煤矿探测机器人姿态控制与局部路径规划研究
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摘要
救援机器人已经成为灾难救援的重要设备,但大多数救援机器人仍依赖操作人员遥控。本课题研究团队提出了由运载机器人和探测机器人构建的煤矿救援机器人系统,旨在实现救援机器人系统的自主导航与智能控制。本文针对煤矿探测机器人在煤矿井下事故现场未知环境中的姿态控制与局部路径规划进行了深入研究。
首先分析了事故后的煤矿井下环境,结合煤矿探测机器人任务需求,提出了煤矿探测机器人三层分布式模块化控制结构。导航控制体系的核心决策规划系统采用分布式递阶结构,各个功能分布式模块按照机器人的行为决策过程递阶分布,各模块间按照反应式的行为准则相互协调配合,完成探测机器人的决策与控制过程。该系统具有较好的自主性、实时性与开放性,能够满足机器人在复杂环境下自主控制的需求。
提出将煤矿探测机器人的并联肢体履带式运动机构通过运动学等效变形,消除非完整约束条件,进而分析等效并联机器人位姿运动学的方法。采用这种方法将探测机器人运动机构等效成4—PPSR并联机器人,并利用旋量理论对等效并联机器人动平台的姿态进行运动学建模。分析了在运动学模型基础上的机器人静态稳定性,得出了基于几何分析的投影稳定锥静态稳定性判定原理;通过对倾覆过程的重力势能变化分析,得到了机器人动态稳定性和运行速度关系,对这两种稳定性的分析为煤矿探测机器人运行姿态稳定性控制提供了重要依据。针对煤矿探测机器人的驱动行为、姿态控制行为、越障行为和路径规划行为等四种基本行为的协调问题,提出了基于专家逻辑系统的反应式煤矿探测机器人行为协调器。分析了煤矿探测机器人在井下行驶时针对不同路面环境的驱动、姿态控制及越障的行为模式,重点对运行过程中的机器人姿态控制进行了研究,提出了基于模糊控制算法的煤矿探测机器人反应式姿态控制方法,并验证了该方法对复杂地面环境有良好的适应性。
针对未知环境中煤矿探测机器人局部路径规划问题,提出了方向寻优人工势场滚动窗口局部路径规划算法。这种方法摒弃了传统人工势场法将全局环境仅归结为单一势场力的处理思想,针对煤矿探测机器人对障碍物检测的特点,提出在机器人全部可行驶方向中搜索合力最大的最优行驶方向的改进人工势场方法,并结合滚动窗口思想,提出了机器人反应式路径规划算法。针对反应式路径规划方法存在的规划陷阱问题,提出了煤矿探测机器人的慎思式局部路径规划算法。慎思规划过程需要对机器人游历场景建立局部环境地图,为此本文提出了探测机器人游历场景栅格拓扑地图的建立方法,并利用提出的主动生长元栅格拓扑地图Voronoi图分析算法对该地图进行可行路径分析,可生成Voronoi节点和边构成的无碰路径网络,利用Floyd最短路搜索方法找到逃逸目标节点和全局目标节点之间的最短路径。融合两种路径规划算法,提出了煤矿探测机器人的反应——慎思式的混合局部路径规划算法。实验表明该算法不仅有效解决机器人路径规划过程中的规划陷阱问题,而且对煤矿井下未知复杂环境具有良好的适应性。
以课题组研发的煤矿探测机器人本体和控制系统为平台,以本文提出的姿态控制算法和局部路径规划算法为基础,借助于VC++,开发了机器人软件系统,分别在实验室和模拟实验巷道进行了行驶实验,结果证明本文提出的煤矿探测机器人路径规划算法和开发的控制系统能够保证机器人在未知环境中进行环境理解和路径规划,并在非结构化路面条件下保持良好的行驶姿态。
Rescue robot has become an important mean in the process of disaster succoring, but most rescue robots still rely on remote operation for motion control. Our research team of Xi'an University of Science & Technology proposed that the coal mine rescue robot robot was composed of carrier robot and detection robot, and the coal mine rescue robot could autonomous navigation and work. In this paper, the posture control technology and local path planning of coal mine detection robot in unknown environment are in-depth researched.
In this paper, with analyzing the environment of coal mine accident and the mission of coal mine detection robot, the robot control system was designed. The distributed modularized architecture with three-layer was used for the control system, and the distributed hierarchical architecture was used for decision control system as the core of the robot control system. The principle of the control system was taken as the basis for building the mine robot control system experiment platform system. The control system can meet the requirements of detection capability in the coal mine of coal mine detection robot, and it has satisfactory performance in autonomy, openness and real-time.
The motion mechanism of coal mine detection robot was designed as tracked robot with 4- parallel tracked limbs. The method was presented that the pose of equivalent robot which the non-holonomic constraints between parallel limbs of the detection robot and ground were eliminated through equivalent transformation of the motion mechanism was analyzed. By implementing this method, the detecting robot was equivalent into a 4-PPSR parallel robot, and the posture kinematic model of the moving platform in parallel robot was analyzed with the use of motion screw theory. With analyzing the static stability on the basis of the robot kinematics model and dynamic stability based on the changing analysis of potential energy, therefore, two conclusions were got respectively, one was static stability decision theorem based on projection stabilization cone through geometric analysis; another was the relationship between the speed and dynamitic stability, which was the important theory basis for keeping stable motion while the coal mine detection robot was driving. On the coordination for four basis behaviors of coal mine detection robot, driving behavior, attitude adjustment behavior, obstacle crossing behavior and path planning behavior, a reactive behavior coordinator system based on expert logic system for mine rescue robot was proposed. In the paper, the behavior pattern was analyzed of which in different road condition when robot was driving underground in coal mine. Among them, the attitude adjustment behavior on the operation of robot driving was emphasized researched. Therefore, based on the fuzzy control algorithm, the coal mine detection robot reactive behavior control for the attitude adjustment method was proposed, and with the experiment, it was testified that the control method was good adaptable for the complexity underground environment.
In this paper, a local path planning method closer to the human mind, direction optimizing artificial potential field based on the rolling windows was proposed, which was for robot’s path planning behavior of detecting mines in an unknown environment. The method used in classical artificial potential field algorithm which boils down the global information to a single field force was abandoned in the improved artificial potential field. The improved artificial potential field can resolve the best direction which field force is biggest among all driving direction for robot. The improved artificial potential field combining with the rolling windows path planning, the reaction local path planning method was presented. While the reactive path planning behavior of the robot could not achieve at the global target because of absence of global information, it will cause the non decision-making status and planning pitfalls and other issues probably. Therefore, the center average method was proposed of recalculate the maximum force to solve the non decision-making status. Foe solving the planning pitfall issues, it was required to deliberative planning and make judgments on the current situation. The deliberative planning process should analyze the travailed scenes based on the local grid-topological map which was analyzed through active growth Voronoi graph to generate the collision-free paths network consisting of Voronoi edges and nodes. The shortest escape path between escape target node and global goal node can be got through the Floyd shortest path searching method. The reaction- deliberating mixed local path planning method of coal mine detection robot was proposed, which was constituted by reactive path planning behavior and deliberative style planning process. By experiment, it was testified that the method can solve the obstacle avoidance problem effectively in the process of planning the trap and could be adapted to the complexity of coal unknown environment.
Based on the theoretical research of this paper, the coal mine detection robot control system based on the study achievements of this paper was developed used in experimental robot which was developed by our research team through VC++. The experiments were operated in the laboratory environment and the simulated roadway environment with experimental robot, which indicated that the coal mine detection robot could achieve the understanding environment and path planning in an unknown environment, and could maintain the good posture in the condition of unconstructed roads. Above all, it is indicated the research of this paper provided with practicality and availability in the unknown complex environment.
首先分析了事故后的煤矿井下环境,结合煤矿探测机器人任务需求,提出了煤矿探测机器人三层分布式模块化控制结构。导航控制体系的核心决策规划系统采用分布式递阶结构,各个功能分布式模块按照机器人的行为决策过程递阶分布,各模块间按照反应式的行为准则相互协调配合,完成探测机器人的决策与控制过程。该系统具有较好的自主性、实时性与开放性,能够满足机器人在复杂环境下自主控制的需求。
提出将煤矿探测机器人的并联肢体履带式运动机构通过运动学等效变形,消除非完整约束条件,进而分析等效并联机器人位姿运动学的方法。采用这种方法将探测机器人运动机构等效成4—PPSR并联机器人,并利用旋量理论对等效并联机器人动平台的姿态进行运动学建模。分析了在运动学模型基础上的机器人静态稳定性,得出了基于几何分析的投影稳定锥静态稳定性判定原理;通过对倾覆过程的重力势能变化分析,得到了机器人动态稳定性和运行速度关系,对这两种稳定性的分析为煤矿探测机器人运行姿态稳定性控制提供了重要依据。针对煤矿探测机器人的驱动行为、姿态控制行为、越障行为和路径规划行为等四种基本行为的协调问题,提出了基于专家逻辑系统的反应式煤矿探测机器人行为协调器。分析了煤矿探测机器人在井下行驶时针对不同路面环境的驱动、姿态控制及越障的行为模式,重点对运行过程中的机器人姿态控制进行了研究,提出了基于模糊控制算法的煤矿探测机器人反应式姿态控制方法,并验证了该方法对复杂地面环境有良好的适应性。
针对未知环境中煤矿探测机器人局部路径规划问题,提出了方向寻优人工势场滚动窗口局部路径规划算法。这种方法摒弃了传统人工势场法将全局环境仅归结为单一势场力的处理思想,针对煤矿探测机器人对障碍物检测的特点,提出在机器人全部可行驶方向中搜索合力最大的最优行驶方向的改进人工势场方法,并结合滚动窗口思想,提出了机器人反应式路径规划算法。针对反应式路径规划方法存在的规划陷阱问题,提出了煤矿探测机器人的慎思式局部路径规划算法。慎思规划过程需要对机器人游历场景建立局部环境地图,为此本文提出了探测机器人游历场景栅格拓扑地图的建立方法,并利用提出的主动生长元栅格拓扑地图Voronoi图分析算法对该地图进行可行路径分析,可生成Voronoi节点和边构成的无碰路径网络,利用Floyd最短路搜索方法找到逃逸目标节点和全局目标节点之间的最短路径。融合两种路径规划算法,提出了煤矿探测机器人的反应——慎思式的混合局部路径规划算法。实验表明该算法不仅有效解决机器人路径规划过程中的规划陷阱问题,而且对煤矿井下未知复杂环境具有良好的适应性。
以课题组研发的煤矿探测机器人本体和控制系统为平台,以本文提出的姿态控制算法和局部路径规划算法为基础,借助于VC++,开发了机器人软件系统,分别在实验室和模拟实验巷道进行了行驶实验,结果证明本文提出的煤矿探测机器人路径规划算法和开发的控制系统能够保证机器人在未知环境中进行环境理解和路径规划,并在非结构化路面条件下保持良好的行驶姿态。
Rescue robot has become an important mean in the process of disaster succoring, but most rescue robots still rely on remote operation for motion control. Our research team of Xi'an University of Science & Technology proposed that the coal mine rescue robot robot was composed of carrier robot and detection robot, and the coal mine rescue robot could autonomous navigation and work. In this paper, the posture control technology and local path planning of coal mine detection robot in unknown environment are in-depth researched.
In this paper, with analyzing the environment of coal mine accident and the mission of coal mine detection robot, the robot control system was designed. The distributed modularized architecture with three-layer was used for the control system, and the distributed hierarchical architecture was used for decision control system as the core of the robot control system. The principle of the control system was taken as the basis for building the mine robot control system experiment platform system. The control system can meet the requirements of detection capability in the coal mine of coal mine detection robot, and it has satisfactory performance in autonomy, openness and real-time.
The motion mechanism of coal mine detection robot was designed as tracked robot with 4- parallel tracked limbs. The method was presented that the pose of equivalent robot which the non-holonomic constraints between parallel limbs of the detection robot and ground were eliminated through equivalent transformation of the motion mechanism was analyzed. By implementing this method, the detecting robot was equivalent into a 4-PPSR parallel robot, and the posture kinematic model of the moving platform in parallel robot was analyzed with the use of motion screw theory. With analyzing the static stability on the basis of the robot kinematics model and dynamic stability based on the changing analysis of potential energy, therefore, two conclusions were got respectively, one was static stability decision theorem based on projection stabilization cone through geometric analysis; another was the relationship between the speed and dynamitic stability, which was the important theory basis for keeping stable motion while the coal mine detection robot was driving. On the coordination for four basis behaviors of coal mine detection robot, driving behavior, attitude adjustment behavior, obstacle crossing behavior and path planning behavior, a reactive behavior coordinator system based on expert logic system for mine rescue robot was proposed. In the paper, the behavior pattern was analyzed of which in different road condition when robot was driving underground in coal mine. Among them, the attitude adjustment behavior on the operation of robot driving was emphasized researched. Therefore, based on the fuzzy control algorithm, the coal mine detection robot reactive behavior control for the attitude adjustment method was proposed, and with the experiment, it was testified that the control method was good adaptable for the complexity underground environment.
In this paper, a local path planning method closer to the human mind, direction optimizing artificial potential field based on the rolling windows was proposed, which was for robot’s path planning behavior of detecting mines in an unknown environment. The method used in classical artificial potential field algorithm which boils down the global information to a single field force was abandoned in the improved artificial potential field. The improved artificial potential field can resolve the best direction which field force is biggest among all driving direction for robot. The improved artificial potential field combining with the rolling windows path planning, the reaction local path planning method was presented. While the reactive path planning behavior of the robot could not achieve at the global target because of absence of global information, it will cause the non decision-making status and planning pitfalls and other issues probably. Therefore, the center average method was proposed of recalculate the maximum force to solve the non decision-making status. Foe solving the planning pitfall issues, it was required to deliberative planning and make judgments on the current situation. The deliberative planning process should analyze the travailed scenes based on the local grid-topological map which was analyzed through active growth Voronoi graph to generate the collision-free paths network consisting of Voronoi edges and nodes. The shortest escape path between escape target node and global goal node can be got through the Floyd shortest path searching method. The reaction- deliberating mixed local path planning method of coal mine detection robot was proposed, which was constituted by reactive path planning behavior and deliberative style planning process. By experiment, it was testified that the method can solve the obstacle avoidance problem effectively in the process of planning the trap and could be adapted to the complexity of coal unknown environment.
Based on the theoretical research of this paper, the coal mine detection robot control system based on the study achievements of this paper was developed used in experimental robot which was developed by our research team through VC++. The experiments were operated in the laboratory environment and the simulated roadway environment with experimental robot, which indicated that the coal mine detection robot could achieve the understanding environment and path planning in an unknown environment, and could maintain the good posture in the condition of unconstructed roads. Above all, it is indicated the research of this paper provided with practicality and availability in the unknown complex environment.
引文
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