Mars Rover Autonomous Navigation

Autonomous navigation of a rover on Mars surface can improve very significantly the daily traverse, particularly when driving away from the lander, into unknown areas. The autonomous navigation process developed at CNES is based on stereo cameras perception, used to build a model of the environment and generate trajectories. Multiple perception merging with propagation of the locomotion and localization errors have been implemented. The algorithms developed for Mars exploration programs, the vision hardware, the validation tools, experimental platforms and results of evaluation are presented. Portability and the evaluation of computing resources for implementation on a Mars rover are also addressed. The results show that the implementation of autonomy requires only a very small amount of energy and computing time and that the rover capabilities are fully used, allowing a much longer daily traverse than what is enabled by purely ground-planned strategies.     

月球表面巡视探测自主局部避障规划

路径规划是月球表面巡视探测自主导航的重要功能,是提高地外天体表面探测效率和安全性的关键.国外已实现的地外天体表面自主路径规划方法以局部避障为主要目标,不考虑全局目标可达性和完备性,本文针对该问题,提出一种基于地形通过性定量评价和目标可达的综合自主局部避障规划方法,通过对稠密地形数据进行可通过性能的综合评价,并考虑与目标的方位和距离,规划出能够到达目标的避障安全路径.该方法已经成功应用于我国"玉兔号"和"玉兔二号"月球车的自主导航中.     

多传感器数据融合的智能电动车自主导航

以四轮驱动电动车为研究对象,将智能电动车上多种传感器所采集的信息进行处理与融合,实现校园环境里的自主行驶与导航.提出了在空旷路段和沿墙导航的控制决策,着重研究了沿墙算法和入弯算法,使智能电动车在不同的环境下可以有效地完成自主导航.实验结果表明:该导航控制策略和算法具有较高的可靠性.     

动态环境下改进人工势场法的仓储机器人自主导航系统研究

为了解决仓储机器人在全动态环境中的自主导航问题,在分析自主导航技术基础上建立了机器人和动态障碍物的数学模型,搭建了以二维激光雷达为主的环境感知平台,提出了一种改进的人工势场法。在传统人工势场法中同时引入相对速度和相对加速度因素得到改进的人工势场模型,实现机器人在全动态环境中的自主移动。设计了无障碍物和多动态障碍物两种移动环境。经仿真验证,应用改进的人工势场法进行路径规划能高效地避开动态障碍物、跟踪动态目标,且运动路径光滑。     

基于传感器信息的智能移动机器人导航评述

导航是研究智能移动机器人技术中的一个重要领域,对自主导航技术的关键问题——路径规划进行了评述。路径规划一般可分为基于模型的环境信息完全知道的全局路径规划和基于传感器的环境信息完全未知或部分未知的局部路径规划2种类型。分别对各种方法的发展现状进行了总结,指出了各种方法的优点和不足。     

自主微型直升机飞行路径规划研究

实现无人直升机在不确定3 维环境中的自主行为,路径规划是关键所在.考虑到直升机的实时要求,基于距离转换方法,提出一种改进的快速3维路径规划方法,并对避障策略做了一些探讨.仿真试验的结果验证了其有效性. 􀁽     

基于自主行为智能体的月球车运动规划与控制

研究基于自主行为智能体的月球车运动规划与控制方法．在基于自主行为智能体的月球车系统结构基础上,首先设计了月球车运动规划与控制的一组基本行为,对其原理进行证明．通过行为状态机对行为进行选择,如果不能保障月球车安全性能,则由运动规划智能体学习其行为参数,并由神经网络记忆．将月球车运动规划与控制分解为行为设计与学习两个过程,使月球车控制系统易于加入先验知识．同时,月球车运动规划能够满足其机动性与地形传送性约束,保证工程开发的结构化与可实现性．该方法不仅具有实时性,而且对未知环境具有较强的适应能力．仿真研究与实验证明了该方法的有效性．     

Deliberative On-Line Local Path Planning for Autonomous Mobile Robots

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.     

导航系统中多目标路径平滑化规划的研究

路径规划是车辆、机器人出行、无人机航路推荐和计算机游戏等许多应用中的关键任务。现有的大多路径规划常简化为单目标优化问题进行求解。但在现实生活中,还需要同时考虑多种规划目标,且用于规划路径的目标之间还存在着彼此不能变换的问题。在熟知的路径规划算法（D*Lite）上提出了一种新的多目标路径平滑化规划算法-平滑多目标D*Lite算法。通过构造一条初始多目标平滑路径,当检测到环境变化时采用增量搜索思想,仅更新受影响结点并从当前结点重新进行规划得到一条新的多目标平滑路径。仿真结果表明,该算法不但能有效躲避突发障碍物,规划路径拐点较少,还能提高搜索效率,可有效应用于具有不同非交互规划目标的导航系统。     

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Micro aerial vehicles (MAVs), especially quadrotors, have been widely used in field applications, such as disaster response, field surveillance, and search‐and‐rescue. For accomplishing such missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement. In this paper, we present a framework for online generating safe and dynamically feasible trajectories directly on the point cloud, which is the lowest‐level representation of range measurements and is applicable to different sensor types. We develop a quadrotor platform equipped with a three‐dimensional (3D) light detection and ranging (LiDAR) and an inertial measurement unit (IMU) for simultaneously estimating states of the vehicle and building point cloud maps of the environment. Based on the incrementally registered point clouds, we online generate and refine a flight corridor, which represents the free space that the trajectory of the quadrotor should lie in. We represent the trajectory as piecewise Bézier curves by using the Bernstein polynomial basis and formulate the trajectory generation problem as a convex program. By using Bézier curves, we can constrain the position and kinodynamics of the trajectory entirely within the flight corridor and given physical limits. The proposed approach is implemented to run onboard in real‐time and is integrated into an autonomous quadrotor platform. We demonstrate fully autonomous quadrotor flights in unknown, complex environments to validate the proposed method.     

Two-dimensional navigation problems: A knowledge-based system approach

Many researchers are studying ways to create machines that can make their own decisions and act on them. Recently, great advances have been made in intelligent mobile robot technology, advances which will provide autonomous traveling ability to autonomous systems, allowing them not only to surmount stairs but also other obstacles. The autonomous systems are expected to gather knowledge about their environment, construct a symbolic world model of the environment, and use this model in planning and carrying out tasks set them in high-level style. An approach to automatic path planning for self-navigation problems is presented. It is structured as a knowledge-based system and is a method of planning safe paths around circular obstacles in a two-dimensional plane for autonomous systems. The expert system path planner reduces the complexity of the problem and the computer run-time, enabling the agent to achieve a quicker response to its own environment. Also, computer run-time increases very slowly with problem complexity. It is done by: (1) representing the environmental information by sets of facts; (2) guiding the moving object by groups of rules and (3) deriving the result with simple algorithm and fewer calculations. This algorithm is implemented in the expert system environment, and some examples drawn from the system are also demonstrated.     

基于联邦UKF算法的移动机器人自主组合导航

组合导航技术是解决地面机器人自主导航的一个有效途径,其中GPS/DR是一种典型的组合方式。常用的卡尔曼滤波主要用于处理线性问题,针对该导航系统非线性的特点,对Unscented卡尔曼滤波（UKF）与分散式滤波技术相结合的方法进行了研究,建立了用于GPS/DR导航系统的联邦UKF算法。数值仿真实验表明,联邦UKF比联邦EKF有更好的滤波精度,同时有更高的稳定性和容错性,是一种理想的GPS/DR导航非线性滤波方法。     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Experiments and Mobility Components

In this paper, we describe a complete system for mission planning and execution for multiple robots in natural terrain. We report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.Two recently developed sensors are used for obstacle detection: a high-speed laser range finder, and a video-rate stereo system. An updated version of a dynamic path planner, D*, is used for on-line computation of routes. A new mission planning and execution-monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles.We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals.The work reported here includes a number of results not previously published, including the use of a real-time stereo machine and a high-performance laser range finder, and the use of the GRAMMPS planning system.     

深空探测转移段光学成像测量自主导航及仿真验证技术

本文研究了深空探测转移段光学成像测量自主导航涉及的导航天体的选取与规划、导航天体图像的处理、观测方程与状态方程的建立、导航滤波算法的选取以及数学与半物理仿真验证等技术,提出了导航目标最优选取与成像序列规划、复杂导航星图提取与识别、无迹变换导航滤波以及光学成像测量自主导航仿真验证等方法.数学和半物理仿真试验验证结果表明,提出的方法有效提高了自主导航精度.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

基于轨迹引导的移动机器人导航策略优化算法

针对在杂乱、障碍物密集的复杂环境下移动机器人使用深度强化学习进行自主导航所面临的探索困难,进而导致学习效率低下的问题,提出了一种基于轨迹引导的导航策略优化（TGNPO）算法。首先,使用模仿学习的方法为移动机器人训练一个能够同时提供专家示范行为与导航轨迹预测功能的专家策略,旨在全面指导深度强化学习训练;其次,将专家策略预测的导航轨迹与当前时刻移动机器人所感知的实时图像进行融合,并结合坐标注意力机制提取对移动机器人未来导航起引导作用的特征区域,提高导航模型的学习性能;最后,使用专家策略预测的导航轨迹对移动机器人的策略轨迹进行约束,降低导航过程中的无效探索和错误决策。通过在仿真和物理平台上部署所提算法,实验结果表明,相较于现有的先进方法,所提算法在导航的学习效率和轨迹平滑方面取得了显著的优势。这充分证明了该算法能够高效、安全地执行机器人导航任务。     

基于BDS的虾塘投饵船导航控制系统设计与试验

为降低虾类养殖人工使用量,实现饵料的均匀遍洒,设计了一种基于北斗定位系统(BDS)的虾塘投饵船导航控制系统.根据虾塘投喂需求和控制性能要求,对系统整体组成和双体式船体型线进行了设计,提升控制执行能力.该导航控制系统主要针对中大型虾塘,依托北斗自主导航定位技术,采用回转式方法对路径进行规划,提出了路径保持算法.现场试验表明投饵船在虾塘中能成功实现饵料在虾塘中的均匀遍洒,满足虾塘饲料投喂要求,可有效降低现有人工投饵工作量.系统设计中对性能和成本进行平衡,符合渔业养殖实际需求,有利于系统的推广应用.     

Design and field testing of a rover with an actively articulated suspension system in a Mars analog terrain

This study presents the electromechanical design, the control approach, and the results of a field test campaign with the hybrid wheeled‐leg rover SherpaTT. The rover ranges in the 150 kg class and features an actively articulated suspension system comprising four legs with actively driven and steered wheels at each leg’s end. Five active degrees of freedom are present in each of the legs, resulting in 20 active degrees of freedom for the complete locomotion system. The control approach is based on force measurements at each wheel mounting point and roll–pitch measurements of the rover’s main body, allowing active adaption to sloping terrain, active shifting of the center of gravity within the rover’s support polygon, active roll–pitch influencing, and body‐ground clearance control. Exteroceptive sensors such as camera or laser range finder are not required for ground adaption. A purely reactive approach is used, rendering a planning algorithm for stability control or force distribution unnecessary and thus simplifying the control efforts. The control approach was tested within a 4‐week field deployment in the desert of Utah. The results presented in this paper substantiate the feasibility of the chosen approach: The main power requirement for locomotion is from the drive system, active adaption only plays a minor role in power consumption. Active force distribution between the wheels is successful in different footprints and terrain types and is not influenced by controlling the body’s roll–pitch angle in parallel to the force control. Slope‐climbing capabilities of the system were successfully tested in slopes of up to 28° inclination, covered with loose soil and duricrust. The main contribution of this study is the experimental validation of the actively articulated suspension of SherpaTT in conjunction with a reactive control approach. Consequently, hardware and software design as well as experimentation are part of this study.