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.     

一种新的位置数据融合方法的研究

给出了一新的基于团队一致法的多传感器位置数据融合方法，该方法按传感器队中的每个成员的测量不确定性，构造团队期望效用函数（或密度），并基于该期望效用函数求得位置参数估计，其优点是可消除失效传感器和测量值为野值的传感器的影响，本文给出了仿真结果。     

Mobile Robot Navigation for Moving Obstacles with Unpredictable Direction Changes,Including Humans

In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.     

Multiobjective Navigation of a Guide Mobile Robot for the Visually Impaired Based on Intention Inference of Obstacles

Different from ordinary mobile robots used in a well-structured industrial workspace, a guide mobile robot for the visually impaired should be designed in consideration of multiple moving obstacles of various types and with different speeds while it adaptively maintains a certain distance from the user. Here, the moving obstacles mostly refer to pedestrians in intentional motions. Thus, navigation of the guide robot can be facilitated if the intention of each obstacle detected can be known in advance.In the paper, we propose to use a fuzzy grid-type local map in order to infer the intention of a moving obstacle. And, then, we determine the motion control of the robot by adopting a multiobjective decision making method in order to take into consideration various requirements including goal-seeking, multiple obstacle avoidance and maintenance of a certain distance from the user. To show the effectiveness of the proposed method, some experimental results are provided.     

Navigation integration of a mobile robot in dynamic environments

This article presents a design and experimental study of navigation integration of an intelligent mobile robot in dynamic environments. The proposed integration architecture is based on the virtual‐force concept, by which each navigation resource is assumed to exert a virtual force on the robot. The resultant force determines how the robot will move. Reactive behavior and proactive planning can both be handled in a simple and uniform manner using the proposed integration method. A real‐time motion predictor is employed to enable the mobile robot to deal in advance with moving obstacles. A grid map is maintained using on‐line sensory data for global path planning, and a bidirectional algorithm is proposed for planning the shortest path for the robot by using updated grid‐map information. Therefore, the mobile robot has the capacity to both learn and adapt to variations. To implement the whole navigation system efficiently, a blackboard model is used to coordinate the computation on board the vehicle. Simulation and experimental results are presented to verify the proposed design and demonstrate smooth navigation behavior of the intelligent mobile robot in dynamic environments. ©1999 John Wiley & Sons, Inc.     

Aimy: An autonomous mobile robot navigation in unknown environment with infrared detector system

Aimy — an Autonomous Mobile Robot (AMR), capable of moving in an unknown environment filled with obstacles, has been developed. To avoid collision with unexpected obstacles, an Infrared Detector System (IDS) for providing multiple reading data was designed and implemented. A navigation/obstacle avoidance strategy for a mobile robot, which is based on the use of infrared detector data only, is discussed. Experiment results are also presented which exhibit the power of the developed algorithm and Infrared Detector System.     

非结构环境下移动机器人的运动规划

在给出了非结构环境下移动障碍物优先级定义的前提下,提出了移动机器人在含有多个障碍物且障碍物运动具有不确定性的情况下的实时避障运动规划策略.同时给出了障碍物的运动具有不确定性时预测k个采样周期后确定障碍物位置的表达式.仿真结果表明此避障策略是行之有效的,且具有很强的适用性和实际应用价 值.     

A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer

A mobile robot should be designed to navigate with collision avoidance capability in the real world, flexibly coping with the changing environment. In this paper, a novel limit-cycle navigation method is proposed for a fast mobile robot using the limit-cycle characteristics of a 2nd-order nonlinear function. It can be applied to the robot operating in a dynamically changing environment, such as in a robot soccer system. By adjusting the radius of the motion circle and the direction of obstacle avoidance, the navigation method proposed enables a robot to maneuver smoothly towards any desired destination. Simulations and real experiments using a robot soccer system demonstrate the merits and practical applicability of the proposed method.     

在动态环境中移动机器人导航和避碰的一种新方法

本文提出了基于超声传感器的信息,将改进的栅格 和回归预测法结合起来,应用于具有静态和动态障碍物的动态环境中,移动机器人THMR-Ⅱ 的导航和避碰的一种新方法．对栅格法的改进就是以障碍物为单位记录信息量,结果比原来 以栅格为单位记录的信息量少得多,克服了栅格法中存在环境信息存储量大的问题,提高了 实时性．对回归预测法也作了改进,并把它们结合起来,在求得最佳候选扇区后,使移动机 器人躲避了静态和动态障碍物,实现了导航,最终到达目标．通过三种仿真实验,结果表明 作者提出的方法是正确和有效的．     

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera^® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

基于实时障碍物预测的机器人运动规划

本文给出了移动机器人的虚力导航和运动规划系统．这种方法结合最小方差估计算 法（L M S E）能有效地对机器人进行实时导航和避撞．在预测过程中,根据导航的不同阶 段和预测误差的变化情况,采用Fuzzy规则动态地调整误差函数中的权重,使预测过程尽可 能准确．导航算法的基本思想是首先通过预测算法来获得移动机器人的运动信息,然后虚力 系统根据预测信息决定机器人的未来运动,仿真结果表明该方法实时性好,能准确躲避障碍 物并且到达目标点．     

Rollover-free navigation for a mobile agent in an unstructured environment.

This paper introduces a navigation method for a teleoperated mobile agent (or robot) moving in an unstructured environment that includes unknown obstacles and uneven terrain, based on a guided-navigation algorithm (GNA) and a rollover-prevention algorithm (RPA). Although the mobile agent is primarily driven by an operator at a remote site, it reacts autonomously for avoiding collision with obstacles and for preventing rollover when it suspects/detects possible collision or rollover. The autonomous reactive motion is normally unexpected, thus there exists the inconsistency between the intended motion and the controlled motion of the agent from the operator. A force-reflection technique utilizing a force-feedback joystick is developed to manipulate this inconsistency. To verify the feasibility and effectiveness of the proposed navigation method, experiments with the Robot for Hazardous Application-Double Tracks (ROBHAZ-DT) (actual mobile agent) are successfully carried out.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder

This paper presents a stochastic map building method for mobile robot using a 2-D laser range finder. Unlike other methods that are based on a set of geometric primitives, the presented method builds a map with a set of obstacle regions. In building a map of the environment, the presented algorithm represents the obstacles with a number of stochastic obstacle regions, each of which is characterized by its own stochastic parameters such as mean and covariance. Whereas the geometric primitives based map sometimes does not fit well to sensor data, the presented method reliably represents various types of obstacles including those of irregular walls and sets of tiny objects. Their shapes and features are easily extracted from the stochastic parameters of their obstacle regions, and are used to develop reliable navigation and obstacle avoidance algorithms. The algorithm updates the world map in real time by detecting the changes of each obstacle region. Consequently, it is adequate for modeling the quasi-static environment, which includes occasional changes in positions of the obstacles rather than constant dynamic moves of the obstacles. The presented map building method has successfully been implemented and tested on the ARES-II mobile robot system equipped with a LADAR 2D-laser range finder.     

Evolutionary programming-based univector field navigation methodfor past mobile robots

Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocity. To solve these problems this paper proposes a novel univector field method for fast mobile robot navigation which introduces a normalized two dimensional vector field. The method provides fast moving robots with the desired posture at the target position and obstacle avoidance. To obtain the sub-optimal vector field, a function approximator is used and trained by evolutionary programming. Two kinds of vector fields are trained, one for the final posture acquisition and the other for obstacle avoidance. Computer simulations and real experiments are carried out for a fast moving mobile robot to demonstrate the effectiveness of the proposed scheme.     

动态未知环境下一种新的机器人路径规划方法

针对机器人动态路径规划问题,提出了一种机器人在复杂动态环境中实时路径规划方法.该方法基于滚动窗口的路径规划和避障策略,通过设定可视点子目标、绕行障碍物和对动态障碍物的分析预测,实现机器人在复杂动态环境下的路径规划.针对障碍物分布情况,合理设计可视点法和绕行算法之间转换,有效地解决了局部路径规划的死循环与极小值问题.该方...     

Navigation strategy of multiple mobile robot systems based on the null-space projection method

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.     

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.     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.