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.     

CAD For Intelligent Activity of an Autonomous Submersible System

This paper describes a CAD facility for the design of automatic guidance and control strategies for an unmanned autonomous submersible. The facility utilises a three dimensional colour graphics display of the scene and vehicle trajectory, providing a man/ machine interface for the operator in addition to vehicle motion simulation with six degrees of freedom. A primary application envisaged for the CAD facility is in the design of scene analysis algorithms and the intelligent control system structure, for an autonomous submersible employing scanning sonar as its target sensor.     

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.      

In‐water visual ship hull inspection using a hover‐capable underwater vehicle with stereo vision

Underwater visual inspection is an important task for checking the structural integrity and biofouling of the ship hull surface to improve the operational safety and efficiency of ships and floating vessels. This paper describes the development of an autonomous in‐water visual inspection system and its application to visual hull inspection of a full‐scale ship. The developed system includes a hardware vehicle platform and software algorithms for autonomous operation of the vehicle. The algorithms for vehicle autonomy consist of the guidance, navigation, and control algorithms for real‐time and onboard operation of the vehicle around the hull surface. The environmental perception of the developed system is mainly based on optical camera images, and various computer vision and optimization algorithms are used for vision‐based navigation and visual mapping. In particular, a stereo camera is installed on the underwater vehicle to estimate instantaneous surface normal vectors, which enables high‐precision navigation and robust visual mapping, not only on flat areas but also over moderately curved hull surface areas. The development process of the vehicle platform and the implemented algorithms are described. The results of the field experiment with a full‐scale ship in a real sea environment are presented to demonstrate the feasibility and practical performance of the developed system.     

Experimental tests of autonomous ground vehicles with preview

This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the model predictive control(MPC)algorithm, where the high-level performs on-line optimization to generate the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.     

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPA's Unmanned Ground Vehicle program     

城市交叉口车辆速度与交通信号协同优化控制

为了降低城市交通中的行车延误与燃油消耗,针对人类驾驶车辆与自动驾驶车辆混合交通环境,提出一种基于交通信息物理系统(TCPS)的车辆速度与交通信号协同优化控制方法.首先,综合考虑路口交通信号、人类驾驶车辆、自动驾驶车辆三者之间的相互影响,设计一种适用于自动驾驶车辆与人类驾驶车辆混合组队特性的过路口速度规划模型;其次,针对车辆速度规划单一应用时的局限性,即无法减少车辆路口通行延误且易出现无解情况,提出一种双目标协同优化模型,能够综合考虑车辆速度规划与路口交通信号控制,同时降低车辆燃油消耗与路口平均延误.由于双目标优化问题求解的复杂性,设计一种遗传算法-粒子群算法混合求解策略.基于SUMO的仿真实验验证了所提出方法的有效性.     

Iterative Decentralized Planning for Collective Construction Tasks with Quadrotors

This paper describes an iterative decentralized planning and learning method, based on stochastic learning automata theory and heuristic search techniques, to generate construction and motion strategies to build different types of three-dimensional structures using multiple quadrotors. This architecture is proposed to simultaneously solve three main problems: 1) the iterative generation of feasible construction and motion plans for each quadrotor; 2) the optimization with constraints on power and assembly while taking into account the dynamic nature of the environment, and 3) the planning of the translational speeds and selection of breakpoints for each vehicle. The quadrotors learn the optimal action policy to construct the structures while avoiding collisions during the loading and unloading procedures. In order to demonstrate the generality of the solution, simulated trials of the proposed autonomous construction system are presented where different three-dimensional structures are built.     

Real-time path planning for autonomous vehicle based on teaching–learning-based optimization

Intelligent Service Robotics - This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization...     

A new predictive motion control of a planar vehicle under uncertainty via convex optimization

This paper proposes an optimization-based planning framework for target tracking of a planar vehicle in the presence of some moving obstacles and uncertainty resources. The framework is modeled based on Linear-Time Varying Model Predictive Control (LTV-MPC). Moreover, a successive linearization approach is presented to formulate the obstacle avoidance mission as a set of linear constraints. Finally this LTV-MPC problem is translated to a close-form convex optimization problem by applying some mathematical and algebraic simplifications. Simulation results show beneficially of the proposed path planning method to navigate an autonomous vehicle successfully regardless of different uncertainty resources.     

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。     

Visual inspection of sea bottom structures by an autonomousunderwater vehicle

This paper describes a vision-based system for inspections of underwater structures, e.g., pipelines, cables, etc., by an autonomous underwater vehicle (AUV). Usually underwater inspections are performed by remote operated vehicles (ROVs) driven by human operators placed in a support vessel. However, this task is often challenging, especially in conditions of poor visibility or in presence of strong currents. The system proposed allows the AUV to accomplish the task in autonomy. Moreover, the use of a three-dimensional (3-D) model of the environment and of an extended Kalman filter (EKF) allows the guidance and the control of the vehicle in real time. Experiments done on real underwater images have demonstrated the validity of the proposed method and its efficiency in the case of critical and complex situations.     

自主车的运动仿真

在自主车的运动路径规划中,局部路径规划特别重要,而且是自主车的一项关键技术。该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,为确保自主车运动路径规划的可靠性和合理性,该文就局部路径规划对自主车作运动仿真。     

Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance

This paper provides a survey of motion planning techniques under uncertainty with a focus on their application to autonomous guidance of unmanned aerial vehicles (UAVs). The paper first describes the primary sources of uncertainty arising in UAV guidance and then describes relevant practical techniques that have been reported in the literature. The paper makes a point of distinguishing between contributions from the field of robotics and artificial intelligence, and the field of dynamical systems and controls. Mutual and individual contributions for these fields are highlighted providing a roadmap for tackling the UAV guidance problem.     

General fight rule-based trajectory planning for pairwise collision avoidance in a known environment

This paper presents a general flight rule-based autonomous trajectory planning scheme for two aerial vehicles to avoid obstacles and collisions in known environments in low-altitude airspace for general aviation. Flight rules in low-altitude airspace are first introduced based on the general flight rules in US, UK and China, and then the suitable flight rules are embedded into the trajectory planning algorithm. It is supposed that the flight parameters, such as positions and velocities, are all available to the aerial vehicles involved in the possible conflict. Then the trajectory of each aerial vehicle is calculated by optimizing an objective function, such as distance and fuel consumption, with the constraints corresponding to the airspace traffic rules. The optimization problem is solved by receding horizon control (RHC) based mixed integer linear programming (MILP). Compared with other collision avoidance algorithms, the proposed algorithm can be adapted to plan the autonomous trajectory to avoid pairwise collision and obstacles as proposed general flight rules. Simulations show the feasibility of the proposed scheme.     

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．     

Optimal Multi-Criteria Waypoint Selection for Autonomous Vehicle Navigation in Structured Environment

This paper deals with autonomous navigation of unmanned ground vehicles (UGV). The UGV has to reach its assigned final configuration in a structured environments (e.g. a warehouse or an urban environment), and to avoid colliding neither with the route boundaries nor any obstructing obstacles. In this paper, vehicle planning/set-points definition is addressed. A new efficient and flexible methodology for vehicle navigation throughout optimal and discrete selected waypoints is proposed. Combining multi-criteria optimization and expanding tree allows safe, smooth and fast vehicle overall navigation. This navigation through way-points permits to avoid any path/trajectory planning which could be time consuming and complex, mainly in cluttered and dynamic environment. To evaluate the flexibility and the efficiency of the proposed methodology based on expanding tree (taking into account the vehicle model and uncertainties), an important part of this paper is dedicated to give an accurate comparison with another proposed optimization based on the commonly used grid map. A set of simulations, comparison with other methods and experiments, using an urban electric vehicle, are presented and demonstrate the reliability of our proposals.     

基于改进蚁群优化算法的自动驾驶多车协同运动规划

当前面向多辆自动驾驶汽车的协同运动规划方法能有效保证运行车辆与障碍物及其他车辆之间避免发生碰撞并保持安全距离,但车辆间的在线协同与规划能力仍有待提升。为实现多辆自动驾驶汽车在运动过程中的协同控制,提出一种基于改进蚁群优化算法的多车在线协同规划方法。以空间协同与轨迹代价为优化目标,构造多目标优化函数,确保了多车行驶过程中的协同安全性与轨迹平滑性。将多目标优化函数引入蚁群优化算法的信息素更新过程中,根据自动驾驶车辆数量产生多个种群,使得种群之间相互独立的同时为每辆自动驾驶汽车规划可行路线。最终对蚁群优化算法中的挥发因子进行自适应调整,提升了算法全局搜索能力及收敛速度。实验结果表明,该方法能使多辆自动驾驶汽车在运动过程中保持协同控制并规划出无碰撞路线,相比于基于人工势场和模型预测的协同驾驶方法在复杂道路场景下车辆间的协同效果更好且适应性更强。     

Fast approximate clearance evaluation for rovers with articulated suspension systems

We present a light‐weight body‐terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse‐kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the approximate clearance evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst‐case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.     

Vision-based path following by using a neural network Guidance System

This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view-angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high-contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single-speed implementation and limited network training. Phase II featured a multi-speed implementation using adaptively varied view-angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi-speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.