GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

This paper describes GPU based algorithms to compute state transition models for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle–wave interaction. A state transition model is a key component of the Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. The USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 min. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.     

Modeling,Identification, and Control of an Unmanned Surface Vehicle

This paper describes planar motion modeling for an unmanned surface vehicle (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planing conditions. The modeling and identification objective is to determine a model that is sufficiently rich to enable effective model‐based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hullforms and actuator configurations. We focus, however, on a specific platform: a modified rigid hull inflatable boat with automated throttle and steering. Analysis of experimental results for this vessel indicates that Nomoto's first‐order steering model provides the best compromise between simplicity and fidelity at higher speeds. At low speeds, it is helpful to include a first‐order lag model for sideslip. Accordingly, we adopt a multiple model approach in which the model structure and parameter values are scheduled based on the nominal forward speed. The speed‐scheduled planar motion model may be used to generate dynamically feasible trajectories and to develop trajectory tracking control laws. The paper describes the development, analysis, and experimental implementation of two trajectory tracking control algorithms: a cascade of proportional‐derivative controllers and a nonlinear controller obtained through backstepping. Experimental results indicate that the backstepping controller is much more effective at tracking trajectories with highly variable speed and course angle. © 2013 Wiley Periodicals, Inc.     

Two-Step System Identification and Trajectory Tracking Control of a Small Fixed-Wing UAV

An approach for obtaining dynamically feasible reference trajectories and feedback controllers for a small unmanned aerial vehicle (UAV) based on an aerodynamic model derived from flight tests is presented. The modeling method utilizes stepwise multiple regression to determine relevant explanatory terms for the aerodynamic coefficients. A dynamically feasible trajectory is then obtained through the solution of an optimal control problem using pseudospectral optimal control software. Discrete-time feedback controllers are further designed to regulate the vehicle along the desired reference trajectory. Simulations in a realistic operational environment as well as flight testing of the feedback controllers on the aircraft platform demonstrate the capabilities of the approach.     

Parametric Planning for Multiple Cooperative Robots

A novel planning strategy, parametric planning, is proposed to negotiate the task-oriented object manipulation of multiple coordinated robots. The approach provides an advantage to improve flexibility of robotic cooperation, in which the desired trajectories in Cartesian space derived from task requirements are converted into the trajectories of robots in joint space for a fixed-coordinated multi-robot system. For this purpose, a parametric cooperative index matrix is introduced to handle the relationship of the input desired Cartesian trajectories and the position of robots. A case study of 2-dimension object-motion trajectory tracking using four robots is presented in the end. It proved that the proposed approach effectively delivers trajectory task requirements to the joint trajectories of robots.     

From Trajectory Tracking Control to Leader–Follower Formation Control

Abstract

This work investigates the leader–follower formation control of multiple nonholonomic mobile robots. First, the formation control problem is converted into a trajectory tracking problem and a tracking controller based on the dynamic feedback linearization technique drives each follower robot toward its corresponding reference trajectory in order to achieve the formation. The desired orientation for each follower is selected such that the nonholonomic constraint of the robot is respected, and thus the tracking of the reference trajectory for each follower is feasible. An adaptive dynamic controller that considers the actuators dynamics in the design procedure is proposed. The dynamic model of the robots includes the actuators dynamics in order to obtain the velocities as control inputs instead of torques or voltages. Using Lyapunov control theory, the tracking errors are proven to be asymptotically stable and the formation is achieved despite the uncertainty of the dynamic model parameters. In order to assess the proposed control laws, a ROS-framework is developed to conduct real experiments using four ROS-enabled mobile robots TURTLEBOTs. Moreover, the leader fault problem, which is considered as the main drawback of the leader–follower approach, is solved under ROS. An experiment is conducted where in order to overcome this problem, the desired formation and the leader role are modified dynamically during the experiment.     

Roadmap-based motion planning in dynamic environments

In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.     

接触作业型飞行机械臂系统的力/位置混合控制

针对飞行机械臂系统移动接触作业问题,使用了一个力/位置混合控制框架,用以控制飞行器系统持续可靠地接触外部环境同时保持一定大小的接触力,并实现在接触过程中的期望轨迹跟踪.首先将作业空间分成2个子空间--约束空间和自由空间,并分别进行力控制和位置控制.对于力控制问题,证明闭环无人机系统是一个类弹簧-质量-阻尼系统,然后在约束子空间中设计逆动力学控制器来实现接触力控制.自由飞行空间中的运动控制依靠轨迹规划和位置控制器来实现.最后,开发了基于六旋翼飞行机器人的单自由度飞行机械臂系统,在飞行状态下进行接触墙面并跟踪倾斜直线轨迹的实验.结果显示本文所使用方法能够保证在平稳移动的同时控制期望的接触力.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

Motion control and obstacle avoidance of a mobile robot with an onboard manipulator

Navigation and control of autonomous mobile vehicles with onboard manipulator systems are currently being investigated for intelligent manufacturing applications. A systematic approach for modeling and base motion control of a mobile vehicle with an onboard robot arm is presented. Feedback linearization is used to take into account the complete dynamics with non-holonomic constraints, yet methods from potential field theory are incorporated to provide resolution among possibly conflicting performance goals (e.g. path following and obstacle avoidance). The feedback linearization provides an inner loop that accounts for possible motion of the onboard arm. The two cases of maintaining a desired course and speed, and following a desired Cartesian trajectory are considered. The outer control loop is designed using potential field theory, with the two objectives of homing and avoiding an obstacle. This simple result obtained using potential functions provides very naturally the necessary intelligence for online resolution of conflicting performance objectives. It gives capabilities to these autonomous vehicles for maintaining a desired course and speed or tracking a Cartesian trajectory, avoiding obstacles during the course of travel, and initiating new online path planning when the size of the object is large so that unnecessary wandering in the work space is avoided.     

A novel cartesian trajectory planning method by using triple NURBS curves for industrial robots

This article presents a novel method of robot pose trajectory synchronization planning. First of all, based on triple NURBS curves, a method of describing the position and orientation synchronization of the robot is proposed. Then, through considering geometric and kinematic constraints, especially angular velocity constraint, and employing bidirectional interpolation algorithm, a robot pose trajectory planning approach is developed, which has limited linear jerk, continuous bounded angular velocity and approximate optimal time, and does not need an optimization program. Ultimately, two robot pose paths, blade-shaped curve and fan-shaped curve, are utilized for simulations, and the results indicate that the proposed trajectory planning method can satisfy the given constraint conditions, i.e. the linear jerk is limited and the angular velocity is continuous bounded. The trajectory tracking experiments are further carried out on a 6-DOF industrial robot, and the results show that the proposed planning method can generate smooth trajectories to ensure the stability of the robot motion without impact in practical situations.     

机器人操作臂跟踪动态目标的轨迹规划方法

针对机器人操作臂跟踪运动目标问题,提出一种基于遗传算法的轨迹规划方法。通过对关节加速度的增量进行编码,实现在操作臂的关节空间进行轨迹优化,得到操作臂在跟踪运动目标过程中所需要的轨迹。仿真计算的结果表明,所提出的方法是有效的。     

Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty

We address the problem of position trajectory-tracking and path-following control design for underactuated autonomous vehicles in the presence of possibly large modeling parametric uncertainty. For a general class of vehicles moving in either 2- or 3-D space, we demonstrate how adaptive switching supervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of global boundedness and convergence of the position tracking error to a neighborhood of the origin that can be made arbitrarily small. The desired trajectory does not need to be of a particular type (e.g., trimming trajectories) and can be any sufficiently smooth bounded curve parameterized by time. We also show how these results can be applied to solve the path-following problem, in which the vehicle is required to converge to and follow a path, without a specific temporal specification. We illustrate our design procedures through two vehicle control applications: a hovercraft (moving on a planar surface) and an underwater vehicle (moving in 3-D space). Simulations results are presented and discussed.     

平面四旋翼无人飞行器运送系统的轨迹规划与跟踪控制器设计

对于四旋翼无人飞行器运送系统而言,需要保证飞行过程中负载的摆幅维持在适当的范围内,并且在飞行器到达目的地后负载无残余摆动.本文针对四旋翼无人飞行器运送系统,提出了一种新颖的轨迹规划与跟踪控制方法.论文首先得到了平面四旋翼无人飞行器的运动特性与负载摆角之间的非线性耦合关系.通过相平面内的几何分析,分别设计了两个轴方向上的分段式加速度轨迹.这种轨迹具有简洁的解析表达式并可获得较高的运送效率,同时满足飞行器的速度,加速度等物理约束.为了使四旋翼无人飞行器准确跟踪规划好的轨迹,本文基于反步法设计了一种非线性跟踪控制器,并通过李雅普诺夫方法对其闭环稳定性进行分析,证明其能使跟踪误差指数收敛于零.论文最后通过仿真结果验证了本文所提出方法的可行性与有效性,及其对外界干扰的鲁棒性.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

双连杆刚柔机械臂无残余振动位置控制

针对双连杆刚柔机械臂,提出一种基于轨迹规划的无残余振动位置控制方法,在将机械臂的末端执行器从任意初始位置移动到目标位置的同时,确保系统没有残余振动产生.首先,建立系统的动力学模型,并通过分析该模型得到系统的状态约束方程.其次,基于状态约束方程,运用双向轨迹规划方法规划一条系统前向轨迹和一条系统反向轨迹.然后,利用时间倒转方法及基于遗传算法的轨迹优化方法对两条轨迹进行拼合,得到一条从系统初始状态到目标状态的期望轨迹.最后,设计轨迹跟踪控制器使系统沿期望轨迹到达目标状态,实现系统的无残余振动位置控制目标.仿真结果验证了本文所提方法的有效性.     

Universal Practical Tracking Control of a Planar Underactuated Vehicle

In this article, a universal controller is proposed for a planar underactuated vehicle to track arbitrary trajectories including feasible/non‐feasible ones and fixed points. The controller design relies on several coordinate/input transformations, auxiliary trajectory design and the back‐stepping technique. The stability analysis shows that the position and orientation tracking errors are uniformly globally practically asymptotically convergent (UGPAC), and the velocity tracking errors are uniformly globally asymptotically convergent (UGAC) to a ball of origin. Moreover, if the tracked target is in uniform rectilinear motion or motionless, the whole closed‐loop tracking error system is uniformly globally practically asymptotically stable (UGPAS). The effectiveness of proposed control law is verified by simulation examples.     

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

As the complexity of an unmanned vehicle’s operational environment increases so does the need to consider the obstacle space continually, and this is aided by splitting the motion planning functionality into distinct global and local layers. This paper presents a new continuous local motion planning framework, where the output and control space elements of the traditional receding horizon control problem are separated into distinct layers. This separation reduces the complexity of the local motion trajectory optimisation, enabling faster design and increased horizon length. The focus of this paper is on the output space component of this framework. Bezier polynomial functions are used to describe local motion trajectories which are constrained to vehicle performance limits and optimised to track a global trajectory. Development and testing is in simulation, targeted at a nonlinear model of a quadrotor unmanned air vehicle. The defined framework is used to provide situation-aware tracking of a global trajectory in the presence of static and dynamic obstacles, as well as realistic turbulence and gusts. Also demonstrated is the immediate-term decentralised deconfliction of multiple unmanned vehicles, and multiple formations of unmanned vehicles.     

运动目标三维轨迹可视化与关联分析方法

随着治安监控系统的普及,越来越多的监控摄像头被安装在各个交通道路和公共场所中,每天都产生大量的监控视频.如今,监控视频分析工作主要是采用人工观看的方式来排查异常,以这种方式来分析视频内容耗费大量的人力和时间.目前,关于视频分析方面的研究大多是针对目标个体的异常行为检测和追踪,缺乏针对对象之间的关联关系的分析,对视频中的一些对象和场景之间的关联关系等还没有较为有效的表示和分析方法.针对这一现状,提出一种基于运动目标三维轨迹的关联视频可视分析方法来辅助人工分析视频,首先对视频资料进行预处理,获取各个目标对象的运动轨迹信息,由于二维轨迹难以处理轨迹的自相交、循环运动和停留等现象,并且没有时间信息就难以对同一空间内多个对象轨迹进行的关联性分析,于是结合时间维度对轨迹进行三维化扩展.该方法支持草图交互方式来操作,在分析过程中进行添加草图注释来辅助分析.可结合场景和对象的时空关系对轨迹进行关联性计算,得出对象及场景之间的关联模型,通过对对象在各个场景出现状况的统计,结合人工预先设定的规则,可实现对异常行为报警,辅助用户决策.     

无人车辆轨迹规划与跟踪控制的统一建模方法

无人车辆的轨迹规划与跟踪控制是实现自动驾驶的关键.轨迹规划与跟踪控制一般分为两个部分，即先根据车辆周边环境信息以及自车运动状态信息规划出参考轨迹，再依此轨迹来调节车辆纵横向输出以实现跟随控制.本文通过对无人车辆的轨迹规划与跟踪进行统一建模，基于行车环境势场建模与车辆动力学建模，利用模型预测控制中的优化算法来选择人工势场定义下的局部轨迹，生成最优的参考轨迹，并在实现轨迹规划的同时进行跟踪控制.通过CarSim与MATLAB/Simulink的联合仿真实验表明，该方法可在多种场景下实现无人车辆的动态避障.