Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.

Lyapunov function-based obstacle avoidance scheme for a two-wheeled mobile robot

An obstacle avoidance scheme of a two-wheeled mobile robot is shown by selecting an appropriate Lya- punov function. When considering the obstacle, the Lyapunov function may have some local minima. A method which erases the local minima is proposed by using a function which covers the minima with a plane surface. The effectiveness of the proposed method is verified by numerical simulations.

Reinforcement Learning Based Obstacle Avoidance for Autonomous Underwater Vehicle

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV)in an unknown underwater environment during exploration process.Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties,disturbance,or plant model mismatch.On the other hand,model-free reinforcement learning(RL)algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach.Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment.A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore,function approximation is utilized using neural network(NN)to overcome the continuous states and large statespace problems which arise in RL-based controller design.The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance.Also,the same algorithm is utilized to deal with multiple obstacle avoidance problems.

Collision Avoidance of Cooperative Dual Redundant Manipulators

Dual redundant manipulators are extremely useful for tasks in dangerous or space environments, but efficient and real time coordinated control is hard to achieve. Collision avoidance between two cooperative manipulators is vital to the successful applications of dual redundant manipulators. Although methods based on the distance function have been demonstrated simple and efficient, different collision avoidance points can usually produce completely different results and even failure. The paper discussed the choices of collision avoidance points and proposed a novel method for the choosing of those points. The method is testified by simulation results of two redundant planar manipulators.

Potential-based obstacle avoidance in formation control

Based on the double integrator mathematic model, a new kind of potential function is presented in this paper by referring to the concepts of the electric field; then a new formation control method is proposed, in which the potential functions are used between agent-agent and between agent-obstacle, while state feedback control is applied for the agent and its goal. This strategy makes the whole potential field simpler and helps avoid some local minima. The stability of this combination of potential functions and state feedback control is proven. Some simulations are presented to show the rationality of this control method.

Target Tracking and Obstacle Avoidance for Multi-agent Systems

This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment, a novel control algorithm based on potential function and behavior rules is proposed. Meanwhile, the interactions among agents are also considered. According to the state whether an agent is within the area of its neighbors＇ influence, two kinds of potential functions are presented. Meanwhile, the distributed control input of each agent is determined by relative velocities as well as relative positions among agents, target and obstacle. The maximum linear speed of the agents is also discussed. Finally, simulation studies are given to demonstrate the performance of the proposed algorithm.

Target Tracking and Obstacle Avoidance for Multi-agent Networks with Input Constraints

In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.

Optimal obstacle avoidance via distributed consensus algorithms with communication delay

A consensus problem of multi-agent system subject to the communication delay with fixed and switching topologies as well as optimal obstacle avoidance is investigated. An innovative non-quadratic obstacle avoidance cost function is designed from an inverse optimal control perspective and a novel dynamic potential field whose vectors are rotating around the obstacle is introduced to avoid obstacles. The asymptotic stability condition of dynamical networks based on the Lyapunov-Krasovskii function is obtained via a linear matrix inequality (LMI) formulation. Finally, numerical examples are presented to show the effectiveness of the proposed theoretical results. © 2016 Beijing Institute of Aerospace Information.

基于恐惧-回避模型的康复护理方案在老年慢性心力衰竭患者中的应用

目的减轻老年慢性心力衰竭患者康复运动恐惧心理,促进其运动康复。方法便利抽取70例心内科住院老年慢性心力衰竭患者,按入院时间分为两组各35例;两组均行常规治疗护理,同时对照组行常规康复护理,干预组构建和实施基于恐惧-回避模型的康复运动方案,持续干预至出院后1个月评价效果。结果干预后干预组心功能指标显著优于对照组(均P<0.05),心脏病运动恐惧得分、明尼苏达州心力衰竭生活质量得分显著低于对照组(均P<0.05);运动方案实施过程中未发生运动相关不良事件。结论对老年慢性心力衰竭患者实施基于恐惧-回避模型的康复运动方案能减轻患者运动恐惧心理,改善心功能,提高生活质量。

复杂环境下DWA与RRT算法融合的AUV局部路径规划

针对复杂水下环境下的自主水下航行器(autonomous underwater vehicle,AUV)局部路径规划问题,传统动态窗口法(dynamic window approach,DWA)存在复杂障碍物中陷入局部停滞,动态避障性能不佳等问题,本文提出了一种基于DWA与快速随机搜索树(rapid-exploration random tree,RRT)算法融合的路径规划算法。改进的DWA算法速度空间根据整个动态窗口的周期生成,重设了评价函数并结合AUV任务环境引入洋流能耗评价函数;改进的RRT算法在局部已知空间内规划导引点,帮助DWA脱离局部停滞状态并实现更安全的动态避障。将2种算法融合,实现了AUV在复杂水下环境中的局部路径规划。仿真表明,该融合算法能够降低AUV在洋流中的能耗代价,解决了DWA在复杂障碍物中陷入局部停滞的问题,能够安全有效地躲避动态避障物。

基于V2V的智能车辆换道轨迹规划方法研究

针对传统车辆换道轨迹算法实时性差与换道效率低的问题,提出一种基于V2V的智能车辆动态自主换道轨迹规划算法。换道起始时刻,智能车辆通过V2V通信技术获取周围交通车辆运动状态信息,在换道条件允许的情况下,采用多项式函数对车辆换道过程的横纵向运动进行描述,通过构建基于双圆车体近似模型的避障算法筛选无碰撞的换道轨迹,以车辆换道舒适性、换道效率等换道性能指标构建多目标优化价值函数,通过求解约束化问题求得最优换道轨迹。为验证算法的有效性,利用CarSim和Matlab/Simulink等软件进行联合仿真验证。结果表明:对于不同的换道工况,基于V2V的换道轨迹规划算法比传统非V2V的换道轨迹规划算法所规划出的换道轨迹需要的换道空间更小,具有更高的换道效率。

面向预期功能安全的eVTOL智能避障系统运行时保证方法

为保障电动垂直起降飞行器(eVTOL)在城市空中交通(UAM)场景下的预期功能安全(SOTIF),降低人工智能算法的验证难度,建立基于运行时保证(RTA)方法的避障模型。首先,使用人工势场法改进的柔性动作评价(SAC)算法作为eVTOL智能避障系统的复杂功能;然后,使用动态反应规划(DRP)作为智能航电系统的备用功能,以减缓SOTIF危险,同时,使用监视和决策模块采集环境状态,搭建RTA架构;最后,分别仿真仅使用复杂功能和具有RTA架构的避障系统,对比避障效果。结果表明:2种方法均可实现避障,但仅使用复杂功能的传统避障系统会带来SOTIF危险;经过RTA架构设计,可使eVTOL的安全飞行时长占比由78.4%提高到98.15%,总航路长度仅增加0.95%,在保障效率的同时,降低了在运行场景中的风险。

基于一致性理论和S-MPC的四旋翼编队协同避障

针对四旋翼无人机队形保持、编队避障问题,在双向、时不变通信拓扑结构下,基于领航跟随策略,利用安全攸关模型预测控制(safety-critical model predictive control,S-MPC)和一致性理论,设计编队控制器并实现了具有避障能力的队形保持。采用分散式S-MPC算法,每架无人机在满足避碰条件无人机的可行区域内,仅规划自身运动来跟踪编队控制算法指定的轨迹。研究了各解耦后的无人机如何与其他无人机并行求解带有耦合约束的优化问题,从而保证了各无人机独立决策的一致性。同时,所提算法将控制障碍函数(control barrier function,CBF)引入到MPC控制器的约束中,从而保证无人机飞行在远离障碍物的安全集合内,规划出的轨迹更为平滑,减小了系统能耗。最后,通过仿真实验验证了所提方法的有效性。

无人驾驶中运用DQN进行障碍物分类的避障方法

安全是无人驾驶汽车需要考虑的首要因素,而避障问题是解决驾驶安全最有效的手段。基于学习的避障方法因其能够从环境中学习并直接从感知中做出决策的能力而受到研究者的关注。深度Q网络(DQN)作为一种流行的强化学习方法,在无人驾驶避障领域取得了很大的进展,但这些方法未考虑障碍物类型对避障策略的影响。基于对障碍物的准确分类提出一种Classification Security DQN(CSDQN)的车辆行驶决策框架。根据障碍物的不同类型以及环境信息给出具有更高安全性的无人驾驶决策,达到提高无人驾驶安全性的目的。首先对检测到的障碍物根据障碍物的安全性等级进行分类,然后根据不同类型障碍物提出安全评估函数,利用位置的不确定性和基于距离的安全度量来评估安全性,接着CSDQN决策框架利用障碍物类型、相对位置信息以及安全评估函数进行不断迭代优化获得最终模型。仿真结果表明,与先进的深度强化学习进行比较,在多种障碍物的情况下,采用CSDQN方法相较于DQN和SDQN方法分别提升了43.9%和4.2%的安全性,以及17.8%和3.7%的稳定性。

基于DWA算法的配网作业机器人避障路径规划

配网作业机器人运行环境中存在不同大小的障碍物,作业过程中会出现路径冗余问题,导致配网作业机器人避障能力差,为此提出基于DWA算法的配网作业机器人避障路径规划方法。根据机器人质量、车轮半径和轮间距信息构建机器人运动学模型,描述机器人在不同时刻的位姿;确定机器人作业空间,结合起点与目标点位置构建局部地图,约束机器人运动范围;设计DWA算法的速度向量空间,多采样机器人速度,获取限速区域,在该区间内生成多条待选择的路径;利用方向评价、障碍物距离评价和速度评价3个子函数组成DWA评价函数,分别对子函数做归一化处理,选取评价值最高的路径作为最优避障规划路径。实验结果表明,该方法在简单和复杂的作业环境下均能准确避障,且路径最短。

列车控制系统的抗拒绝服务攻击弹性控制策略

当遭受拒绝服务(DoS)攻击时,分布式列车控制系统的弹性控制问题受到广泛关注.本文提出了一种基于分布式领导车状态观测器和障碍李雅普诺夫函数的弹性控制策略,不仅可以避免列车碰撞,同时实现了编队控制的目标.首先,给出了一种分布式的领导车状态观测器设计方法,用于实时估计领导车的状态.理论分析表明,在DoS攻击满足一定约束的条件下,该状态观测器的估计误差具有指数稳定特性.在此基础上,通过将列车碰撞避免问题转化为状态受限问题,提出一种基于障碍李雅普诺夫函数的状态受限控制律,解决了DoS攻击下确保碰撞避免的车队控制问题.最后,数值仿真证实了本文方法的有效性.

基于安全强化学习的不确定二阶系统模型参考跟踪控制

针对带有不确定性动态的二阶系统模型参考轨迹跟踪控制问题,设计了一种基于鲁棒控制障碍函数的安全强化学习算法。该算法能在学习跟踪控制器的过程中保证系统避障。首先,对具有安全约束的轨迹跟踪控制强化学习问题进行建模。其次,利用高斯过程模型对未知系统动力学的估计,得到鲁棒的避障控制障碍函数条件,在该条件下得到概率意义下的系统安全性保证。最后,提出基于鲁棒控制障碍函数的安全强化学习算法,得到最优模型参考轨迹跟踪控制律。仿真结果验证了所提算法的有效性。

基于多策略改进蚁群算法的机器人路径规划

针对传统蚁群算法初期容易盲目搜索,收敛速度慢,全局性不强,搜索路径安全性不足等问题,提出了一种改进蚁群算法。通过全局变量改进启发函数,使算法不容易陷入局部最优;用椭圆几何法构造数学模型,来重新分配初始信息素,使算法初期不会盲目搜索;提出自适应有界的信息素迭代方式,增强收敛速度。除距离最优,时间最优外,提出安全最优的邻接矩阵改造方法,避免机器人移动中的磕碰现象。仿真结果表明,改进后算法收敛速度提高,迭代次数减少,初期搜索效率加强,规划路径避开所有障碍物顶点,验证了算法的有效性,优越性和安全性。

基于多目标函数的避洪转移安置方案优化

针对目前避洪转移方案仅考虑时间或距离成本最优导致部分安置点人数分配不合理的问题,从“安全第一、就近就便、平灾结合、利于管理”的避洪转移安置方案基本要求出发,首先对安置点的适用性、分布合理性及容量大小3个方面进行分析,依据“时间、距离、适宜度和安置容量”四因素初步筛选合适的安置点。然后通过路权函数建立避洪转移时间成本矩阵,以转移时间成本和安置点的优化分配为多目标函数确定避洪转移安置点的最优分配方案。最后以TOPSIS评价方法对避洪转移安置点分配的单目标方案与多目标方案进行对比分析。结果表明:多目标方案的相对接近度大于单目标方案,基于多目标函数确定的避洪转移安置分配方案更加合理。