碰撞锥检测改进的多智能体避障算法

避障是多智能体能够适应复杂环境并顺利完成任务的必要条件之一。为使多智能体更快通过障碍物并达到一致,提出了一种多智能体避障控制算法。算法引入了避障系数,该系数由基于角度比较的碰撞锥检测方法来确定,并通过牵制控制输入完成多智能体的避障。证明了在该算法作用下所有智能体最终会避开障碍,避免碰撞并最终达到一致。通过实验仿真分析和对比,该方法能够使得多智能体更快避开障碍物。     

基于模糊人工势场法的多智能体编队控制及避障方法

针对动态环境中多智能体编队控制及避障问题,提出了一种基于模糊人工势场法的编队方法。首先,在领航跟随法的框架下控制编队队形,在动态队形变换策略的异构模式下,使用人工势场法为多智能体编队中每个智能体规划避障路径;其次,利用模糊控制器控制跟随智能体追踪领航智能体,同时保持跟随智能体之间与领航智能体的相对距离,遇到未知障碍物时,及时保持多智能体编队之间的队形并避免碰撞障碍物。针对人工势场法在引力增量系数和斥力增量系数设置的局限性,利用模糊控制器选择出适应环境的增量系数。Matlab仿真实验结果表明,该方法能够有效地解决复杂环境下多智能体编队控制及避障问题,使用效率函数对实验数据进行分析,验证了所优化方法的合理性和有效性。     

固定翼无人机在线航迹规划方法

在不确定环境下,针对固定翼无人机（UAV）航迹规划问题,提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先,对凸多边形障碍物进行最小外接圆拟合;然后,根据静态威胁,将规划问题转化为一系列时域窗口内的在线子问题,利用模糊粒子群算法实时优化求解以实现静态避障;当环境中存在动态威胁时,使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束,同时提出固定翼UAV的碰撞检测法,可提前判断障碍物是否为真正威胁源,以此减少转弯频率和幅度,降低飞行代价。仿真实验结果表明,所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力,且克服了传统人工势场容易陷入局部最优的缺点。     

Three‐dimensional map building for mobile robot navigation environments using a self‐organizing neural network

In recent years, mobile robots have been required to become more and more autonomous in such a way that they are able to sense and recognize the three‐dimensional space in which they live or work. In this paper, we deal with such an environment map building problem from three‐dimensional sensing data for mobile robot navigation. In particular, the problem to be dealt with is how to extract and model obstacles which are not represented on the map but exist in the real environment, so that the map can be newly updated using the modeled obstacle information. To achieve this, we propose a three‐dimensional map building method, which is based on a self‐organizing neural network technique called “growing neural gas network.” Using the obstacle data acquired from the 3D data acquisition process of an active laser range finder, learning of the neural network is performed to generate a graphical structure that reflects the topology of the input space. For evaluation of the proposed method, a series of simulations and experiments are performed to build 3D maps of some given environments surrounding the robot. The usefulness and robustness of the proposed method are investigated and discussed in detail. © 2004 Wiley Periodicals, Inc.     

A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

A dynamic motion primitive (DMP) is a robust framework that generates obstacle avoidance trajectories by introducing perturbative terms. The perturbative term is usually constructed with an artificial potential field (APF) method. Dynamic obstacle avoidance is rarely considered with this approach; furthermore, even when dynamic obstacles are considered, only the velocity and position information of the current state are incorporated into the obstacle avoidance framework. However, if the position of an obstacle changes suddenly, a robot may be placed in a dangerous position close to the obstacle, resulting in large obstacle avoidance accelerations, sharp trajectories, or even obstacle avoidance failure. Therefore, we present a model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter. This method has three main components: Dynamic motion primitives are used to generate the desired trajectory and introduce perturbations to achieve obstacle avoidance; the Kalman filter method is adopted to estimate the future positions of the obstacles; and model predictive control is employed to optimize the repulsive force generated by the APF while minimizing the defined cost function, thus guaranteeing the safety and flexibility of the method. We validate the presented method with 2D and 3D obstacle avoidance simulations. The method is also verified with a real robot: the-Kinova MOVO. The simulation and experimental results show that the proposed method not only avoids dynamic obstacles but also tracks the desired trajectory more smoothly and precisely.     

A local obstacle avoidance method for mobile robots in partially known environment

Local obstacle avoidance is a principle capability for mobile robots in unknown or partially known environment. A series of velocity space methods including the curvature velocity method (CVM), the lane curvature method (LCM) and the beam curvature method (BCM) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space by taking the physical constraints of the environment and the dynamics of the vehicle into account. We present a new local obstacle avoidance approach that combines the prediction model of collision with the improved BCM. Not only does this method inherit the quickness of BCM and the safety of LCM, but also the proposed prediction based BCM (PBCM) can be used to avoid moving obstacles in dynamic environments.     

感知范围受限的群机器人自主围捕算法

针对在有障碍物场地中感知范围受限的群机器人协同围捕问题,本文首先给出了机器人个体、障碍物、目标的模型,并用数学形式对围捕任务进行描述,在此基础上提出了机器人个体基于简化虚拟速度和基于航向避障的自主围捕控制律.基于简化虚拟速度模型的控制律使得机器人能自主地围捕目标同时保持与同伴的距离避免互撞;基于航向的避障方法提升了个体...     

A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system.     

An analytically tractable potential field model of free space andits application in obstacle avoidance

An analytically tractable potential field model of free space is presented. The model assumes that the border of every two dimensional (2D) region is uniformly charged. It is shown that the potential and the resulting repulsion (force and torque) between polygonal regions can he calculated in closed form. By using the Newtonian potential function, collision avoidance between object and obstacle thus modeled is guaranteed in a path planning problem. A local planner is developed for finding object paths going through narrow areas of free space where the obstacle avoidance is most important. Simulation results show that not only does individual object configuration of a path obtained with the proposed approach avoid obstacles effectively, the configurations also connect smoothly into a path.     

未知环境下基于虚拟角速度跟踪的避障策略EI北大核心CSCD

当智能体自主执行任务时,局部障碍物可测的未知环境增加了局部极值和执行器饱和发生的概率.对此,本文提出了虚拟角速度跟踪的避障策略.首先,基于简易障碍物的几何模型构造虚拟的避障引导角,并利用李雅普诺夫方法设计角速度控制律,通过受限制的虚拟角速度跟踪来实现避障控制.然后,引入方位因子改进距离型权值分配器,强化轨迹附近障碍物的影响以降低局部极值发生的概率.最后,对于不完全可测的复杂障碍物,根据历史探测信息建立以边界点为中心的简易障碍物模型.仿真结果表明,该策略能够避让低速动态障碍物及U型复杂障碍物,并且可实现抗饱和控制.     

一种用于群体模拟的分层次避障法

个体避障是实现基于主体的(agent-based)群体模拟中一个很重要的问题,为了实现个体间以及个体和环境间的碰撞避免并杜绝穿透,人们提出了大量避障方法.但是,这些方法面临的挑战在于:如何杜绝穿透现象并最大程度地减少由于避障需求而带来的个体行为模拟上的空间限制和失真.针对这一问题,提出了一种分层次避障方法,从静态避障、动态避障、穿透矫正3个不同的层次对避障进行处理.静态避障层和动态避障层通过对物体的划分和分别避障,极大地减少了各层次避障时需要考虑的各种复杂情形;而基于可变包围盒和原位置的穿透矫正层则有效地杜绝了模拟中出现的穿透现象,也消除了现有模拟中由于避免穿透而引入的空间限制和失真.     

Learning Dynamic Obstacle Avoidance for a Robot Arm Using Neuroevolution

Neural networks can be evolved to control robot manipulators in tasks like target tracking and obstacle avoidance in complex environments. Neurocontrollers are robust to noise and can be adapted to different environments and robot configurations. In this paper, neurocontrollers were evolved to position the end effector of a robot arm close to a target in three different environments: environments without obstacles, environments with stationary obstacles, and environments with moving obstacles. The evolved neurocontrollers perform qualitatively like inverse kinematic controllers in environments with no obstacles and like path-planning controllers based on Rapidly-exploring random trees in environments with obstacles. Unlike inverse kinematic controllers and path planners, the approach reliably generalizes to environments with moving obstacles, making it possible to use it in natural environments.     

New technique of mobile robot navigation using a hybrid adaptive fuzzy potential field approach

This paper presents a summary of the research aimed at developing a new reliable methodology for robot navigation and obstacle avoidance. This new approach is based on the artificial potential field (APF) method, which is used extensively for obstacle avoidance. The classical APF is dependent only on the separation distance between the robot and the surrounding obstacles. The new scheme introduces a variable, which is used to determine the importance that each obstacle has on the robot's future path. The importance variable is dependent on the obstacles position, both angle and distance, with respect to the robot. Simulation results are presented demonstrating the ability of the algorithm to perform successfully in simple environments.     

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.     

Collision‐free control of robotic manipulators in the task space

This paper addresses the problem of position control of robotic manipulators in the task space with obstacles. A computationally simple class of task space regulators consisting of a transpose Jacobian controller plus an integral term including the task error and the gradient of a penalty function generated by obstacles is proposed. The Lyapunov stability theory is used to derive the control scheme. Through the use of the exterior penalty function approach, collision avoidance of the robot with obstacles is ensured. The performance of the proposed control strategy is illustrated through computer simulations for a direct‐drive arm of a SCARA type manipulator operating in both an obstacle‐free task space and a task space including obstacles. © 2005 Wiley Periodicals, Inc.     

基于群集行为的分布式多无人机编队动态避障控制

针对无人机编队保持和动态障碍物规避控制问题,本文提出了一种新的基于群集行为的分布式多无人机编队控制和避障控制算法.首先考虑了由机间气流等因素带来的干扰,基于吸引/排斥势场和一致性方法,设计了分布式无人机编队的队形保持控制算法,对编队内无人机之间的距离进行控制.进一步考虑外部移动障碍对无人机编队的影响,引入了排斥势场产生...     

考虑障碍环境下的多机器人编队控制研究

编队和避障控制是机器人路径规划设计中的典型问题,文中提出了将leader—following法和人工势场法相结合的方法,来更好地完成多机器人在未知环境下的编队和避障控制。之前的研究只将leader—following算法用于多机器人的编队控制,而文中提出此方法也可以用于多机器人系统的避障控制。基于leader—following法,多机器人能自动编队并保持队形;而结合人工势场法,多机器人可以保持队形行进,在遇到障碍物的情况下变换队形避障,在避障后恢复原队形,最终到达目标。通过仿真实验证明,该算法实现了多机器人在未知环境下的自动编队和避障,从而证明了leader—following算法可以用于机器人的避障控制。     

Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning

This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.     

基于分层深度强化学习的移动机器人导航方法

针对现有基于深度强化学习(deep reinforcement learning, DRL)的分层导航方法在包含长廊、死角等结构的复杂环境下导航效果不佳的问题,提出一种基于option-based分层深度强化学习(hierarchical deep reinforcement learning, HDRL)的移动机器人导航方法.该方法的模型框架分为高层和低层两部分,其中低层的避障和目标驱动控制模型分别实现避障和目标接近两种行为策略,高层的行为选择模型可自动学习稳定、可靠的行为选择策略,从而有效避免对人为设计调控规则的依赖.此外,所提出方法通过对避障控制模型进行优化训练,使学习到的避障策略更加适用于复杂环境下的导航任务.在与现有DRL方法的对比实验中,所提出方法在全部仿真测试环境中均取得最高的导航成功率,同时在其他指标上也具有整体优势,表明所提出方法可有效解决复杂环境下导航效果不佳的问题,且具有较强的泛化能力.此外,真实环境下的测试进一步验证了所提出方法的潜在应用价值.