基于相对定位信息的局部动态轨迹规划方法研究

目前,自动驾驶车辆用高级驾驶辅助系统（ADAS）往往仅能通过识别车道线获取局部相对定位信息,如何在车道线识别存在跳变和遇到障碍物时避免碰撞是ADAS急需解决的关键技术之一。对此,文章提出一种基于相对定位信息的局部动态轨迹规划方法,其首先在车辆曲线坐标系中进行网格采样,经过坐标转换后获得车辆坐标系下的候选路径集,并通过碰撞检查和评价函数对路径集进行筛选,获得无碰撞最优路径;接着,根据最优路径的曲率和横向偏移来自适应调节最高规划速度,并由改进梯形速度规划方法获得速度曲线;最后,通过梯度下降算法平滑速度曲线,以避免加速度突变。仿真和实车试验结果表明,采用所提算法,车辆可以在有弯道的局部相对定位场景下平稳纠偏、避障和跟车行驶;并且,在无障碍物场景下,车辆在仿真和实车试验中的最大横向偏移分别降低36.7%和28.6%、平均横向加速度分别减小29.6%和46.4%,具有较高的舒适性和安全性。     

一种新的实时智能汽车轨迹规划方法

针对智能汽车的驾驶决策和轨迹规划问题, 将轨迹表示为轨迹曲线和加速度变化两部分, 以优化轨迹的行驶效率、安全性、舒适性和经济性为目标建立非线性规划模型. 基于序优化思想, 提出混合智能优化算法OODE, 分内、外两层分别优化加速度变化和轨迹曲线, 通过“粗糙” 评价轨迹曲线实现轨迹曲线的快速择优. 仿真结果表明, 所提出的方法能够处理包含多动态障碍物的复杂交通场景, 且具备实时应用能力, 模型的精度和求解速度均优于传统方法.

     

基于交互车辆轨迹预测的自动驾驶车辆轨迹规划

针对城市道路等复杂行车场景,提出了一种基于交互车辆轨迹预测的自动驾驶车辆轨迹规划方法,将高维度的轨迹规划解耦为低维度的路径规划和速度规划;首先,采用五次多项式曲线和碰撞剩余时间规划车辆行驶路径;其次,在社会生成对抗网络Social-GAN的基础上结合车辆空间影响和注意力机制对交互车辆进行轨迹预测;然后,结合主车规划路径、交互车辆预测轨迹及碰撞判定模型得到主车S-T图,采用动态规划和数值优化方法求解S-T图,从而得到满足车辆动力学约束的安全、舒适最优速度曲线;最后,搭建PreScan-CarSim-Matlab&Simulink-Python联合仿真模型进行实验验证。仿真结果表明,提出的轨迹规划方法能够在对交互车辆有效避撞的前提下,保证车辆行驶的舒适性和高效性。     

基于改进人工鱼群算法的车辆轨迹规划方法

针对车联网环境下若干典型车辆轨迹规划方法存在车速与轨迹波动性较大的问题,提出一种基于改进人工鱼群算法的车辆轨迹规划方法。该方法以短程通信（DSRC）的车联网应用场景为设计平台,以车辆的最优行车速度为核心计算基础,分析得到了车辆的最佳轨迹。首先,对人工鱼群算法在车联网应用场景的优势和不足进行分析,引入万有引力力学模型与避障模式控制,提出一种改进的人工鱼群算法;然后,分析车辆在车联网应用场景中的受力约束,利用网联车辆的自组织行为控制策略推导最优行车速度;最后,基于最优行车速度实现对车辆的实时轨迹诱导和轨迹避障控制规划。仿真测试结果表明,在运用了基于改进人工鱼群算法的轨迹规划模型后,车辆的驾驶速度更加平稳,轨迹波动性较小,对障碍物可实现零失误避撞;在多车相遇情况下,测试车辆为2~40时,相对于原人工鱼群算法和萤火虫算法,运用改进人工鱼群算法后车速的平均迭代次数减少,迭代效率提高3~7、4~8倍,且随着车辆数目越多,迭代效率提升越明显。     

基于贝塞尔曲线的智能汽车避障轨迹规划方法研究

开展了一种基于贝塞尔曲线的智能汽车避障局部轨迹规划,即路径规划和速度规划方法研究。路径规划时,为了适应各种形状道路,将道路笛卡尔坐标转换为Frénet坐标,以路径的长度、曲率和连续性,以及车辆碰撞风险为代价函数,其中引入危险势场理论,描述车辆碰撞风险,并采用序列二次规划方法来求解路径规划这一非线性优化问题;速度规划时,以行车效率和舒适性为目标,实现速度规划,该方法可以通过调整各子目标函数的权重来满足不同驾驶需求。为了验证基于贝塞尔曲线轨迹规划算法的有效性,设计了直道和弯道上静态和动态避障场景的仿真实验,结果表明,提出的轨迹规划方法能够在各种形状道路上完成避障任务,且避障过程中车辆状态变化平稳,能够保证乘坐舒适性。     

V2X环境下基于MPC算法的弯道区域CACC车队轨迹跟踪控制策略

针对CACC(cooperative adaptive cruise control)车队在弯道行驶的安全性和稳定性问题,提出一种V2X(vehicle to everything)环境下基于MPC(model predictive control)算法的弯道区域CACC车队行驶轨迹跟踪策略.首先,分析CACC车队在弯道区域的行驶工况以及纵向平衡问题,并基于牛顿第二定律构建车辆在弯道行驶的车辆动力学模型;其次,CACC车队基于V2X技术实现车车之间状态信息的实时交互,并以基于车辆运动学的MPC算法为基础,引入可变间距的车队安全距离控制模型,提出一种适用于弯道区域的轨迹跟踪模型;最后,通过二次规划进行模型求解.实验分析结果表明:V2X环境下的CACC车队在弯道行驶过程中面对不同的行驶工况能够不同程度地保证车车之间的安全性、稳定性以及驾乘人员的舒适性,有效验证了所提V2X环境下基于MPC算法的弯道区域CACC车队轨迹跟踪策略的可行性.     

基于改进RRT^(*)与行驶轨迹优化的智能汽车运动规划EI北大核心CSCD

针对传统快速扩展随机树算法(Rapidly-exploring random tree,RRT)搜索较慢、规划路径曲折、平顺性差等问题,提出了一种结合改进RRT^(*)与贝塞尔曲线控制点优化的智能车辆运动规划方法.该方法通过在给定概率分布下采样,结合基于方向相似性的多步扩展与路径简化,使用贝塞尔曲线拟合生成规划问题初始解,最后使用序列二次规划优化曲线控制点,从而在动态障碍物环境中生成兼具安全性与驾驶舒适性的车辆行驶轨迹.在仿真实验中将本文算法与常规RRT及曲线拟合方法进行了比较,结果显示本文算法在搜索速度、平顺性、安全性等方面有较大提升.     

基于轨迹安全性评价的免疫遗传路径规划算法

针对移动机器人栅格路径规划中安全轨迹规划、局部极小点问题,提出了一种基于轨迹安全性评价的免疫遗传路径规划算法.通过将人工势场(APF)模型与轨迹安全性评价相结合,提出利用斥力场强度评估轨迹安全性,建立基于轨迹安全性、行驶代价评估的适应度函数,利用免疫遗传算法对APF模型中的势力场参数进行自适应优化估计.通过将参数变化控制在一个合理的区间,有效避免局部极小情况的发生,同时提高了路径的安全性.算法的有效性通过仿真实验得到了验证.     

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.     

基于微分平坦与MPC的智能车换道控制算法

为了提高智能车换道的安全性,提出了一种基于微分平坦理论与模型预测控制(MPC)算法相结合的智能车换道轨迹规划与跟踪算法。该算法利用约束求解得到基于sigmoid函数的优化路径;将其与多项式参数化时间函数作为平坦输出,利用微分平坦理论构造一个非线性性能指标函数并对其进行优化求解完成车速规划;从而实现对智能车辆路径-速度分解式的轨迹规划。利用动力学模型预测控制算法线上控制的优点,对智能车的车轮转向进行实时控制,使得车辆按照规划好的轨迹行驶完成换道。通过CarSim与MATLAB/Simulink的联合仿真,将提出的轨迹规划算法应用于车辆系统仿真软件中进行验证,结果表明该算法能够实现对智能车进行轨迹规划和跟踪控制,使其安全高效地换至目标车道。     

基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。     

On optimal constrained trajectory planning in 3D environments

A novel approach to generating acceleration-based optimal smooth piecewise trajectories is proposed. Given two configurations (position and orientation) in 3D, we search for the minimal energy trajectory that minimizes the integral of the squared acceleration, opposed to curvature, which is widely investigated. The variation in both components of acceleration: tangential (forces on gas pedal or brakes) and normal (forces that tend to drive a car on the road while making a turn) controls the smoothness of generated trajectories. In the optimization process, our objective is to search for the trajectory along which a free moving robot is able to accelerate (decelerate) to a safe speed in an optimal way. A numerical iterative procedure is devised for computing the optimal piecewise trajectory as a solution of a constrained boundary value problem. The resulting trajectories are not only smooth but also safe with optimal velocity (acceleration) profiles and therefore suitable for robot motion planning applications. Experimental results demonstrate this fact.     

An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.     

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.     

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.     

A technique for time-jerk optimal planning of robot trajectories

A technique for optimal trajectory planning of robot manipulators is presented in this paper. In order to get the optimal trajectory, an objective function composed of two terms is minimized: a first term proportional to the total execution time and another one proportional to the integral of the squared jerk (defined as the derivative of the acceleration) along the trajectory. This latter term ensures that the resulting trajectory is smooth enough. The proposed technique enables one to take into account kinematic constraints on the robot motion, expressed as upper bounds on the absolute values of velocity, acceleration and jerk. Moreover, it does not require the total execution time of the trajectory to be set a priori. The algorithm has been tested in simulation yielding good results, also in comparison with those provided by another important trajectory planning technique.     

FIR filters for online trajectory planning with time- and frequency-domain specifications

In this paper, the use of FIR (Finite Impulse Response) filters for planning minimum-time trajectories for robots or automatic machines under constraints of velocity, acceleration, etc. is presented and discussed. In particular, the relationship between multi-segment polynomial trajectories, i.e. trajectories composed of several polynomial segments, each one possibly characterized by constraints on one or more specific derivatives (i.e. velocity, acceleration, jerk, etc.), and FIR filters disposed in a cascade configuration is demonstrated and exploited in order to design a digital filter for online trajectory planning. The connection between analytic functions and dynamic filters allows a generalization of these trajectories, usually obtained by second- or third-order polynomial functions (e.g. trapezoidal velocity and double S velocity trajectories), to a generic order with only a modest increase of the complexity. As a matter of fact, the computation of trajectories with higher degree of continuity simply requires additional FIR filters in the chain. Moreover, the modular structure of the planner provides a direct frequency characterization of the motion law. In this way, it is possible to define the trajectories by considering constraints expressed in the frequency-domain besides the classical time-domain specifications, such as bounds on velocity, acceleration, and so on. Two examples illustrate the main features of the proposed trajectory planner, in particular with respect to the problems of multi-point trajectories generation and residual vibrations suppression.     

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.     

Gaussian mixture spline trajectory: learning from a dataset,generating trajectories without one

Most optimization-based motion planners use a naive linear initialization, which does not use previous planning experience. We present an algorithm called ‘Gaussian mixture spline trajectory’ (GMST) that leverages motion datasets for generating trajectories for new planning problems. Unlike other trajectory prediction algorithms, our method does not retrieve trajectories from a dataset. Instead, it first uses a Gaussian mixture model (GMM) to modelize the likelihood of the trajectories to be inside the dataset and then uses the GMM's parameters to generate new trajectories. As the use of the dataset is restricted only to the learning phase it can take advantage of very large datasets. Using both abstract and robot system planning problems, we show that the GMST algorithm decreases the computation time and number of iterations of optimization-based planners while increasing their success rates as compared to that obtained with linear initialization.     

Time-optimal parabolic interpolation with velocity,acceleration, and minimum-switch-time constraints

Time-optimal trajectories with bounded velocities and accelerations are known to be parabolic, i.e. piecewise constant in acceleration. An important characteristic of this class of trajectories is the distribution of the switch points – the time instants when the acceleration of any robot joint changes. When integrating parabolic trajectory generation into a motion planning pipeline, especially one that involves a shortcutting procedure, resulting trajectories usually contain a large number of switch points with a dense distribution. This high frequency acceleration switching intensifies joint motor wear as well as hampers the robot performance. In this paper, we propose an algorithm for planning parabolic trajectories subject to both physical bounds, i.e. joint velocity and acceleration limits, and the minimum-switch-time constraint. The latter constraint ensures that the time duration between any two consecutive switch points is always greater than a given minimum value. Analytic derivations are given, as well as comparisons with other methods to demonstrate the efficiency of our approach.