多AUV协调控制技术研究
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摘要
近年来,自治水下机器人(autonomous underwater vehicles,简称AUV)在海洋探测中表现出来的巨大优越性吸引了人们越来越多的关注。AUV自带能源,与水面母船无物理连接,无需人为干预,可以携带相应设备对海洋进行自由观测,可用于海洋数据搜集、海底目标探测以及海底管路跟踪等广泛领域。
然而,单个AUV在能源、控制、传感等方面能力有限,所以只能用于执行一些简单的、局部的海洋任务,而复杂的、大范围的海洋任务仍然超出了AUV的能力范围。为了解决这一问题,一方面可以进一步开发智能更高、能力更强、功能更多的AUV,另一方面也可以在现有AUV的基础上,通过多个AUV之间的协调合作来完成单个AUV无法或难以胜任的工作。
多AUV协作的核心思想是让多个具有简单功能的AUV有机地组成一个整体,通过一定的协作策略,使团队中的每个成员各司其职、充分发挥团队精神来相互协调与合作,从而完成给定的任务。相对单AUV而言,多AUV系统具有空间分布性、功能分布性以及更高可靠性等优点。
由于水下环境的特殊性,与地面和空中的多机器人系统研究相比,多水下机器人协调控制方面的研究还不成熟,尚处于初级阶段。本文通过理论分析和数值仿真的方法,对多AUV协调控制技术进行了一定研究,主要内容包括: 1. AUV六自由度运动建模与控制。多AUV协作系统归根结底是由一群单个AUV组成的,所以要想研究多AUV系统,先要对这一系统中单个AUV的性能进行全面分析,以保证整个系统的安全性和可靠性。由于通常基于欧拉角方法的运动方程在AUV纵倾±90度时会产生奇异点,所以本文基于四元数法来建立AUV六自由度运动方程,以避免奇异点的发生,使AUV能够以任意纵倾角进行全自由度仿真;
2.多AUV协调路径规划。AUV在执行任务的时候,往往是沿着事先规划好的路径前进的,这些路径由一个个相邻的、已知的路径点连接而成。由于能源是制约AUV作业时间的重要因素,所以我们希望规划出的路径是最优的。在路径点数量较少的情况下,通过排列组合或者枚举法很容易找出AUV的最优路径;但是当路径点较多时,那么无论采用人工排列组合方法或是计算机枚举法其计算量都非常大,所以本文采用善于处理组合优化问题的智能算法(如遗传算法和蚁群算法)来对路径进行优化,确保找到最佳路径。算法分为三部分:1)路径点分配:将路径点分配给各个AUV;2)路径优化:以启发式算法为工具,结合旅行商问题对路径进行优化,找出最短路径;3)路径校核:检验是否存在静态碰撞或动态碰撞的危险;
3.多AUV队形控制算法。一群AUV在抵达目的地的过程中,保持一定的队形很有必要,适当的队形可以提高多AUV系统的安全和作业效率。传统的领航者-跟随者方法对领航者过分依赖,所以一旦领航者发生故障,队形就会失去控制。鉴于此,本文设计了基于模糊势场的虚拟领航者队形控制方法,由于该方法中的领航者是虚拟的,于是不存在领航者发生故障的情况,弥补了“领航者-跟随者”方法的不足。虚拟领航者队形控制方法中AUV的运动状态可以通过定义人工势场来进行控制,由于通常采用势函数所定义的人工势场其势场力会发生突变从而会导致规划出的路径轨迹并不光滑,所以本文引入模糊逻辑来建立模糊人工势场,通过隶属度函数和模糊规则来构造势场中距离与势场力之间连续的映射关系;
4.多AUV覆盖控制算法。假定最初聚集在一起、装备有测量和通信传感器的一群AUV要对某一海域进行探测,为了使探测效果最优,这群AUV应以某种特定的空间分布(密度函数)布放在这块区域。本文通过一定的控制算法,使描述覆盖控制性能指标的位置优化函数逐渐趋于收敛,最终使得一群AUV在被考察海域形成指定密度函数(如均匀分布、圆形分布、高斯分布等)的质心Voronoi分布(简称CVT分布)来对指定水域进行勘探。覆盖控制算法在处理非均匀密度分布函数时,选取合适的惩罚系数非常重要,惩罚系数过大或过小都会对AUV的运动轨迹产生不利影响。本文在非均匀密度分布函数中采用了一种连续线性惩罚系数即惩罚函数,以确保AUV实现期望的覆盖分布。
The recent years have witnessed people’s growing interests of applying of autonomous underwater vehicles (AUVs) in ocean explorations and exploitations, e.g. oceanographic data collection, underwater object inspection and pipes tracking. With its onboard power and instruments, an AUV can be used to explore oceans without physical link to the support ship supply vehicle and intervention of human beings.
However, due to the limitations from related technologies such as power, control and sensing, a single AUV can only conduct simple tasks within a small area. To enhance AUV’s ability for complex and long duration missions, there are two options: 1) to develop a single AUV with high intelligence, strong capacities and multiple functions; 2) to deploy multiple small AUVs and make them to achieve a cooperated group behavior.
The key idea behind the second option is that the coordinated multiple simple AUVs can work in a coordinated manner and act as a group during missions. By employing related control strategies, the complex missions can be reduced to many simple sub-missions which are allocated to corresponding AUVs, each of which is responsible for its own sub-mission and exhibits the team work spirit. Through cooperation and coordination of multiple vehicles, many complex tasks can be conducted. Moreover, coordinated multiple AUVs have much more advantages than single AUV in many aspects, such as spatial distribution, function distribution and higher reliability.
Due to the special undersea environment, the existing research on coordination and cooperation of multiple AUVs is much more immature compared with those works for land and air vehicles. In this dissertation, the coordinated operation of multiple AUVs has been studied through theory analysis and numerical simulations, the following efforts have been made in this work, including:
1. The modeling and controlling of a single AUV in six degrees-of-free (DOFs). To guarantee the reliability and safety of the system, an overall analysis of the performance of a single AUV is necessary since the coordinated system is based on single vehicles. The existing modeling of marine vehicles in six-DOFs is mainly based on Euler angles which has representation singularities for a pitch angle of positive/negative ninety degrees. To avoid singularities inherently in Euler angle representation, quaternion representation is adopted in dynamic modeling.
2. Cooperative path planning for multiple vehicles. During the exploration, the path of an AUV consists of finite sequence of waypoints which are defined a prior. It is desirable to obtain an optimal route due to the limited onboard power. Enumeration or permutation can be applied to find optimal paths for limited waypoints, but they are computationally expensive for large scale of waypoints. The efficient intelligent methods, e.g. genetic algorithm and ant colony algorithm, have been utilized to solve the combinational optimization problem in this work. The proposed path planning algorithm consists of three phases: 1) waypoint assignment: allocating the waypoints to individual AUVs; 2) route optimization: minimizing the total journey of all vehicles by heuristic methods involved in the Traveling Salesman Problem (TSP); 3) route validation: checking if there exists static or moving potential collisions.
3. Formation control of multiple AUVs. It is significant for a group of vehicles to maintain certain formation from initial location to a specified destination within an unknown environment. It is believed that such a formation may improve the safety and efficiency of the group. Since there exists possible failure of Leader in the conventional Leader-Follower method, an alternative, Virtual Leader method, is presented in this paper to improve the Leader-Follower method. In this method, the interactions between vehicles are based on artificial potentials. Since the potential force is not continuous for conventional function-based artificial potentials, a fuzzy-logic-based artificial potential, which deals with input and output variables of fuzzy logic controller through membership functions and fuzzy rules, is proposed to generate continuous potential forces.
4. Coverage control of multiple AUVs. Suppose a group of initially clustered AUVs, which equipped with sensors for measuring and communicating, are exploring a certain ocean region. In order to optimize the exploring performance, it is desirable to distribute the vehicles according to certain density function. The control law minimizes the locational optimization function, which defines coverage quality, and ultimately drives a group of vehicles to form centroidal Voronoi tessellations (CVT) over the given area with a prescribed geometric pattern, e.g. uniform, circular and Gaussian styles. It is of great important to design a proper penalty coefficient for non-uniform density distribution areas since an improper coefficient would result in unexpected vehicle trajectories. Therefore, a continuous linear penalty coefficient, i.e. penalty function, is utilized in density function to achive the desired distribution.
然而,单个AUV在能源、控制、传感等方面能力有限,所以只能用于执行一些简单的、局部的海洋任务,而复杂的、大范围的海洋任务仍然超出了AUV的能力范围。为了解决这一问题,一方面可以进一步开发智能更高、能力更强、功能更多的AUV,另一方面也可以在现有AUV的基础上,通过多个AUV之间的协调合作来完成单个AUV无法或难以胜任的工作。
多AUV协作的核心思想是让多个具有简单功能的AUV有机地组成一个整体,通过一定的协作策略,使团队中的每个成员各司其职、充分发挥团队精神来相互协调与合作,从而完成给定的任务。相对单AUV而言,多AUV系统具有空间分布性、功能分布性以及更高可靠性等优点。
由于水下环境的特殊性,与地面和空中的多机器人系统研究相比,多水下机器人协调控制方面的研究还不成熟,尚处于初级阶段。本文通过理论分析和数值仿真的方法,对多AUV协调控制技术进行了一定研究,主要内容包括: 1. AUV六自由度运动建模与控制。多AUV协作系统归根结底是由一群单个AUV组成的,所以要想研究多AUV系统,先要对这一系统中单个AUV的性能进行全面分析,以保证整个系统的安全性和可靠性。由于通常基于欧拉角方法的运动方程在AUV纵倾±90度时会产生奇异点,所以本文基于四元数法来建立AUV六自由度运动方程,以避免奇异点的发生,使AUV能够以任意纵倾角进行全自由度仿真;
2.多AUV协调路径规划。AUV在执行任务的时候,往往是沿着事先规划好的路径前进的,这些路径由一个个相邻的、已知的路径点连接而成。由于能源是制约AUV作业时间的重要因素,所以我们希望规划出的路径是最优的。在路径点数量较少的情况下,通过排列组合或者枚举法很容易找出AUV的最优路径;但是当路径点较多时,那么无论采用人工排列组合方法或是计算机枚举法其计算量都非常大,所以本文采用善于处理组合优化问题的智能算法(如遗传算法和蚁群算法)来对路径进行优化,确保找到最佳路径。算法分为三部分:1)路径点分配:将路径点分配给各个AUV;2)路径优化:以启发式算法为工具,结合旅行商问题对路径进行优化,找出最短路径;3)路径校核:检验是否存在静态碰撞或动态碰撞的危险;
3.多AUV队形控制算法。一群AUV在抵达目的地的过程中,保持一定的队形很有必要,适当的队形可以提高多AUV系统的安全和作业效率。传统的领航者-跟随者方法对领航者过分依赖,所以一旦领航者发生故障,队形就会失去控制。鉴于此,本文设计了基于模糊势场的虚拟领航者队形控制方法,由于该方法中的领航者是虚拟的,于是不存在领航者发生故障的情况,弥补了“领航者-跟随者”方法的不足。虚拟领航者队形控制方法中AUV的运动状态可以通过定义人工势场来进行控制,由于通常采用势函数所定义的人工势场其势场力会发生突变从而会导致规划出的路径轨迹并不光滑,所以本文引入模糊逻辑来建立模糊人工势场,通过隶属度函数和模糊规则来构造势场中距离与势场力之间连续的映射关系;
4.多AUV覆盖控制算法。假定最初聚集在一起、装备有测量和通信传感器的一群AUV要对某一海域进行探测,为了使探测效果最优,这群AUV应以某种特定的空间分布(密度函数)布放在这块区域。本文通过一定的控制算法,使描述覆盖控制性能指标的位置优化函数逐渐趋于收敛,最终使得一群AUV在被考察海域形成指定密度函数(如均匀分布、圆形分布、高斯分布等)的质心Voronoi分布(简称CVT分布)来对指定水域进行勘探。覆盖控制算法在处理非均匀密度分布函数时,选取合适的惩罚系数非常重要,惩罚系数过大或过小都会对AUV的运动轨迹产生不利影响。本文在非均匀密度分布函数中采用了一种连续线性惩罚系数即惩罚函数,以确保AUV实现期望的覆盖分布。
The recent years have witnessed people’s growing interests of applying of autonomous underwater vehicles (AUVs) in ocean explorations and exploitations, e.g. oceanographic data collection, underwater object inspection and pipes tracking. With its onboard power and instruments, an AUV can be used to explore oceans without physical link to the support ship supply vehicle and intervention of human beings.
However, due to the limitations from related technologies such as power, control and sensing, a single AUV can only conduct simple tasks within a small area. To enhance AUV’s ability for complex and long duration missions, there are two options: 1) to develop a single AUV with high intelligence, strong capacities and multiple functions; 2) to deploy multiple small AUVs and make them to achieve a cooperated group behavior.
The key idea behind the second option is that the coordinated multiple simple AUVs can work in a coordinated manner and act as a group during missions. By employing related control strategies, the complex missions can be reduced to many simple sub-missions which are allocated to corresponding AUVs, each of which is responsible for its own sub-mission and exhibits the team work spirit. Through cooperation and coordination of multiple vehicles, many complex tasks can be conducted. Moreover, coordinated multiple AUVs have much more advantages than single AUV in many aspects, such as spatial distribution, function distribution and higher reliability.
Due to the special undersea environment, the existing research on coordination and cooperation of multiple AUVs is much more immature compared with those works for land and air vehicles. In this dissertation, the coordinated operation of multiple AUVs has been studied through theory analysis and numerical simulations, the following efforts have been made in this work, including:
1. The modeling and controlling of a single AUV in six degrees-of-free (DOFs). To guarantee the reliability and safety of the system, an overall analysis of the performance of a single AUV is necessary since the coordinated system is based on single vehicles. The existing modeling of marine vehicles in six-DOFs is mainly based on Euler angles which has representation singularities for a pitch angle of positive/negative ninety degrees. To avoid singularities inherently in Euler angle representation, quaternion representation is adopted in dynamic modeling.
2. Cooperative path planning for multiple vehicles. During the exploration, the path of an AUV consists of finite sequence of waypoints which are defined a prior. It is desirable to obtain an optimal route due to the limited onboard power. Enumeration or permutation can be applied to find optimal paths for limited waypoints, but they are computationally expensive for large scale of waypoints. The efficient intelligent methods, e.g. genetic algorithm and ant colony algorithm, have been utilized to solve the combinational optimization problem in this work. The proposed path planning algorithm consists of three phases: 1) waypoint assignment: allocating the waypoints to individual AUVs; 2) route optimization: minimizing the total journey of all vehicles by heuristic methods involved in the Traveling Salesman Problem (TSP); 3) route validation: checking if there exists static or moving potential collisions.
3. Formation control of multiple AUVs. It is significant for a group of vehicles to maintain certain formation from initial location to a specified destination within an unknown environment. It is believed that such a formation may improve the safety and efficiency of the group. Since there exists possible failure of Leader in the conventional Leader-Follower method, an alternative, Virtual Leader method, is presented in this paper to improve the Leader-Follower method. In this method, the interactions between vehicles are based on artificial potentials. Since the potential force is not continuous for conventional function-based artificial potentials, a fuzzy-logic-based artificial potential, which deals with input and output variables of fuzzy logic controller through membership functions and fuzzy rules, is proposed to generate continuous potential forces.
4. Coverage control of multiple AUVs. Suppose a group of initially clustered AUVs, which equipped with sensors for measuring and communicating, are exploring a certain ocean region. In order to optimize the exploring performance, it is desirable to distribute the vehicles according to certain density function. The control law minimizes the locational optimization function, which defines coverage quality, and ultimately drives a group of vehicles to form centroidal Voronoi tessellations (CVT) over the given area with a prescribed geometric pattern, e.g. uniform, circular and Gaussian styles. It is of great important to design a proper penalty coefficient for non-uniform density distribution areas since an improper coefficient would result in unexpected vehicle trajectories. Therefore, a continuous linear penalty coefficient, i.e. penalty function, is utilized in density function to achive the desired distribution.
引文
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