自主车的局部路径规划

局部路径规划是自主车的一项关键技术,它的品质 密切关系到整个自主车系统的性能．本文提出了将自主式多智能体的任务和反应性行为模型 嵌入到离散事件系统框架中作局部路径规划的方法,此方法克服了势场法（包括早期的虚力 场法）的缺陷——即由于把所有信息压缩为单个合力而损失部分有价值的局部障碍物分布信 息,从而提高了局部路径规划的可靠性．     

一种移动机器人路径规划方法的研究

该文主要是设计了一种可扩展式移动机器人,提出了基于Levenberg-Marquardt方法优化的EKF-SLAM算法、基于势场蚁群算法的移动机器人全局路径规划,并探索了一种新的最优路径搜索方法,即有机地将移动机器人局部路径规划融入全局路径规划中,并且通过机器人仿真实验完成室内移动机器人的自主导航,相比传统方法能够提高...     

自主移动机器人局部路径规划综述

自主移动机器人技术是近年来的研究热点,而路径规划技术是自主移动机器人技术研究中的一个重要内容。讨论了自主移动机器人路径规划技术的分类和研究局部路径规划的重要性;分析了局部路径规划技术的发展现状;指出了局部路径规划各种方法的优点与不足;对局部路径规划技术今后的发展方向做出了展望。     

煤矿搜救机器人最优路径规划算法

针对煤矿搜救机器人路径规划过程中因受障碍区间的干扰导致全局最优路径难以确定的问题,提出了基于梯度-坐标轮换法的煤矿搜救机器人最优路径规划算法。机器人首先按照梯度-坐标轮换法运动,然后根据已有的运动路径进行局部路径优化,并对所规划的局部最优路径进行可行性判断,直到路径中不存在障碍区间为止。仿真结果表明,该算法能够使煤矿搜救机器人在避开障碍的前提下,准确地规划出从出发点到目标点的全局最优路径,从而提高机器人运动路径规划的合理性和高效性。     

基于混合势场法的移动机器人路径规划

针对目前移动机器人在路径规划中出现的问题,提出一种自主移动机器人路径规划的新方法——混合势场法。分析了人工势场法的不足,找出局部极小值点的形成原因;针对人工势场法中障碍物附近目标不可达问题,采用了在斥力场函数中加入斥力因子,使得机器人顺利到达目标点;针对陷入局部极小值和振荡的问题,提出了混合势场法,通过将势场法和可视图法结合起来,使得机器人走出局部极小值和振荡区域。最后,将混合势场法应用于室内移动机器人的路径规划中,仿真实验证明了该方法的有效性。     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。     

基于ROS的无人配送车导航系统的设计与实现

针对无人配送车在自主导航过程中存在的寻路效率低、避障能力弱、转折幅度过大等问题,该文采用搭载机器人操作系统(ROS)的Turtlebot3机器人作为无人配送车,设计并实现了高效稳定的无人配送车自主导航系统。ROS是专门用于编写机器人软件的灵活框架,对其集成的SLAM算法进行改进,以完成无人配送车在封闭园区环境中的即时定位与地图构建,同时对ROS导航功能包集成的路径规划算法进行改进,使无人配送车在已知环境地图中规划生成出适合无人配送车工作的路径和有效避开障碍物。最后在Gazebo仿真环境中对无人配送车自主导航系统进行测试与验证。仿真试验结果表明,设计实现的无人配送车导航系统能够很好地满足无人配送车在封闭园区中的自主导航功能。     

越野环境下自主车辆导航地图自动创建方法研究*

针对越野环境下的地图创建问题,提出了一种自动创建自主车导航地图的方法。首先将车载摄像机获得的图像投影到车体坐标系,然后结合车辆行驶轨迹信息采用基于标记的分水岭算法判定可通行区域,最后融合局部俯视图信息生成全局一致地图,并在实时导航需求下对地图进行优化得到最终的导航地图。自主车实车实验结果表明,该方法生成的地图满足自主车实时导航需求,提高了路径规划效率。     

基于学习分类器的自主地面车在狭隘环境中的路径规划

提出了一种基于学习分类器(LCS)的避碰路径规划方法,设计了集成适应度函数,在确保安全避碰的前提下,解决自主地面车(ALV)在狭隘环境下的路径优化问题.不同环境的仿真实验结果表明,遗传算法和学习分类器结合用于自主地面车的路径规划是收敛的,提高了ALV在狭隘环境中快速发现安全路径的能力.     

基于势场的运动路径规划

本文阐述了基于势场的三轮自治车(AV)运动路径规划,以由障碍物和目标产生的虚拟势场力作为 AV运动的驱动力.先讨论了势场力的存在条件包括距离条件和方向条件并提出了计算方法,然后讨论了把 AV 简化成杆的运动路径规划,包括 AV 及杆的运动机理、加权势场合力的求法及其控制作用和运动定位等问题.最后给出了仿真结果.本文首次将势场法应用于三轮 AV 的路径规划.     

Collision avoidance by a modified least-mean-square-error classification scheme for indoor autonomous land vehicle navigation

In this article, a new collision-avoidance scheme is proposed for autonomous land vehicle (ALV) navigation in indoor corridors. The goal is to conduct indoor collisionfree navigation of a three-wheel ALV among static obstacles with no a priori position information as well as moving obstacles with unknown trajectories. Based on the predicted positions of obstacles, a local collision-free path is computed by the use of a modified version of the least-mean-square-error (LMSE) classifier in pattern recognition. Wall and obstacle boundaries are sampled as a set of 2D coordinates, which are then viewed as feature points. Different weights are assigned to different feature points according to the distances of the feature points to the ALV location to reflect the locality of path planning. The trajectory of each obstacle is predicted by a real-time LMSE estimation method. And the maneuvering board technique used for nautical navigation is employed to determine the speed of the ALV for each navigation cycle. Smooth collision-free paths found in the simulation results are presented to show the feasibility of the proposed approach.     

Target following with motion prediction for unmanned surface vehicle operating in cluttered environments

The capability of following a moving target in an environment with obstacles is required as a basic and necessary function for realizing an autonomous unmanned surface vehicle (USV). Many target following scenarios involve a follower and target vehicles that may have different maneuvering capabilities. Moreover, the follower vehicle may not have prior information about the intended motion of the target boat. This paper presents a trajectory planning and tracking approach for following a differentially constrained target vehicle operating in an obstacle field. The developed approach includes a novel algorithm for computing a desired pose and surge speed in the vicinity of the target boat, jointly defined as a motion goal, and tightly integrates it with trajectory planning and tracking components of the entire system. The trajectory planner generates a dynamically feasible, collision-free trajectory to allow the USV to safely reach the computed motion goal. Trajectory planning needs to be sufficiently fast and yet produce dynamically feasible and short trajectories due to the moving target. This required speeding up the planning by searching for trajectories through a hybrid, pose-position state space using a multi-resolution control action set. The search in the velocity space is decoupled from the search for a trajectory in the pose space. Therefore, the underlying trajectory tracking controller computes desired surge speed for each segment of the trajectory and ensures that the USV maintains it. We have carried out simulation as well as experimental studies to demonstrate the effectiveness of the developed approach.     

Towards a Trajectory Planning Concept: Augmenting Path Planning Methods by Considering Speed Limit Constraints

Trajectory planning is an essential part of systems controlling autonomous entities such as vehicles or robots. It requires not only finding spatial curves but also that dynamic properties of the vehicles (such as speed limits for certain maneuvers) must be followed. In this paper, we present an approach for augmenting existing path planning methods to support basic dynamic constraints, concretely speed limit constraints. We apply this approach to the well known A* and state-of-the-art Theta* and Lazy Theta* path planning algorithms. We use a concept of trajectory planning based on a modular architecture in which spatial and dynamic parts can be easily implemented. This concept allows dynamic aspects to be processed during planning. Existing systems based on a similar concept usually add dynamics (velocity) into spatial curves in a post-processing step which might be inappropriate when the curves do not follow the dynamics. Many existing trajectory planning approaches, especially in mobile robotics, encode dynamic aspects directly in the representation (e.g. in the form of regular lattices) which requires a precise knowledge of the environmental and dynamic properties of particular autonomous entities making designing and implementing such trajectory planning approaches quite difficult. The concept of trajectory planning we implemented might not be as precise but the modular architecture makes the design and implementation easier because we can use (modified) well known path planning methods and define models of dynamics of autonomous entities separately. This seems to be appropriate for simulations used in feasibility studies for some complex autonomous systems or in computer games etc. Our basic implementation of the augmented A*, Theta* and Lazy Theta* algorithms is also experimentally evaluated. We compare (i) the augmented and basic A*, Theta* and Lazy Theta* algorithms and (ii) optimizing of augmented Theta* and Lazy Theta* for distance (the trajectory length) and duration (time needed to move through the trajectory).     

A KNOWLEDGE-BASED NAVIGATION SCHEME FOR AUTONOMOUS LAND VEHICLES

A knowledge-based navigation system for autonomous land vehicles (ALVs) has been developed which can successfully negotiate an obstacle and threat-laden terrain, even if nothing is known beforehand about the terrain. The ALV stores new information in its memory as it travels, has the ability to backtrack out of unexpected dead ends, and performs spontaneous decision making in the field based on local sensor readings. The optimal global route of the ALV journey is obtained using dynamic programming, and decision making is accomplished via a production rule-based system. Execution examples demonstrate the power of the prototype system to solve navigation problems. This establishes the feasibility of constructing a valid ALV by combining search techniques with artificial intelligence tools such as production rule-based systems.     

An open‐source system for vision‐based micro‐aerial vehicle mapping,planning, and flight in cluttered environments

We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search‐and‐rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real‐world experiments.     

Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

Trajectory planning and trajectory tracking constitute two important functions of an autonomous overtaking system and a variety of strategies have been proposed in the literature for both functionalities. However, uncertainties in environment perception using the current generation of sensors has resulted in most proposed methods being applicable only during low-speed overtaking. In this paper, trajectory planning and trajectory tracking approaches for autonomous overtaking systems are reviewed. The trajectory planning techniques are compared based on aspects such as real-time implementation, computational requirements, and feasibility in real-world scenarios. This review shows that two important aspects of trajectory planning for high-speed overtaking are: (i) inclusion of vehicle dynamics and environmental constraints and (ii) accurate knowledge of the environment and surrounding obstacles. The review of trajectory tracking controllers for high-speed driving is based on different categories of control algorithms where their respective advantages and disadvantages are analysed. This study shows that while advanced control methods improve tracking performance, in most cases the results are valid only within well-regulated conditions. Therefore, existing autonomous overtaking solutions assume precise knowledge of surrounding environment which is not representative of real-world driving. The paper also discusses how in a connected driving environment, vehicles can access additional information that can expand their perception. Hence, the potential of cooperative information sharing for aiding autonomous high-speed overtaking manoeuvre is identified as a possible solution.     

Driving on Point Clouds: Motion Planning,Trajectory Optimization,and Terrain Assessment in Generic Nonplanar Environments

We present a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six‐dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot‐sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling‐based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint‐aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two‐level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.     

Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.