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.     

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.      

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.     

Integrated Guidance and Feedback Control of Underactuated Robotics System in SE(3)

An integrated guidance and feedback control scheme for steering an underactuated vehicle through desired waypoints in three-dimensional space, is developed here. The underactuated vehicle is modeled as a rigid body with four control inputs. These control inputs actuate the three degrees of freedom of rotational motion and one degree of freedom of translational motion in a vehicle body-fixed coordinate frame. This actuation model is appropriate for a wide range of underactuated vehicles including spacecraft with internal attitude actuators, vertical take-off and landing (VTOL) aircraft, fixed-wing multirotor unmanned aerial vehicles (UAVs), maneuverable robotic vehicles, etc. The guidance problem is developed on the special Euclidean group of rigid body motions, SE(3), in the framework ofgeometric mechanics, which represents the vehicle dynamics globally on this configuration manifold. The integrated guidance and control algorithm selects the desired trajectory for the translational motion that passes through the given waypoints, and the desired trajectory for the attitude based on the desired thrust direction to achieve the translational motion trajectory. A feedback control law is then obtained to steer the underactuated vehicle towards the desired trajectories in translation and rotation. This integrated guidance and control scheme takes into account known bounds on control inputs and generates a trajectory that is continuous and at least twice differentiable, which can be implemented with continuous and bounded control inputs. The integrated guidance and feedback control scheme is applied to an underactuated quadcopter UAV to autonomously generate a trajectory through a series of given waypoints in SE(3) and track the desired trajectory in finite time. The overall stability analysis of the feedback system is addressed. Discrete time models for the dynamics and control schemes of the UAV are obtained in the form of Lie group variational integrators using the discrete Lagrange-d’Alembert principle. Almost global asymptotic stability of the feedback system over its state space is shown analytically and verified through numerical simulations.     

Vehicle detection and tracking in airborne videos by multi-motion layer analysis

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity.     

一种双无人机协同跟踪地面移动目标方法

针对通信延时情况下双无人机协同跟踪地面移动目标问题进行研究, 构建了基于分布式遗传算法和滚动时域优化结合的目标跟踪航迹规划算法模型。考虑到通信延时会增加目标状态信息数据融合时的误差, 导致无人机跟踪任务效果变差, 结合递推最小二乘滤波和加权最小二乘估计设计了融合方法, 来融合处理目标状态信息; 考虑到无人机对目标的观测效果与未来时刻的目标状态信息密切相关, 采用递推最小二乘滤波预测目标的状态信息, 结合分布式遗传算法和滚动时域优化设计了双无人机目标跟踪航迹规划算法。适应度函数考虑了无人机和目标之间的距离、无人机之间的通信距离、无人机之间的通信角度。仿真结果表明：该协同跟踪方法能够较好地完成跟踪任务; 与一架无人机跟踪相比误差明显减小, 并且可以减小通信延时带来的跟踪误差。     

四旋翼无人机三维航迹规划及跟踪控制

无人机航迹规划是指根据地形和威胁分布,规划出满足任务要求的合理航迹.为了满足三维空间快速规划的需求,提出了一种基于人工势场的三维航迹规划方法.首先,定义了目标和威胁物的虚拟力函数,推导出了三维空间参数约束方程,并采用联合威胁概念解决三维空间局部极小和振动问题;其次,引入空间圆弧插补技术生成光滑航迹;此外,为方便跟踪控制,提出了航迹时域化方法;最后,利用动态系统全局渐近稳定定理,设计具有全局Lipschitz的闭环系统,实现了具有内外环严格稳定性的双环轨迹跟踪控制.仿真结果验证了航迹规划和跟踪算法的有效性.     

封闭园区内无人驾驶洗扫车路径规划及控制

为实现封闭园区自动洗扫车的无人驾驶功能,设计了一种基于封闭园区场景的路径规划与控制算法。采用改进的A-Star算法并融合三阶贝塞尔曲线构建全局路径规划层;基于状态空间采样设计局部路径规划层,考虑洗扫车转向约束、安全碰撞距离、道路边界距离等约束设计代价函数得到最优局部路径;使用Pure-Pursuit跟踪算法对局部路径规划层输出的最优路径进行追踪;搭建Simulink-ROS联仿平台,在Simulink中编写规划控制算法并编译植入到ROS平台下进行仿真验证;最后采用MDC300F车载计算机对算法进行多工况实车实验。结果表明,该规划控制算法能够控制自动环卫洗扫车在多工况场景下完成跟车、避障、会车等无人驾驶功能。     

Motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: A distributed receding horizon approach

This paper presents a decentralized motion planner for a team of nonholonomic mobile robots subject to constraints imposed by sensors and the communication network. The motion planning scheme consists of decentralized receding horizon planners that reside on each vehicle to achieve coordination among flocking agents. The advantage of the proposed algorithm is that each vehicle only requires local knowledge of its neighboring vehicles. The main requirement for designing an optimal conflict-free trajectory in a decentralized way is that each robot does not deviate too far from its presumed trajectory designed without taking the coupling constraints into account. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages, especially in terms of computing time. Finally, experiments are performed on a team of three mobile robots to demonstrate the validity of the proposed approach.     

Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, heading rates and speeds.     

Autonomous Navigation of UAV in Foliage Environment

This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously using a 2D laser range finder in foliage environment without GPS. The navigation framework consists of real-time dual layer control, navigation state estimation and online path planning. In particular, the inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The navigation state estimation consists of real-time onboard motion estimation and trajectory smoothing using the GraphSLAM technique. The onboard real-time motion estimation is achieved by a Kalman filter, fusing the planar velocity measurement from matching the consecutive scans of a laser range finder and the acceleration measurement of an inertial measurement unit. The trajectory histories from the real-time autonomous navigation together with the observed features are fed into a sliding-window based pose-graph optimization framework. The online path planning module finds an obstacle-free trajectory based the local measurement of the laser range finder. The performance of the proposed navigation system is demonstrated successfully on the autonomous navigation of a small-scale UAV in foliage environment.     

基于滚动时域优化的无人飞行器轨迹规划

给出了寻求无人飞行器的最优轨迹的一种方法,其问题描述为使飞行器从初始状态飞行到目标状态,同时避免撞到障碍物。基于混合整数规划的滚动时域优化方法用来求解飞行器的轨迹规划问题。给出的仿真结果显示此方法的有效性以及在复杂环境下的可实时计算性。     

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。     

基于MCPDDPG的智能车辆路径规划方法及应用

针对智能车路径规划过程中常存在动态环境感知预估不足的问题,使用基于蒙特卡罗深度策略梯度学习(Monte Carlo prediction deep deterministic policy gradient, MCPDDPG)的智能车辆路径规划方法,设计一种基于环境感知预测、行为决策和控制序列生成的框架,实现实时的决策和规划,并输出连续的车辆控制序列.首先,利用序贯蒙特卡罗预估他车行为状态量;然后,设计基于强化Q学习的行为决策方法,使智能车辆实时预知碰撞风险,采取合理的规避策略;最后,构建深度策略梯度学习网络框架,获取智能车辆规划路径的最优轨迹序列.实验结果表明,所提方法能够缓解环境感知的预估不足问题,提升智能车辆行为决策的快速性,保障路径规划的主动安全,并输出连续的轨迹序列,为智能车辆导航控制提供前提.     

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.     

Decentralized planning and control for UAV–UGV cooperative teams

In this paper we study a symbiotic aerial vehicle-ground vehicle robotic team where unmanned aerial vehicles (UAVs) are used for aerial manipulation tasks, while unmanned ground vehicles (UGVs) aid and assist them. UGV can provide a UAV with a safe landing area and transport it across large distances, while UAV can provide an additional degree of freedom for the UGV, enabling it to negotiate obstacles. We propose an overall system control framework that includes high-accuracy motion planning for each individual robot and ad-hoc decentralized mission planning for complex missions. Experimental results obtained in a mockup arena for parcel transportation scenario show that the system is able to plan and execute missions in various environments and that the obtained plans result in lower energy consumption.     

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.     

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。     

Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms

The problem of integrating task assignment and planning paths for a group of cooperating uninhabited aerial vehicles, servicing multiple targets, is addressed. In the problem of interest the uninhabited aerial vehicles need to perform multiple consecutive tasks cooperatively on each ground target. A Dubins car model is used for motion planning, taking into account each vehicle's specific constraint of minimum turn radius. By using a finite set to define the visitation angle of a vehicle over a target we pose the integrated problem of task assignment and path optimization in the form of a graph. This new approach results in suboptimal trajectory assignments. Refining the visitation angle discretization allows for an improved solution. Due to the computational complexity of the resulting combinatorial optimization problem, we propose genetic algorithms for the stochastic search of the space of solutions. We distinguish between two cases of vehicle group composition: homogeneous, where all vehicles are identical; and heterogeneous, where the vehicles may have different operational capabilities and kinematic constraints. The performance of the genetic algorithms is demonstrated through sample runs and a Monte Carlo simulation study. Results show that the algorithms quickly provide good feasible solutions, and find the optimal solution for small sized problems.     

智能车辆深度强化学习的模型迁移轨迹规划方法

针对智能驾驶车辆传统路径规划中出现车辆模型跟踪误差和过度依赖问题,提出一种基于深度强化学习的模型迁移的智能驾驶车辆轨迹规划方法.首先,提取真实环境的抽象模型,该模型利用深度确定性策略梯度(DDPG)和车辆动力学模型,共同训练逼近最优智能驾驶的强化学习模型;其次,通过模型迁移策略将实际场景问题迁移至虚拟抽象模型中,根据该环境中训练好的深度强化学习模型计算控制与轨迹序列;而后,根据真实环境中评价函数选择最优轨迹序列.实验结果表明,所提方法能够处理连续输入状态,并生成连续控制的转角控制序列,减少横向跟踪误差;同时通过模型迁移能够提高模型的泛化性能,减小过度依赖问题.