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.     

基于改进ND+算法的机器人避障方法设计

针对室内结构化环境下服务机器人避障过程中传统ND+算法生成的决策速度不平滑问题,提出了一种对ND+算法生成的决策方向进行修正,使得机器人决策速度变得平滑的设计方法,该方法能够使得机器人更快速平稳地避开障碍物到达目标点。首先,根据ND+算法获取机器人的初始决策方向,以此方向把地图划分为两部分,获取左右两侧的近距离的障碍物点集,各个障碍物点共同对机器人作用,修正机器人的决策方向;实验结果表明,相比ND+算法,该方法能够提前规避前进方向潜在障碍物,机器人更加平缓地行走于充斥拥挤障碍物的室内环境中到达指定目标点。     

Real-time hierarchical POMDPs for autonomous robot navigation

This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory efficient. It will be referred to in this paper as the Robot Navigation–Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments. As such, it is used for localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP is introduced. The latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been experimentally validated in real dynamic environments.     

A Dynamical Model of Visually-Guided Steering,Obstacle Avoidance,and Route Selection

Using a biologically-inspired model, we show how successful route selection through a cluttered environment can emerge from on-line steering dynamics, without explicit path planning. The model is derived from experiments on human walking performed in the Virtual Environment Navigation Lab (VENLab) at Brown. We find that goals and obstacles behave as attractors and repellors of heading, the direction of locomotion, for an observer moving at a constant speed. The influence of a goal on turning rate increases with its angle from the heading and decreases exponentially with its distance; the influence of an obstacle decreases exponentially with angle and distance. Linearly combining goal and obstacle terms allows us to simulate paths through arbitrarily complex scenes, based on information about obstacles in view near the heading direction and a few meters ahead. We simulated the model on a variety of scene configurations and observed generally efficient routes, and verified this behavior on a mobile robot. Discussion focuses on comparisons between dynamical models and other approaches, including potential field models and explicit path planning. Effective route selection can thus be performed on-line, in simple environments as a consequence of elementary behaviors for steering and obstacle avoidance.     

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.     

Global Navigation in Dynamic Environments Using Case-Based Reasoning

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.     

Autonomous navigation and obstacle avoidance using navigation laws with time-varying deviation functions

In this paper we introduce a new family of navigation functions for robot navigation and obstacle avoidance. The method can be used for both path finding and real-time path planning. Each navigation function is composed of three parts: a proportionality term, a deviation function and a deviation constant. Deviation functions are time-varying functions satisfying certain conditions. These functions and parameters are updated in real-time to avoid collision with obstacles. Our strategy uses polar kinematics equations to model the navigation problem in terms of the range and direction between the robot and the goal. The obstacles are mapped to polar planes, and represented by the range and the direction from the robot or the final goal in polar coordinates. This representation gives a certain weight to the obstacles based on their relative position from the robot and facilitates the design of the navigation law. There exists an infinite number of navigation functions obtained by changing the proportionality constant, the deviation constant or the deviation function. This offers an infinite number of possibilities for the robot's path. Our navigation strategy is illustrated using an extensive simulation where different navigation parameters are used.     

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

This paper proposes a new approach for solving the problem of obstacle avoidance during manipulation tasks performed by redundant manipulators. The developed solution is based on a double neural network that uses Q-learning reinforcement technique. Q-learning has been applied in robotics for attaining obstacle free navigation or computing path planning problems. Most studies solve inverse kinematics and obstacle avoidance problems using variations of the classical Jacobian matrix approach, or by minimizing redundancy resolution of manipulators operating in known environments. Researchers who tried to use neural networks for solving inverse kinematics often dealt with only one obstacle present in the working field. This paper focuses on calculating inverse kinematics and obstacle avoidance for complex unknown environments, with multiple obstacles in the working field. Q-learning is used together with neural networks in order to plan and execute arm movements at each time instant. The algorithm developed for general redundant kinematic link chains has been tested on the particular case of PowerCube manipulator. Before implementing the solution on the real robot, the simulation was integrated in an immersive virtual environment for better movement analysis and safer testing. The study results show that the proposed approach has a good average speed and a satisfying target reaching success rate.     

Set-point Control of Mobile Robot with Obstacle Detection and Avoidance Using Navigation Function - Experimental Verification

This paper presents the results of an experimental verification of mobile robot control algorithm including obstacle detection and avoidance. The controller is based on the navigation potential function that was proposed in work (Urakubo, Nonlinear Dyn. 81(3), 1475–1487 2015). Conducted experiments considered the task of reaching and stabilization of robot in point. The navigation potential agregates information of robot position and orientation but also the repelling potentials of obstacles. The obstacle detection is performed solely with the use of laser scanner. The experiments show that the method can easily handle environments with one or two obstacles even if they instantly hide or show-up due to the scanner range limits. The experiments also indicate that the utilized control method has a good potential for being used in parallel parking task.     

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.     

Line of sight robot navigation toward a moving goal.

In this paper, we consider the problem of robot tracking and navigation toward a moving goal. The goal's maneuvers are not a priori known to the robot. Thus, off-line strategies are not effective. To model the robot and the goal, we use geometric rules combined with kinematics equations expressed in a polar representation. The intent of the strategy is to keep the robot between a reference point, called the observer, and the goal. We prove under certain assumptions that the robot navigating using this strategy reaches the moving goal successfully. In the presence of obstacles, the method is combined with an obstacle avoidance algorithm. The robot then moves in two modes, the navigation mode and the obstacle avoidance mode. Simulation of various scenarios highlights the efficiency of the method and provides an instructive comparison between the paths obtained for different reference points.     

Reinforcement learning-based dynamic obstacle avoidance and integration of path planning

Deep reinforcement learning has the advantage of being able to encode fairly complex behaviors by collecting and learning empirical information. In the current study, we have proposed a framework for reinforcement learning in decentralized collision avoidance where each agent independently makes its decision without communication with others. In an environment exposed to various kinds of dynamic obstacles with irregular movements, mobile robot agents could learn how to avoid obstacles and reach a target point efficiently. Moreover, a path planner was integrated with the reinforcement learning-based obstacle avoidance to solve the problem of not finding a path in a specific situation, thereby imposing path efficiency. The robots were trained about the policy of obstacle avoidance in environments where dynamic characteristics were considered with soft actor critic algorithm. The trained policy was implemented in the robot operating system (ROS), tested in virtual and real environments for the differential drive wheel robot to prove the effectiveness of the proposed method. Videos are available at https://youtu.be/xxzoh1XbAl0.

     

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.     

Trajectory time scaling of a mobile robot to avoid dynamic obstacles on the basis of the INLVO

To ensure the collision safety of mobile robots, the velocity of dynamic obstacles should be considered while planning the robot’s trajectory for high-speed navigation tasks. A planning scheme that computes the collision avoidance trajectory by assuming static obstacles may result in obstacle collisions owing to the relative velocities of dynamic obstacles. This article proposes a trajectory time-scaling scheme that considers the velocities of dynamic obstacles. The proposed inverse nonlinear velocity obstacle (INLVO) is used to compute the nonlinear velocity obstacle based on the known trajectory of the mobile robot. The INLVO can be used to obtain the boundary conditions required to avoid a dynamic obstacle. The simulation results showed that the proposed scheme can deal with typical collision states within a short period of time. The proposed scheme is advantageous because it can be applied to conventional trajectory planning schemes without high computational costs. In addition, the proposed scheme for avoiding dynamic obstacles can be used without an accurate prediction of the obstacle trajectories owing to the fast generation of the time-scaling trajectory.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

On-line Planning for Collision Avoidance on the Nominal Path

In this paper a solution to the obstacle avoidance problem for a mobile robot moving in the two-dimensional Cartesian plane is presented. The robot is modelled as a linear time-invariant dynamic system of finite size enclosed by a circle and the obstacles are modelled as circles travelling along rectilinear trajectories. This work deals with the avoidance problem when the obstacles move in known trajectories. The robot starts its journey on a nominal straight line path with a nominal velocity. When an obstacle is detected to be on a collision course with the robot, the robot must devise a plan to avoid the obstacle whilst minimising a cost index defined as the total sum squared of the magnitudes of the deviations of its velocity from the nominal velocity. The planning strategy adopted here is adjustment of the robot's velocity on the nominal path based on the time of collision between the robot and a moving obstacle, and determination of a desired final state such that its Euclidean distance from the nominal final state is minimal. Obstacle avoidance by deviation from the nominal path in deterministic and random environments is based on the work presented here and is investigated in another paper.     

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的避障效率,避免斥力避障方法导致的死锁问题.其次本文证明了在该控制律下系统的稳定性.仿真结果表明,该算法在有效围捕目标的同时能够高效地避开障碍物,具有对复杂环境的适应性.最后本文分析了与其他方法相比该算法的优点.     

基于改进RBPF算法的轮式机器人SLAM导航系统设计

传统的机器人导航系统在复杂的地形环境中常常无法引导机器人躲避突然出现的障碍物,无法精准采集数据。为此提出一种改进RBPF算法的轮式机器人SLAM导航系统,对系统硬件和软件进行设计。系统硬件主要由导航功能模块、底盘驱动模块、控制模块组成,利用RPLIDAR A1型激光测距雷达设计导航功能模块,并设计底盘驱动模块和控制模块。软件设计中,以改进RBPF算法为基础,设计了轮式机器人SLAM导航系统的实现程序,应用算法代入的方式加强了普通轮式机器人导航算法对粒子计算与卡尔曼滤波的敏感程度。实验结果表明,改进RBPF算法在避障和计算误差方面的优势,证明了该系统相比传统避障后的路径选择更便捷,导航错误出现率降低了30%左右。     

Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot

Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviors to relate each control objective with a desired behavior for the mobile robot. Natural behaviors lead the system to fulfill a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behavior. The adaptive behavior, learned through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behavior of the mobile robot is the addition of the natural and the adaptive behaviors. Our proposed methodology achieves the discovery of 9402 behaviors without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.