This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provid...This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provides the stability analysis of the converging process by investigating the relationship between single agents, and the analysis result is proved by using the Lyapunov theory. Finally, the simulation results in two-dimensional space have confirmed the obtained theoretical results.展开更多
An improved cooperative tracking model is proposed, which is based on the local information between mutually observable individuals with global object information, and this model is used for scalable social foraging s...An improved cooperative tracking model is proposed, which is based on the local information between mutually observable individuals with global object information, and this model is used for scalable social foraging swarm. In this model, the 'follower' individuals in the swarm take the center of the minimal circumcircle decided by the neighbors in the positive visual set of individual as its local object position. We study the stability properties of cooperative tracking behavior of social foraging swarm based on Lyapunov stability theory. Simulations show that the stable cooperative tracking behavior of the global social foraging swarm can be achieved easily, and beautiful scalability emerge from the proposed model for social foraging swarm.展开更多
This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the ...This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.展开更多
The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,n...The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.展开更多
Ever since the concept of swarm intelligence was brought out, a variety of control algorithms for swarm robotics has been put forward, and many of these algorithms are stable enough and efficient. Most of the research...Ever since the concept of swarm intelligence was brought out, a variety of control algorithms for swarm robotics has been put forward, and many of these algorithms are stable enough and efficient. Most of the researches only take an invariable controller which functions through the whole stage into consideration, the situation in which controller changes over time is rarely taken into account. However, there are limitations for invariable controller dominated algorithms in practical situation,which makes them unable to meet changing environment. On the contrary, variable controller is more flexible and can be able to adapt to complex environment. Considering such advantages,a time-varying algorithm for swarm robotics is presented in this paper. The algorithm takes time as one of the independent variables so that the controller is no longer fixed through the time,but can be changed over time, which brings more choices for the swarm robot system. In this paper, some relevant simulations are designed to test the algorithm. Different control strategies are applied on the same flock during the time, and a more complex,flexible and practical control effect is acquired successfully.展开更多
为研究道路交通流特性,基于车载高精度GPS跟驰试验数据进行车辆跟驰建模研究,结合深度学习理论和数据驱动方法,构建了基于粒子群优化(particle swarm optimization,PSO)的长短期记忆(long short term memory,LSTM)车辆跟驰模型。首先,...为研究道路交通流特性,基于车载高精度GPS跟驰试验数据进行车辆跟驰建模研究,结合深度学习理论和数据驱动方法,构建了基于粒子群优化(particle swarm optimization,PSO)的长短期记忆(long short term memory,LSTM)车辆跟驰模型。首先,清洗和平滑车辆轨迹数据,并对驾驶特征行为参数及相关关系进行研究,如加速度、车头时距以及速度与跟驰距离特性关系等。在此基础上,制定跟驰状态筛选规则;其次,构建考虑时间序列的PSO-LSTM模型,识别跟驰数据样本集,将当前时刻的前车速度、车头间距和上一时刻的车头时距作为模型输入,预测当前时刻的跟驰车速度;接着,选用25辆车跟驰试验的高精度GPS数据验证PSO-LSTM模型性能;最后,为验证该模型的优越性,选用传统机器学习支持向量回归(support vector regression,SVR)模型以及深度学习LSTM模型作为对比。结果表明,基于粒子群优化的长短期记忆模型预测精度高达0.993,整体预测效果高于SVR模型和LSTM模型,其中预测误差指标平均绝对百分比误差(mean absolute percentage error,MAPE)较SVR和LSTM分别降低了60.02%和1.52%。PSO算法进行超参数优化后的PSO-LSTM模型能更好地模拟车辆的跟驰行为。展开更多
文摘This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provides the stability analysis of the converging process by investigating the relationship between single agents, and the analysis result is proved by using the Lyapunov theory. Finally, the simulation results in two-dimensional space have confirmed the obtained theoretical results.
基金Supported by National Natural Science Foundation of P. R. China (60274014)
文摘An improved cooperative tracking model is proposed, which is based on the local information between mutually observable individuals with global object information, and this model is used for scalable social foraging swarm. In this model, the 'follower' individuals in the swarm take the center of the minimal circumcircle decided by the neighbors in the positive visual set of individual as its local object position. We study the stability properties of cooperative tracking behavior of social foraging swarm based on Lyapunov stability theory. Simulations show that the stable cooperative tracking behavior of the global social foraging swarm can be achieved easily, and beautiful scalability emerge from the proposed model for social foraging swarm.
基金the National Natural Science Foundation of China (No.60374001,60334030)the Doctoral Fund of Ministry of Education of China (No.20030006003)the Commission of Science,Technology and Industry for National Defence (No.A2120061303)
文摘This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.
基金Major State Basic Research Development Program of China(No.2012CB720500)National Natural Science Foundations of China(Nos.61174118,21376077,61222303)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.
基金supported by the Beijing Municipal Natural Science Foundation(4152004)the National Natural Science Foundation of China(61204040)
文摘Ever since the concept of swarm intelligence was brought out, a variety of control algorithms for swarm robotics has been put forward, and many of these algorithms are stable enough and efficient. Most of the researches only take an invariable controller which functions through the whole stage into consideration, the situation in which controller changes over time is rarely taken into account. However, there are limitations for invariable controller dominated algorithms in practical situation,which makes them unable to meet changing environment. On the contrary, variable controller is more flexible and can be able to adapt to complex environment. Considering such advantages,a time-varying algorithm for swarm robotics is presented in this paper. The algorithm takes time as one of the independent variables so that the controller is no longer fixed through the time,but can be changed over time, which brings more choices for the swarm robot system. In this paper, some relevant simulations are designed to test the algorithm. Different control strategies are applied on the same flock during the time, and a more complex,flexible and practical control effect is acquired successfully.
