基于多传感器的移动机器人行为控制研究
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摘要
随着机器人应用范围的不断拓宽,机器人的工作环境也越来越复杂,而且往往是未知的、动态的、非结构化的。在这种环境下实时地完成各种任务,对机器人的行为控制提出了新的挑战。通过全面了解和分析国内外移动机器人的控制体系结构和导航技术的研究现状,针对基于车载视觉导航技术中存在的从固定视角获得丰富环境视觉信息能力与获取和维护大范围动态环境模型能力差等问题,提出了基于分布式全局视觉和主动视觉(局部视觉)与超声、红外等多传感器相结合的移动机器人行为控制方法。以固定区域非结构化动态环境为基础,构建了一个移动机器人自主导航控制实验系统。以其为平台研究基于目标任务的移动机器人行为控制中的环境感知、行为决策、运动控制、分布式视觉智能体的任务分解与协调控制等方面的相关策略和实现方法。本文从事的主要研究工作内容如下:
设计并实现了一套基于多传感器的移动机器人自主导航控制实验系统ANCES。该系统主要由移动机器人(RIRA-II ROBOT)、分布式视觉系统和无线通信系统组成。可应用于移动机器人行为控制、主动视觉(局部视觉)导航和分布式全局视觉导航等其它相关技术的研究。
对移动机器人的体系结构进行了研究,提出一种基于模块化并行通信的多微处理器分布式控制体系结构并应用于RIRA-II ROBOT硬件系统设计。各模块采用独立微处理器完成控制及信息处理功能,实现了多模块并行运行;模块之间采用的并行通信方式提高了信息传输速度,同时简化了系统的软件设计。RIRA-II ROBOT软件采用慎思/反应混合式分层控制体系结构,自上而下分为人机交互层、任务规划层和行为控制层三个层次。
研制了一种基于多微处理器的分布式超声探测系统。该探测系统由传感器工作模式控制模块和智能超声测距模块阵列组成。超声测距模块由独立的微处理器控制一个超声传感器,完成测距信息预处理,并可根据不同的控制策略实现分组并行工作,提高了探测系统的实时性;智能测距模块采用“阈值比较法”和“改进型递推平均滤波法”处理测距数据,有效地抑制了探测系统中超声波随机干扰信号及串扰信号,提高了探测系统的准确性。
对基于多传感器(视觉、超声、红外)信息的移动机器人行为控制策略进行了研究,采用基于优先级行为决策控制策略,并利用视觉信息使机器人能够完成面向目标的任务。RIRA-II ROBOT包含了三个行为模块:“趋向目标行为”、“模糊控制行为”和“解死锁行为”,每一行为都具有特定的优先级。行为仲裁器根据并发行为的优先级确定竞争获胜行为,由获胜行为控制移动机器人动作。
对分布式视觉系统的组成及工作原理进行了研究。利用图像拼接技术获取移动机器人工作环境的全局场景图像;利用四叉树对环境建模,根据路径最短约束条件完成路径搜索,运用三次样条曲线拟合法对搜索路径进行处理,生成从起点到终点的无碰撞全局路径;对全局路径进行任务分解,将分解后的路径分配到各相关视觉智能体,为视觉智能体对移动机器人进行控制提供任务依据。视觉智能体通过任务协调机制完成对移动机器人全局导航控制。
对移动机器人全局导航控制中动态避障策略进行了研究。将出现在由分布式视觉系统规划完成的路径上的障碍定义为动态障碍,并将其分成行人、运动障碍物和静止障碍物三类。机器人利用超声探测信息、人体红外感应检测信息和主动视觉信息识别障碍类型;并分别采用行人警示避让策略、运动障碍物等待避让策略和静止障碍物组合避障策略,实现移动机器人全局导航中的动态避障控制。
对基于分布式视觉的移动机器人路径跟踪控制进行了研究。采用一种基于定步长预瞄点的路径跟踪模糊控制方法。将机器人的运动控制简化为对其绕瞬心的转动控制,以移动机器人当前位置和期望位置的差值,当前位置移动机器人航向角和下一个控制周期期望路径上预瞄点的航向角差值作为模糊控制器的输入,以移动机器人的转动角速度作为模糊控制器的输出,实现了路径跟踪的模糊控制。
ANCES中的基于并行通信的多微处理器分布式控制体系结构,分布式视觉系统的设计与实现方法具有实用性和可扩展性,对开展智能领域相关技术的研究具有理论意义和实用价值。
As the robot application range extends constantly, its working condition is getting more and more complex, which is always unknown, dynamic and unstructured. So, it is a new challenge for robot to fulfill a mission in real time under these environments. The idea of mobile robot behavior control based on multi-sensor is put forward according to a survey of the research on mobile robot control architectures and navigation technology, and a summary of the shortage in local vision-based navigation: it is difficult to acquire adequate visual information from proper view point; it is difficult to acquire and maintain a consistent model of a wide dynamic environment. A mobile robot navigation control experiment system under a fixed area in the unstructured and dynamic environment is built for the main purpose of this dissertation to study on environment perception, behavior decision, motion control, mission distribution and corresponding control among the distributed vision agents, and so on, in the objective-oriented mission mobile robot behavior control. The main contents of this dissertation are as follows:
An autonomous navigation control experiment system (ANCES) is well developed which can applied to mobile robot behavior control, active vision-based (local vision-based) navigation, distributed vision-based navigation and other correlative research domains. The system consists of a mobile robot (RIRA-II ROBOT), a distributed vision system and a set of communication system.
The study on the architecture of mobile robots is carried out. A modularized distributed control system architecture based on multi-processor with parallel communication is put forward and employed in the hardware system design for RIRA-II ROBOT. Each module is controlled by an individual processor and the data processing is tackled by the same one processor. All modules run and communicate with each other in a parallel method, which improves the speed of data transmission and simplifies the software programming. Deliberated/reactive hierarchical hybrid architecture is used in the software of RIRA-II ROBOT, which consists of a human-machine interface layer, a mission planning layer and a behavior control layer.
A distributed ultrasonic detecting system based on multi-processor is developed. The detecting system adopts upper-lower two layers distributed control architecture. It is composed of an upper working method control module and a lower intelligent ultrasonic ranging module array. Each intelligent ultrasonic ranging sensor is controlled by an individual micro-processor which achieves ranging data processing, and the intelligent ultrasonic ranging modules can be grouped to work in parallel mode, which enhances the real-time performance. The threshold comparison method and an advanced data shift average filtering method are employed in the ranging data processing, which can restrain the ultrasonic crosstalk and random interference and improve the accuracy of the detecting system.
This dissertation investigates deeply behavior control strategy for mobile robot based on multi-sensor (vision, sonar and infrared) information. Priority-based behavior decision is adopted to control the robot. The RIRA-II ROBOT can also accomplish the objective-oriented mission utilizing visual perception. RIRA-II ROBOT has 3 behavior modules: goal approaching behavior, fuzzy control behavior and escape behavior, each module has a given priority. The behavior arbiter decides which behavior module can control the robot by their given priority when the behaviors are valid.
The study on configuration and work principle of distributed vision system(DVS) is carried out. The global image of robot’s working environment is obtained by mosaicing the images acquired by the vision agents; The search for global path is achieved according as the given restriction after quadtree environment mode building; A global path from the start point to end point is obtained by utilizing cubic spline curve to math the searched path line; The DVS decompounds the planned path and distributes them to correlative vision agents. The vision agents can control a mobile robot to fulfill the given mission by corresponding mechanism.
The study on mobile robot dynamic obstacles avoiding strategy in global navigation is carried out. Something that appeared on the path planned by DVS is regarded as dynamic obstacles which are classified into 3 kinds: passengers, motorial obstacles and static obstacles. The robot can distinguish the above dynamic obstacles by using the information of ultrasonic detecting system, human detecting module and active vision system. The robot can avoid the dynamic obstacles appeared in global navigation using different obstacle avoiding strategy.
The research on DVS-based mobile robot path tracking is carried out. A fuzzy path tracking control method based on fixed-step preview point is adopted. In this method, controlling the motion of mobile robot is simplified to control rotation about its instant center. The fuzzy controller has two input parameters: one is the distance error between the current robot’s position and the desired current position on the path; the other is angle error between the bearing of robot’s current position and the bearing of robot’s predictive position on the desired path in next control cycle. The output of the fuzzy controller is the rotation angular velocity of the robot.
The modularized distributed control system architecture based on multi-processor with parallel communication and the design and manufacture methods of DVS in ANCES show the characteristics of practicability and expansibility in intelligent robot research.
设计并实现了一套基于多传感器的移动机器人自主导航控制实验系统ANCES。该系统主要由移动机器人(RIRA-II ROBOT)、分布式视觉系统和无线通信系统组成。可应用于移动机器人行为控制、主动视觉(局部视觉)导航和分布式全局视觉导航等其它相关技术的研究。
对移动机器人的体系结构进行了研究,提出一种基于模块化并行通信的多微处理器分布式控制体系结构并应用于RIRA-II ROBOT硬件系统设计。各模块采用独立微处理器完成控制及信息处理功能,实现了多模块并行运行;模块之间采用的并行通信方式提高了信息传输速度,同时简化了系统的软件设计。RIRA-II ROBOT软件采用慎思/反应混合式分层控制体系结构,自上而下分为人机交互层、任务规划层和行为控制层三个层次。
研制了一种基于多微处理器的分布式超声探测系统。该探测系统由传感器工作模式控制模块和智能超声测距模块阵列组成。超声测距模块由独立的微处理器控制一个超声传感器,完成测距信息预处理,并可根据不同的控制策略实现分组并行工作,提高了探测系统的实时性;智能测距模块采用“阈值比较法”和“改进型递推平均滤波法”处理测距数据,有效地抑制了探测系统中超声波随机干扰信号及串扰信号,提高了探测系统的准确性。
对基于多传感器(视觉、超声、红外)信息的移动机器人行为控制策略进行了研究,采用基于优先级行为决策控制策略,并利用视觉信息使机器人能够完成面向目标的任务。RIRA-II ROBOT包含了三个行为模块:“趋向目标行为”、“模糊控制行为”和“解死锁行为”,每一行为都具有特定的优先级。行为仲裁器根据并发行为的优先级确定竞争获胜行为,由获胜行为控制移动机器人动作。
对分布式视觉系统的组成及工作原理进行了研究。利用图像拼接技术获取移动机器人工作环境的全局场景图像;利用四叉树对环境建模,根据路径最短约束条件完成路径搜索,运用三次样条曲线拟合法对搜索路径进行处理,生成从起点到终点的无碰撞全局路径;对全局路径进行任务分解,将分解后的路径分配到各相关视觉智能体,为视觉智能体对移动机器人进行控制提供任务依据。视觉智能体通过任务协调机制完成对移动机器人全局导航控制。
对移动机器人全局导航控制中动态避障策略进行了研究。将出现在由分布式视觉系统规划完成的路径上的障碍定义为动态障碍,并将其分成行人、运动障碍物和静止障碍物三类。机器人利用超声探测信息、人体红外感应检测信息和主动视觉信息识别障碍类型;并分别采用行人警示避让策略、运动障碍物等待避让策略和静止障碍物组合避障策略,实现移动机器人全局导航中的动态避障控制。
对基于分布式视觉的移动机器人路径跟踪控制进行了研究。采用一种基于定步长预瞄点的路径跟踪模糊控制方法。将机器人的运动控制简化为对其绕瞬心的转动控制,以移动机器人当前位置和期望位置的差值,当前位置移动机器人航向角和下一个控制周期期望路径上预瞄点的航向角差值作为模糊控制器的输入,以移动机器人的转动角速度作为模糊控制器的输出,实现了路径跟踪的模糊控制。
ANCES中的基于并行通信的多微处理器分布式控制体系结构,分布式视觉系统的设计与实现方法具有实用性和可扩展性,对开展智能领域相关技术的研究具有理论意义和实用价值。
As the robot application range extends constantly, its working condition is getting more and more complex, which is always unknown, dynamic and unstructured. So, it is a new challenge for robot to fulfill a mission in real time under these environments. The idea of mobile robot behavior control based on multi-sensor is put forward according to a survey of the research on mobile robot control architectures and navigation technology, and a summary of the shortage in local vision-based navigation: it is difficult to acquire adequate visual information from proper view point; it is difficult to acquire and maintain a consistent model of a wide dynamic environment. A mobile robot navigation control experiment system under a fixed area in the unstructured and dynamic environment is built for the main purpose of this dissertation to study on environment perception, behavior decision, motion control, mission distribution and corresponding control among the distributed vision agents, and so on, in the objective-oriented mission mobile robot behavior control. The main contents of this dissertation are as follows:
An autonomous navigation control experiment system (ANCES) is well developed which can applied to mobile robot behavior control, active vision-based (local vision-based) navigation, distributed vision-based navigation and other correlative research domains. The system consists of a mobile robot (RIRA-II ROBOT), a distributed vision system and a set of communication system.
The study on the architecture of mobile robots is carried out. A modularized distributed control system architecture based on multi-processor with parallel communication is put forward and employed in the hardware system design for RIRA-II ROBOT. Each module is controlled by an individual processor and the data processing is tackled by the same one processor. All modules run and communicate with each other in a parallel method, which improves the speed of data transmission and simplifies the software programming. Deliberated/reactive hierarchical hybrid architecture is used in the software of RIRA-II ROBOT, which consists of a human-machine interface layer, a mission planning layer and a behavior control layer.
A distributed ultrasonic detecting system based on multi-processor is developed. The detecting system adopts upper-lower two layers distributed control architecture. It is composed of an upper working method control module and a lower intelligent ultrasonic ranging module array. Each intelligent ultrasonic ranging sensor is controlled by an individual micro-processor which achieves ranging data processing, and the intelligent ultrasonic ranging modules can be grouped to work in parallel mode, which enhances the real-time performance. The threshold comparison method and an advanced data shift average filtering method are employed in the ranging data processing, which can restrain the ultrasonic crosstalk and random interference and improve the accuracy of the detecting system.
This dissertation investigates deeply behavior control strategy for mobile robot based on multi-sensor (vision, sonar and infrared) information. Priority-based behavior decision is adopted to control the robot. The RIRA-II ROBOT can also accomplish the objective-oriented mission utilizing visual perception. RIRA-II ROBOT has 3 behavior modules: goal approaching behavior, fuzzy control behavior and escape behavior, each module has a given priority. The behavior arbiter decides which behavior module can control the robot by their given priority when the behaviors are valid.
The study on configuration and work principle of distributed vision system(DVS) is carried out. The global image of robot’s working environment is obtained by mosaicing the images acquired by the vision agents; The search for global path is achieved according as the given restriction after quadtree environment mode building; A global path from the start point to end point is obtained by utilizing cubic spline curve to math the searched path line; The DVS decompounds the planned path and distributes them to correlative vision agents. The vision agents can control a mobile robot to fulfill the given mission by corresponding mechanism.
The study on mobile robot dynamic obstacles avoiding strategy in global navigation is carried out. Something that appeared on the path planned by DVS is regarded as dynamic obstacles which are classified into 3 kinds: passengers, motorial obstacles and static obstacles. The robot can distinguish the above dynamic obstacles by using the information of ultrasonic detecting system, human detecting module and active vision system. The robot can avoid the dynamic obstacles appeared in global navigation using different obstacle avoiding strategy.
The research on DVS-based mobile robot path tracking is carried out. A fuzzy path tracking control method based on fixed-step preview point is adopted. In this method, controlling the motion of mobile robot is simplified to control rotation about its instant center. The fuzzy controller has two input parameters: one is the distance error between the current robot’s position and the desired current position on the path; the other is angle error between the bearing of robot’s current position and the bearing of robot’s predictive position on the desired path in next control cycle. The output of the fuzzy controller is the rotation angular velocity of the robot.
The modularized distributed control system architecture based on multi-processor with parallel communication and the design and manufacture methods of DVS in ANCES show the characteristics of practicability and expansibility in intelligent robot research.
引文
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