot among the Cspace obstacles. However, the research experiences have shown that the Cspace transform is very hard when the following situations occur: 1) both the robot and obstacles are not polygons, and 2) the robot is allowed to rotate. This situation gets even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. For this reason, direct path planning approaches without the Cspace transformation is quite useful and expected.Motivated by the practical requirements of robot path planning, a generalized constrained optimization problem (GCOP) with not only logic AND but also logic OR relationships was proposed and a mathematical solution developed previously. This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work, converts the obstacle avoidance problem into a semi-infinite constrained optimization problem with the help of the mathematical transformation, and proposes a direct path planning approach without Cspace calculation, which is quite different from traditional methods. To show its merits, simulation results in 3D space have been presented.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

移动式起重机吊装路径规划仿真平台设计

针对移动式起重机吊装路径规划的过程、结果难以直观显示,算法性能测评困难等问题,设计并实现了一款面向移动式起重机吊装路径规划的仿真平台。给出平台的系统框架,具体介绍路径规划、可视化的设计,用一个实例验证其可用性和有效性。实例表明,平台可实时显示搜索树(图)的生长过程、规划所得的路径及其吊装过程,同时可进行算法性能的自动测试,有助于吊装路径规划算法的研究。     

Optimal Camera Trajectory under Visibility Constraint in Visual Servoing: A Variational Approach

The principal deficiency of an image-based servo is that the induced three-dimensional (3-D) trajectories are not optimal and sometimes, especially when the displacements to realize are large, these trajectories are not physically valid, leading to the failure of the servoing process. In this paper, we adress the problem of generating trajectories of some image features that correspond to optimal 3-D trajectories in order to control efficiently a robotic system using an image-based control strategy. First, a collineation path between given the start and end points is obtained, and then the trajectories of the image features are derived. Path planning is formulated as a variational problem that allows us to consider simultaneously optimality and inequality constraints (visibility). A numerical method is employed for solving the path planning problem in the variational form.