群体机器人系统协同一致行为控制算法设计与仿真研究
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摘要
集群行为是自然界中常见的现象,比如集结迁徙的鸟群,结队巡游的鱼群,聚集而生的细菌等。在对这些行为的研究中发现,自然界中的这种集群行为使得这些个体在逃避天敌,寻找食物等方面都有着单个个体所无法比拟的优势,并且能完成一些单个个体所不能完成的复杂任务。在自然界集群行为的启发下,群体机器人系统主要研究大量简单个体如何通过局部交互涌现出复杂行为。群体机器人系统是多机器人系统的扩展,较单机器人系统和多机器人系统其具有显著的优势:①、具有很好的鲁棒性:即在环境中出现干扰或者系统中有个体失效时,群体机器人系统仍然能够继续执行任务;②、具有很强的适应性:即群体机器人系统有能力解决不同的任务;③、具有可扩展性:群体中个体数量的随意增加或者减少都不影响系统的正常运行。
对群体机器人系统进行研究具有重要的理论及工程意义。对群体机器人系统的研究越深入,越有助于揭示自然界中复杂集群行为的本质,能够更直观地再现生物群体的智能行为。并且随着科学技术的发展,机器人制造技术不断提高,制造成本不断减小,在未来战场、医疗领域、航天领域以及工业领域群体机器人系统都将有着广泛的应用。
本文对群体机器人系统的协同一致行为进行了研究,主要内容如下:
(1)、描述了个体间的模糊邻居关系。个体与其感知范围内的邻居间距大小不一,因而每个邻居在控制策略内部对个体的调整作用也不同,这就需要建立群体中个体间不同邻居强度关系。利用个体间距作为模糊输入,经模糊系统推理得出个体间的邻居强度关系。在此基础上设计了群体协同一致行为控制算法,仿真结果表明群体能够有效地形成稳定结构并同步运行。为了进一步研究群体协同运行时系统的鲁棒性,分别对系统中出现完全失效个体、通信失效个体以及外界随机干扰情况进行了仿真实验。仿真结果表明,在文中给出的控制器作用下,群体具有很好的鲁棒性。
(2)、设计了多个虚拟领导者环境下群体的分裂机制。当环境中出现多个虚拟领导者需要分别跟踪时,个体根据领导者的需求以及周围邻居的跟踪情况利用蚁群算法计算对不同领导者的跟踪概率,并利用轮盘赌的方法确定不同个体的跟踪目标。基于跟踪相同领导者个体间的作用力函数设计了跟踪不同领导者个体间的相互作用力函数,实现了群体分裂成不同子群体的目的,并分别对不同的领导者进行跟踪。仿真结果表明所提出的分裂算法能有效地使群体按照领导者需求分裂成不同大小的子群体对不同的领导者分别进行跟踪。
(3)、针对障碍物环境以及未知阻尼环境设计了群体协同策略。当环境中存在一个或多个障碍物时,基于群体的数量优势改进传统人工势场法实现对障碍物的避碰。避碰过程中,障碍物对个体产生排斥作用,同时个体向周围没有感知到障碍物的邻居中心靠近。通过仿真实验,所提出的算法能够有效地实现群体系统对障碍物的避碰。未知环境阻尼将影响群体速度的收敛,利用模糊自适应整定控制器的方法在线调整控制参数。仿真结果表明,这一方法能有效减小未知阻尼的影响,使群体系统快速收敛至期望的运行速度。
(4)、提出了一种新的应用于群体协同一致行为中的通信策略。针对群体协同行为过程中通信量大的缺陷,首先利用粒子群优化算法对群体运行过程的通信量进行优化,得到最佳通信频率,从而达到了减少冗余通信量的目的。然后,通过建立群体结构以及运行状态的评价函数,在不同的评价值时对应采用不同的通信频率,以进一步降低群体在协同行为中的通信量。经仿真实验验证,所提策略能在不影响群体协同一致运行的基础上有效地降低系统的通信量。
综上所述,本文针对群体协同一致行为的四个问题进行了研究,主要工作着眼于不同外界环境中群体协同行为控制策略的设计,并提出减少系统中冗余的通信量的策略,仿真实验验证了所提算法的有效性。
Flocking behavior is a common phenomenon in nature, such as flocks of birds, schools of fish, and colonies of bacteria. It is found that flocking behavior makes the individuals of the swarm have certain advantages over single individual in many kinds of task, for example, avoiding predators, finding food, and completing some complex task which single individual can never achieved.
Inspired by the flocking behavior in nature, the complex behavior of the swarm robot system is studied through local interaction between simple agents. Swarm robot system is an extend of multi-robot system with significant advantages: For one thing, it has good robustness, which means the swarm robots system can still complete the task even if some agents failed or disturbance emerges; For another thing it has strong adaptability, which means the swarm robots system have the ability to solve different tasks; and also it has extendibility, which means the system can move on normally with increase or decrease in the number of system agents.
The research work on swarm robot system is of great importance both in theory and practice. It can help to reveal the essence of complex collective behavior in nature, and can imitate the intelligent behavior of biological beings perfectly. And with the improvement of manufacturing technology and decrease in cost for the development of science and technology, there will be wide application of swarm robot system in the future battlefield, the medical field, the aerospace and industrial field.
The flocking behavior of swarm robot system is studied in this thesis. And the brief content of the thesis is as follows:
Firstly, the fuzzy neighborhood between agents is described. The distance between agent i and its local neighbors are different, thus the effect of the control strategy of different neighbors are different on the agent. And it is necessary to have different intensities between agents. As fuzzy inputs, the distances between agents are utilized to obtain the intensities with neighbors through the fuzzy logic system. A control algorithm is designed to verify the fuzzy neighborhood of the swarm robot system. And the simulation results show that the swarm can achieve a stable structure and move synchronously. For further study of the robustness of the swarm robot system during the flocking behavior, simulations of the swarm system are performed with a failure agent, a communication failure agent and random disturbance, respectively. And the results demonstrate that the swarm has a good robustness using the controller put forward in this chapter.
Secondly, the split mechanism for swarm robot system is designed under the environment of multiple virtual leaders. Taking the requirements of the leaders and the tracking conditions of the neighbors into account, the ant colony algorithm is applied to calculate the probability for agents to track the leaders and the roulette wheel method is used to determine the tracking target when there are multiple virtual leaders in the environment which need to be tracked separately. The attraction/ repulsion force between agents which have different leaders is deduced based on the force between agents which have the same leader. Under the effect of the attraction/ repulsion force, the swarm can split into different groups to follow different leaders. The simulation results reveal that the swarm can effectively split into different groups according to the leader’s needs under the proposed split algorithm.
Then, the controller is designed for the swarm robot system in an environment with unknown obstacles or damping factors. When there are obstacles (one or more) in the environment, the traditional artificial potential field method is improved to achieve obstacle avoidance based on the number advantage of the swarm. During the collision avoiding process, the agent get repulsion force from the obstacles, and approach to the center of the neighbors which did not perceived obstacles. To validate the proposed algorithm, experimental simulation is performed. And the results show that the algorithm can make the swarm avoid obstacles effectively. When there are unknown damping factors in the environment, the velocity of swarm can hardly reach the convergence. Therefore, the fuzzy adaptive method is introduced to adjust the online parameters of the controller. An algorithm is designed to simulate the situation and the results indicate that this method can decrease the influence of the unknown damping factors, making the swarm converge to the desired speed quickly.
Finally, a novel communication strategy is proposed for the swarm robot system which has flocking behavior. As there is heavy communication traffic in the process that the swarm achieves collective behavior, particle swarm optimization algorithm is firstly used to get the best communicate frequency and reduce redundant communication. After that, the evaluation functions of the swarm structure and moving state are established. And different communication frequencies correspond to different evaluation values to further lighten the communication traffic. Experimental results demonstrate that the proposed strategy can reduce the communication while keeping the formed flocking behavior.
In summary, four problems of the flocking behavior of swarm robot system are studied in this thesis. The research work focuses on the controller design for flocking behavior of swarm robot system under different external environment, and also a communicate strategy is proposed to reduce redundant communication. Simulation experiments are done to validate the proposed algorithm.
对群体机器人系统进行研究具有重要的理论及工程意义。对群体机器人系统的研究越深入,越有助于揭示自然界中复杂集群行为的本质,能够更直观地再现生物群体的智能行为。并且随着科学技术的发展,机器人制造技术不断提高,制造成本不断减小,在未来战场、医疗领域、航天领域以及工业领域群体机器人系统都将有着广泛的应用。
本文对群体机器人系统的协同一致行为进行了研究,主要内容如下:
(1)、描述了个体间的模糊邻居关系。个体与其感知范围内的邻居间距大小不一,因而每个邻居在控制策略内部对个体的调整作用也不同,这就需要建立群体中个体间不同邻居强度关系。利用个体间距作为模糊输入,经模糊系统推理得出个体间的邻居强度关系。在此基础上设计了群体协同一致行为控制算法,仿真结果表明群体能够有效地形成稳定结构并同步运行。为了进一步研究群体协同运行时系统的鲁棒性,分别对系统中出现完全失效个体、通信失效个体以及外界随机干扰情况进行了仿真实验。仿真结果表明,在文中给出的控制器作用下,群体具有很好的鲁棒性。
(2)、设计了多个虚拟领导者环境下群体的分裂机制。当环境中出现多个虚拟领导者需要分别跟踪时,个体根据领导者的需求以及周围邻居的跟踪情况利用蚁群算法计算对不同领导者的跟踪概率,并利用轮盘赌的方法确定不同个体的跟踪目标。基于跟踪相同领导者个体间的作用力函数设计了跟踪不同领导者个体间的相互作用力函数,实现了群体分裂成不同子群体的目的,并分别对不同的领导者进行跟踪。仿真结果表明所提出的分裂算法能有效地使群体按照领导者需求分裂成不同大小的子群体对不同的领导者分别进行跟踪。
(3)、针对障碍物环境以及未知阻尼环境设计了群体协同策略。当环境中存在一个或多个障碍物时,基于群体的数量优势改进传统人工势场法实现对障碍物的避碰。避碰过程中,障碍物对个体产生排斥作用,同时个体向周围没有感知到障碍物的邻居中心靠近。通过仿真实验,所提出的算法能够有效地实现群体系统对障碍物的避碰。未知环境阻尼将影响群体速度的收敛,利用模糊自适应整定控制器的方法在线调整控制参数。仿真结果表明,这一方法能有效减小未知阻尼的影响,使群体系统快速收敛至期望的运行速度。
(4)、提出了一种新的应用于群体协同一致行为中的通信策略。针对群体协同行为过程中通信量大的缺陷,首先利用粒子群优化算法对群体运行过程的通信量进行优化,得到最佳通信频率,从而达到了减少冗余通信量的目的。然后,通过建立群体结构以及运行状态的评价函数,在不同的评价值时对应采用不同的通信频率,以进一步降低群体在协同行为中的通信量。经仿真实验验证,所提策略能在不影响群体协同一致运行的基础上有效地降低系统的通信量。
综上所述,本文针对群体协同一致行为的四个问题进行了研究,主要工作着眼于不同外界环境中群体协同行为控制策略的设计,并提出减少系统中冗余的通信量的策略,仿真实验验证了所提算法的有效性。
Flocking behavior is a common phenomenon in nature, such as flocks of birds, schools of fish, and colonies of bacteria. It is found that flocking behavior makes the individuals of the swarm have certain advantages over single individual in many kinds of task, for example, avoiding predators, finding food, and completing some complex task which single individual can never achieved.
Inspired by the flocking behavior in nature, the complex behavior of the swarm robot system is studied through local interaction between simple agents. Swarm robot system is an extend of multi-robot system with significant advantages: For one thing, it has good robustness, which means the swarm robots system can still complete the task even if some agents failed or disturbance emerges; For another thing it has strong adaptability, which means the swarm robots system have the ability to solve different tasks; and also it has extendibility, which means the system can move on normally with increase or decrease in the number of system agents.
The research work on swarm robot system is of great importance both in theory and practice. It can help to reveal the essence of complex collective behavior in nature, and can imitate the intelligent behavior of biological beings perfectly. And with the improvement of manufacturing technology and decrease in cost for the development of science and technology, there will be wide application of swarm robot system in the future battlefield, the medical field, the aerospace and industrial field.
The flocking behavior of swarm robot system is studied in this thesis. And the brief content of the thesis is as follows:
Firstly, the fuzzy neighborhood between agents is described. The distance between agent i and its local neighbors are different, thus the effect of the control strategy of different neighbors are different on the agent. And it is necessary to have different intensities between agents. As fuzzy inputs, the distances between agents are utilized to obtain the intensities with neighbors through the fuzzy logic system. A control algorithm is designed to verify the fuzzy neighborhood of the swarm robot system. And the simulation results show that the swarm can achieve a stable structure and move synchronously. For further study of the robustness of the swarm robot system during the flocking behavior, simulations of the swarm system are performed with a failure agent, a communication failure agent and random disturbance, respectively. And the results demonstrate that the swarm has a good robustness using the controller put forward in this chapter.
Secondly, the split mechanism for swarm robot system is designed under the environment of multiple virtual leaders. Taking the requirements of the leaders and the tracking conditions of the neighbors into account, the ant colony algorithm is applied to calculate the probability for agents to track the leaders and the roulette wheel method is used to determine the tracking target when there are multiple virtual leaders in the environment which need to be tracked separately. The attraction/ repulsion force between agents which have different leaders is deduced based on the force between agents which have the same leader. Under the effect of the attraction/ repulsion force, the swarm can split into different groups to follow different leaders. The simulation results reveal that the swarm can effectively split into different groups according to the leader’s needs under the proposed split algorithm.
Then, the controller is designed for the swarm robot system in an environment with unknown obstacles or damping factors. When there are obstacles (one or more) in the environment, the traditional artificial potential field method is improved to achieve obstacle avoidance based on the number advantage of the swarm. During the collision avoiding process, the agent get repulsion force from the obstacles, and approach to the center of the neighbors which did not perceived obstacles. To validate the proposed algorithm, experimental simulation is performed. And the results show that the algorithm can make the swarm avoid obstacles effectively. When there are unknown damping factors in the environment, the velocity of swarm can hardly reach the convergence. Therefore, the fuzzy adaptive method is introduced to adjust the online parameters of the controller. An algorithm is designed to simulate the situation and the results indicate that this method can decrease the influence of the unknown damping factors, making the swarm converge to the desired speed quickly.
Finally, a novel communication strategy is proposed for the swarm robot system which has flocking behavior. As there is heavy communication traffic in the process that the swarm achieves collective behavior, particle swarm optimization algorithm is firstly used to get the best communicate frequency and reduce redundant communication. After that, the evaluation functions of the swarm structure and moving state are established. And different communication frequencies correspond to different evaluation values to further lighten the communication traffic. Experimental results demonstrate that the proposed strategy can reduce the communication while keeping the formed flocking behavior.
In summary, four problems of the flocking behavior of swarm robot system are studied in this thesis. The research work focuses on the controller design for flocking behavior of swarm robot system under different external environment, and also a communicate strategy is proposed to reduce redundant communication. Simulation experiments are done to validate the proposed algorithm.
引文
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