Reactive navigation of multiple moving agents by collaborative resolution of conflicts

In navigation that involves several moving agents or robots that are not in possession of each other's plans, a scheme for resolution of collision conflicts between them becomes mandatory. A resolution scheme is proposed in this paper specifically for the case where it is not feasible to have a priori the plans and locations of all other robots, robots can broadcast information between one another only within a specified communication distance, and a robot is restricted in its ability to react to collision conflicts that occur outside of a specified time interval called the reaction time interval. Collision conflicts are resolved through velocity control by a search operation in the robot's velocity space. The existence of a cooperative phase in conflict resolution is indicated by a failure of the search operation to find velocities in the individual velocity space of the respective robots involved in the conflict. A scheme for cooperative resolution of conflicts is modeled as a search in the joint velocity space of the robots involved in conflict when the search in the individual space yields a failure. The scheme for cooperative resolution may further involve modifying the states of robots not involved in any conflict. This phenomenon is characterized as the propagation phase where cooperation spreads to robots not directly involved in the conflict. Apart from presenting the methodology for the resolution of conflicts at various levels (individual, cooperative, and propagation), the paper also formally establishes the existence of the cooperative phase during real‐time navigation of multiple mobile robots. The effect of varying robot parameters on the cooperative phase is presented and the increase in requirement for cooperation with the scaling up of the number of robots in a system is also illustrated. Simulation results involving several mobile robots are presented to indicate the efficacy of the proposed strategy. © 2005 Wiley Periodicals, Inc.     

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。     

动态环境下分布式异构多机器人避障方法研究

多机器人系统在联合搜救、智慧车间、智能交通等领域得到了日益广泛的应用。目前,多个机器人之间、机器人与动态环境之间的路径规划和导航避障仍需依赖精确的环境地图,给多机器人系统在非结构环境下的协调与协作带来了挑战。针对上述问题,本文提出了不依赖精确地图的分布式异构多机器人导航避障方法,建立了基于深度强化学习的多特征策略梯度优化算法,并考虑了人机协同环境下的社会范式,使分布式机器人能够通过与环境的试错交互,学习最优的导航避障策略;并在Gazebo仿真环境下进行了最优策略的训练学习,同时将模型移植到多个异构实体机器人上,将机器人控制信号解码,进行真实环境测试。实验结果表明:本文提出的多特征策略梯度优化算法能够通过自学习获得最优的导航避障策略,为分布式异构多机器人在动态环境下的应用提供了一种技术参考。     

Hybrid Potential Field Based Control of Differential Drive Mobile Robots

This paper suggests a new way for nonholonomic mobile robots to navigate in obstacle environments using potential fields based on navigation functions. The proposed strategy is a time-invariant feedback control design with the distinguishing feature that it requires almost no switching compared to alternative methodologies of the same nature. Asymptotic convergence with collision avoidance for the proposed approach is established analytically, and the method is demonstrated on a differential-drive skid steering mobile robot.     

Distance-based Formation Control Using Angular Information Between Robots

Distance-based formation of groups of mobile robots provides an alternative focus for motion coordination strategies respect to the standard consensus-based formation strategies. However, the setup formulation introduces non rigidity problems, multiple formation patterns that verify the distance constraints or local minima appeared when collision avoidance strategies are added to the control laws. This paper proposes a novel combined distance-based potential functions with attractive-repulsive behavior in order to simplify the navigation problem as well as the use of angular information between robots to reduce the likelihood of unwanted formation patterns. Moreover, this approach eliminates the local minima generated by the control laws to reach the desired formation configuration in the case of three robots. The analysis addresses the case of omnidirectional robots and is extended to the case of unicycle-type robots with numerical simulations and real-time experiments.     

Practical Formation Control of Multiple Unicycle-Type Mobile Robots with Limited Sensing Ranges

This paper presents a design of cooperative controllers that force a group of N unicycle-type mobile robots with limited sensing ranges to perform a desired tight formation and that guarantee no collisions between any robots in the group. The desired formation can be stabilized at any reference trajectories with bounded time derivatives. The formation control design is based on several nonlinear coordinate changes, the transverse function approach, the backstepping technique, the Lyapunov direct method, and smooth or p −times differentiable step functions. These functions are introduced and incorporated into novel potential functions to solve the collision avoidance problem without the need of switchings despite of the robots’ limited sensing ranges. The proposed formation control system is applied to solve a gradient climbing problem.     

基于多智能体深度强化学习的协作导航应用

多机器人协作导航目前广泛应用于搜索救援、物流等领域, 协作策略与目标导航是多机器人协作导航面临的主要挑战. 为提高多个移动机器人在未知环境下的协作导航能力, 本文提出了一种新的分层控制协作导航(hierarchical control cooperative navigation, HCCN) 策略, 利用高层目标决策层和低层目标导航层, 为每个机器人分配一个目标点, 并通过全局路径规划和局部路径规划算法, 引导智能体无碰撞地到达分配的目标点. 通过Gazebo平台进行实验验证, 结果表明, 文中所提方法能够有效解决协作导航过程中的稀疏奖励问题, 训练速度至少可提高16.6%, 在不同环境场景下具有更好的鲁棒性, 以期为进一步研究多机器人协作导航提供理论指导, 应用至更多的真实场景中.     

基于输出反馈线性化的多移动机器人目标包围控制

针对受非完整约束的多移动机器人系统的移动目标包围控制问题, 提出一种基于输出反馈线性化的局部协同控制方法. 利用机器人与邻居节点和目标的相对距离信息、角度信息以及机器人自身的方位角信息设计协同控制器. 该方法无需事先指定包围编队形状, 可实现对移动目标的速度估计, 且保证机器人之间的障碍规避. 严格的理论分析证明了移动目标指数收敛到多移动机器人构成的凸包内部. 最后, 仿真结果验证了所提控制方法的有效性.     

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.     

Cooperative formation control of autonomous underwater vehicles: An overview

Formation control is a cooperative control concept in which multiple autonomous underwater mobile robots are deployed for a group motion and/or control mission. This paper presents a brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles (AUV) based on literature reported till date. Various cooperative and formation control schemes for collecting huge amount of data based on formation regulation control and formation tracking control are discussed. To address the challenge of detecting AUV failure in the fleet, communication issues, collision and obstacle avoidance are also taken into attention. Stability analysis of the feasible formation is also presented. This paper may be intended to serve as a convenient reference for the further research on formation control of multiple underwater mobile robots.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

The SDRE control of mobile base cooperative manipulators: Collision free path planning and moving obstacle avoidance

This work proposes application of a state-dependent Riccati equation (SDRE) controller for wheeled mobile cooperative manipulators. Implementation of the SDRE on a wheeled mobile manipulator (WMM) considering holonomic and non-holonomic constraints is difficult and leads to instability of the system. The present study introduces a method of controlling the WMMs including: a general formulation, state-dependent coefficient parameterization, and control structure of the SDRE. Overcoming the problem of instability of the WMM resulted in control design for a system of cooperative manipulators mounted on a wheeled mobile platform. Optimal load distribution (OLD) was employed to distribute the load between the cooperative arms. The presence of obstacles and the probability of a collision between multiple robots in a workspace are the motivations behind employment of the artificial potential field (APF) approach. Two cooperative manipulators mounted on a mobile platform retrieved from Scout robot were modeled and simulated for situations such as controlling multiple mobile bases (collision avoidance), a cooperative system of manipulators, and moving obstacle avoidance. The OLD improved the load capacity, precision, and stability in motion of the cooperative system. Compatibility of the APF within the structure of the SDRE controller is another promising aspect of this research.     

动态环境下基于速度空间寻优的局部避障方法

在动态环境下的局部避障是移动机器人的一项基本功能.在各种速度空间方法,如曲率-速率法(CVM)、巷道-曲率法(LCM)和扇区-曲率法(BCM)的基础上,提出了一种适用于未知或部分未知动态环境的局部避障方法.该方法将碰撞预测模型与改进后的BCM有效结合,不仅兼备了CVM的平滑性、LCM的安全性和BCM快速性的优点,而且弥补了各种速度空间寻优方法的不足,使其能够适用于移动机器人在动态环境下的避障与导航.实际机器人的导航实验表明该算法是可行而有效的.     

Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments

This paper presents a novel reactive collision avoidance method for mobile robots moving in dense and cluttered environments. The proposed method, entitled Tangential Gap flow (TGF), simplifies the navigation problem using a divide and conquer strategy inspired by the well-known Nearness-Diagram Navigation (ND) techniques. At each control cycle, the TGF extracts free openings surrounding the robot and identifies the suitable heading which makes the best progress towards the goal. This heading is then adjusted to avoid the risk of collision with nearby obstacles based on two concepts namely, tangential and gap flow navigation. The tangential navigation steers the robot parallel to the boundary of the closest obstacle while still emphasizing the progress towards the goal. The gap flow navigation safely and smoothly drives the robot towards the free area in between obstacles that lead to the target. The resultant trajectory is faster, shorter and less-oscillatory when compared to the ND methods. Furthermore, identifying the avoidance maneuver is extended to consider all nearby obstacle points and generate an avoidance rule applicable for all obstacle configurations. Consequently, a smoother yet much more stable behavior is achieved. The stability of the motion controller, that guides the robot towards the desired goal, is proved in the Lyapunov sense. Experimental results including a performance evaluation in very dense and complex environments demonstrate the power of the proposed approach. Additionally, a discussion and comparison with existing Nearness-Diagram Navigation variants is presented.     

基于预测窗的轮式移动机器人最优避障避碰算法

针对非线性轮式移动机器人的避障以及多机器人间的相互避碰问题,提出了一种基于预测窗的避障避碰算法.首先为了便于预测碰撞的发生,通过反馈线性化将非线性的机器人运动学模型转化成线性模型;然后根据线性模型预测会导致机器人发生碰撞的所有相对虚拟加速度变化量集合,称之为加速度变化障碍.基于此,为每个机器人构造既能躲避障碍物又能相互避碰的可行加速度变化集合.然后通过优化指标函数求得最优虚拟加速度变化量,最后将其转换成机器人的实际控制量.这种算法与现有的相比,可使机器人在避障或避碰过程中的行驶方向角、线速度的变化幅值更小,角速度和线加速度的变化更为平顺,而且运行所用的平均时间更短.仿真结果演示了所提出算法的有效性和相对于已有方法的优势.     

The stable and precise motion control for multiple mobile robots

Intelligent path planning of multiple mobile robots has been addressed in this paper. Cooperative behaviour can be achieved using several mobile robots, which require online inter-communication among themselves. In the present investigation rule-based and rule-based-neuro-fuzzy techniques are analyzed for multiple mobile robots navigation in an unknown or partially known environment. The final aims of the robots are to reach some pre-defined goals. Based upon a reference motion, direction; distances between the robots and obstacles; distances between the robots and targets; different types of rules are taken heuristically and refined later to find the steering angle. The control system combines a repelling influence related to the distance between robots and nearby obstacles and with an attracting influence between the robots and targets. Then a hybrid rule-based-neuro-fuzzy technique is analyzed to find the steering angle of the robots. Results show that the proposed rule-based-neuro-fuzzy technique can improve navigation performance in complex and unknown environments compared to this simple rule-based technique.     

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.     

基于几何法的移动机器人路径规划

旨在解决动态环境中移动机器人与障碍物发生碰撞可能性的判断和避开障碍的路径规划。提出了采用几何计算的方法判断机器人和障碍物之间发生碰撞的条件,规划出机器人沿着收敛曲线运动到安全圆周,在安全圆周上作动态圆周运动,最后沿着圆弧退出圆周到达预定的避障路径。将基本的避开障碍的理论和几何算法有机地结合起来,获得了光滑的路径,提高了机器人避开障碍的效率。     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.     

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.