Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

In this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.     

A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles

Maintaining the connectivity of networked robots is a challenge in multirobot applications. In this paper, this challenging problem is addressed through the development of a novel controller that can guarantee that robots will approach their individual desired positions while maintaining existing network topology and avoiding obstacles. A new concept of connectivity constraint, along with a continuous modeling approach to obstacle avoidance, is utilized in building the navigation function. The designed potential field integrates the navigation requirement, connectivity constraint, and obstacle avoidance simultaneously, based on which a bounded control input is generated for multirobot control. It is shown that if the initial configurations of the robots are connected and the desired configuration is reachable, the proposed controller is capable of driving multirobots to their individual goal positions conditionally while keeping the underlying network connected. Simulations and experiments are finally performed using a group of mobile robots to demonstrate the effectiveness of the proposed controller.     

Tracking Control for a Cushion Robot Based on Fuzzy Path Planning With Safe Angular Velocity

This study proposes a new nonlinear tracking control method with safe angular velocity constraints for a cushion robot. A fuzzy path planning algorithm is investigated and a realtime desired motion path of obstacle avoidance is obtained. The angular velocity is constrained by the controller, so the planned path guarantees the safety of users. According to Lyapunov theory, the controller is designed to maintain stability in terms of solutions of linear matrix inequalities and the controller's performance with safe angular velocity constraints is derived. The simulation and experiment results confirm the effectiveness of the proposed method and verify that the angular velocity of the cushion robot provided safe motion with obstacle avoidance.      

基于收缩约束模型预测控制的无人车辆路径跟踪

针对存在有界扰动的非线性无人驾驶车辆避障过程中最优路径规划跟踪问题,提出一种基于预测时域内系统输入输出收缩约束(PIOCC)的模型预测控制(MPC)方法.首先在构建目标函数时,为扩大可行性解的范围引入软约束思想,将最优规划路径的跟随问题转化为对模型预测控制优化问题的求解;其次为避免短预测时域造成闭环系统发散而导致在约束条件限定下出现无可行性解的情况,采用预测时域内系统输入输出收缩约束的方法,设计模型预测控制器;再次基于Lyapunov稳定性理论证明所设计的模型预测闭环控制系统是渐近稳定的;最后通过仿真实例验证了所提出基于PIOCC的控制策略在解决扩大可行解范围和避免闭环系统发散问题时的有效性,实现了无人驾驶车辆在路径跟踪时具有良好的快速性和稳定性.     

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.      

移动在线水质监测平台动态避障方法

针对移动水质监测平台在自主导航中遇到移动障碍物的问题,提出了一种将障碍物运动状态预测模型结合速度避障碰撞模型的动态避障新方法。首先,通过移动水质监测平台上的超声波测距模块和图像采集模块测量移动水质监测平台与障碍物的距离和相对方位角,采用坐标系转换的方法计算出障碍物速度和运动方向;其次,利用极大似然估计法建立障碍物运动状态预测模型,通过该模型得到下一个采样时刻障碍物速度和运动方向范围;最后,利用速度避障的碰撞模型,计算出下一时刻的移动水质监测平台的航向角。实验结果证明,所提的避障方法能够规划出一条更为真实的较优路径。与无障碍物运动状态预测模型的避障方法相比,该避障方法能提高动态避障的成功率。     

基于行为优先与模糊控制的移动机器人导航

由于动态未知环境下自主移动机器人的导航具有较大困难,为实现自主机器人在动态未知环境下的无碰撞运行,文中将行为优先级控制与模糊逻辑控制相结合,提出4种基本行为控制策略:目标寻找、避障、跟踪和解锁.针对'U'型和'V'型障碍物运行解锁问题,提出了行走路径记忆方法,并通过构建虚拟墙来避免机器人再次走入此类区域.仿真实验表明,所提出的控制策略可有效地运用于复杂和未知环境下自主移动机器人的导航,且具有较好的鲁棒性和适应性.     

基于三维成像声纳技术的AUV前视避障方法

针对自主式水下航行器（ AUV）在水下自主航行过程中的避障问题,提出了一种基于三维成像声纳技术的前视避障方法。该方法使用三维成像声纳探测目标,通过声纳目标图像处理提取目标,完成目标的运动状态分析和轨迹预测。通过设置碰撞区域,建立避障模型和设计避障规则,实现了AUV的智能化避障。借助于其他传感器,这些关联模块构成了一个集目标探测、目标提取、轨迹预测、避障与路线回归等功能于一体的闭环系统。     

基于多行为的移动机器人路径规划

机器人由当前点向目标点运动的过程中,所处环境经常为动态变化且未知的,这使得传统的路径规划算法对于移动机器人避障过程很难建立精确的数学模型.为此,针对环境信息完全未知的情况,为移动机器人设计一种基于模糊控制思想的多行为局部路径规划方法.该方法通过对各种行为之间进行适时合理的切换,以保证机器人安全迅速地躲避静态和动态障碍物,并利用改进的人工势场法实现对变速目标点的追踪.对于模糊避障中常见的U型陷阱问题,提出一种边界追踪的陷阱逃脱策略,使得机器人成功解除死锁状态.另外,设计一个速度模糊控制器,实现了机器人的智能行驶.最后,基于Matlab平台的仿真结果验证了所提出算法的有效性和实时性,与A*势场法的对比结果更突出了该算法的可行性.     

未知环境下基于改进DWA的多机器人编队控制

针对多机器人系统在未知环境下难以有效避障和保持队形的问题,在改进动态窗口法(DWA)的基础上,提出一种领航-跟随法与行为法相结合的多机器人编队控制算法.首先,通过修正速度窗口和3个现有评价函数,并添加两个新的评价函数改进DWA算法,增加速度的采样范围,提高优秀轨迹的评分,并增强机器人朝目标导航和未知环境下的全局搜索能力;然后,对周围环境和编队状态实时检测,为各机器人设计不同的行为(包括导航,避障,跟踪和等待)及其选择方式,兼顾编队避障及队形保持;接着,基于改进DWA和社会力模型(SFM)设计行为控制策略,在未知环境下使领航者能够规划适合整体编队运行的路径,跟随者能够根据编队的不同状态自适应地切换跟随方式;最后,基于Matlab和V-REP进行一系列仿真,结果表明在未知环境下,所提出的改进DWA能够显著提高机器人的通行效率和全局搜索能力,编队控制算法能够实现队形稳定保持、灵活避障与变换.     

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

The existing automated lifting robot technology focuses merely on motion control and ignores the surrounding environment. In practice, obstacles inevitably exist in the movement path of the automated lifting robot, which affects construction safety. Furthermore, due to the underactuated characteristics of the automated lifting robot, the load can be difficult to control when it swings violently, which undoubtedly poses huge challenges to obstacle avoidance trajectory planning and controller design. In this paper, an obstacle avoidance trajectory and its tracking controller with antiswing and tracking errors constraint are proposed. To ensure accurate load positioning and effective obstacle avoidance, the proposed control method introduces a four-segment polynomial trajectory interpolation curve to construct an obstacle avoidance trajectory based on analyzing the geometric relationship between variables. To improve the transient coupling control performance of the system, combined with the passive analysis of the automated lifting robot system, this method constructs a potential function that limits the tracking error and a coupling signal that enhances the coupling relationship between the system variables. Barbalat's lemma and Lyapunov techniques are used to analyze the stability of the system. Simulation and experimental results show that the proposed control method can significantly suppress or even eliminate load oscillation, accurately locate the load, avoid obstacles, improve the safety and efficiency of the working automated lifting robot, and have strong robustness to changes in system parameters and the addition of external disturbances.     

A robust navigation system for robotic wheelchairs

A landmark based navigation system for robotic wheelchairs is developed. The proposed navigation system is robust in the localization procedure which is the major problem in robotic navigation systems. Every landmark is composed of a segment of metallic path and a radio-frequency identification (RFID) tag. The odometry information is used for localization, which is corrected on-line every time the robotic wheelchair is over a landmark. A topological map is generated using such landmarks to compute the shortest path. A technique to generate the topological map for this navigation system and an obstacle avoidance strategy are also developed.     

无人机三维路径规划及避障方法

针对无人机(UAV)在三维环境中如何由起始点到目标点合理地规划路径避开障碍物,提出了一种基于改进粒子群算法与滚动策略相结合的UAV路径规划与避障方法.该方法首先以UAV为中心,通过传感器建立UAV的可视区域模型;其次结合滚动策略滚动探知UAV周围环境信息;最后,利用改进的粒子群算法进行路径搜索,并加入综合转角控制提高路径的平滑性.在传统粒子群算法中加入信息素与启发函数,增强算法的全局搜索能力,并对参数进行特定设计提高算法的收敛速度.仿真结果表明,该方法可以实现实时避障,所规划的路径相对平滑,且改进算法比传统算法具有较高的收敛性.     

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.     

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.     

基于规则的移动机器人实时运动规划

研究移动机器人在动态环境中的导航与避障问题。为提高规划的实时性,提出了基于规则的规划方法,将多移动障碍环境机器人的运动规划分解为相对简单的单移动障碍运动规划,利用最优控制来实现单障碍的最优避障,并用智能搜索方法解决了移动机器人在多移动障碍环境中的实时运动规划问题。仿真实例表明了该方法的有效性。     

基于双层模糊逻辑的多机器人路径规划与避碰

针对无通信情况下的多机器人系统在未知动态环境下的路径规划问题,设计了基于双层模糊逻辑的多机器人路径规划与动态避碰系统。方向模糊控制器充分考虑了障碍物的距离信息和目标的角度信息,转化为机器人与障碍物的碰撞可能性,从而输出转向角度实现机器人的动态避障;速度模糊控制器将障碍物的距离信息作为输入,将速度因子作为输出,提高了多机器人路径规划与动态避碰系统的效率和鲁棒性。在Pioneer3-DX机器人实体上验证了该系统的可行性。     

基于Vega的虚拟漫游场景中的路径规划研究

针对当前虚拟漫游系统存在的问题,对虚拟漫游场景中两种运动方式进行分析,将避障路经规划中的可视图法进行适当改进,提出了"左右判断法",简化了原有算法的时间复杂度,并将运用扩展到三维空间中。在手动漫游方式下实现了漫游物体的智能"滑行"避障。给出了Vega中这两种运动方式的实现步骤。算法在虚拟空间会议系统中得到了验证。