scholarly journals Multitarget Search of Swarm Robots in Unknown Complex Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
You Zhou ◽  
Anhua Chen ◽  
Hongqiang Zhang ◽  
Xin Zhang ◽  
Shaowu Zhou
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Force Model ◽  
Complex Environments ◽  
Target Response ◽  
Complex Environment ◽  
Multiple Targets ◽  
Virtual Force ◽  
Swarm Robots ◽  
Division Model

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

Knowledge sharing, complex environments and small-worlds

2005 ◽  
Vol 24 (3) ◽  
pp. 185-195
Author(s):  
Mike Metcalfe
Keyword(s):  
Knowledge Sharing ◽  
System Thinking ◽  
Small Worlds ◽  
Complex Environments ◽  
Complex Environment ◽  
Strategic Response ◽  
Social Species ◽  
The Social ◽  
Self Organisation ◽  
Do So

This paper is about knowledge sharing vision appropriate for a complex environment. In these environments, traditional views of knowledge sharing as informing a hierarchical, centralised leadership may be misleading. A complex environment is defined as one that emerges unpredictable changes that require organisations to reconnect, to reorganise. Organisations need to be able to rapidly reconnect relationships so as to reflect new priorities, and to do so without causing change “bottlenecks”. The empirical biologists have observed that some social species have evolved structures that enable them to do this automatically what ever the environmental change. These organisational forms have survived for millions of years without central planning; rather they use local knowledge is reconnect as required overall providing an appropriate strategic response. These organisational forms seem to result from the small-worlds phenomenon and it is self organising. Specifically, this paper will argue that this small-worlds, self organisation, phenomena is a useful vision for designing a knowledge sharing vision appropriate for a complex environment. The supportive evidence is provided in the form of identifying the empirical attributes of self organisation and small worlds to provide an explanation of how and why it works. The system thinking, biology (insect) and the social-network literature are used.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

Discrete Time-Delay Optimal Control Method for Experimental Active Chatter Suppression and Its Closed-Loop Stability Analysis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Xingwu Zhang ◽  
Ziyu Yin ◽  
Jiawei Gao ◽  
Jinxin Liu ◽  
Robert X. Gao ◽  
...  
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Control Method ◽  
Trial And Error ◽  
Stability Lobe Diagram ◽  
Weighting Matrix ◽  
Chatter Suppression ◽  
Stability Lobe ◽  
Optimal Control Method ◽  
Discrete Time Delay

Chatter is a self-excited and unstable vibration phenomenon during machining operations, which affects the workpiece surface quality and the production efficiency. Active chatter control has been intensively studied to mitigate chatter and expand the boundary of machining stability. This paper presents a discrete time-delay optimal control method for chatter suppression. A dynamical model incorporating the time-periodic and time-delayed characteristic of active chatter suppression during the milling process is first formulated. Next, the milling system is represented as a discrete linear time-invariant (LTI) system with state-space description through averaging and discretization. An optimal control strategy is then formulated to stabilize unstable cutting states, where the balanced realization method is applied to determine the weighting matrix without trial and error. Finally, a closed-loop stability lobe diagram (CLSLD) is proposed to evaluate the performance of the designed controller based on the proposed method. The CLSLD can provide the stability lobe diagram with control and evaluate the performance and robustness of the controller cross the tested spindle speeds. Through many numerical simulations and experimental studies, it demonstrates that the proposed control method can make the unstable cutting parameters stable with control on, reduce the control force to 21% of traditional weighting matrix selection method by trial and error in simulation, and reduce the amplitude of chatter frequency up to 78.6% in experiment. Hence, the designed controller reduces the performance requirements of actuators during active chatter suppression.

scholarly journals Energy-efficient non-linear k-barrier coverage in mobile sensor network

2020 ◽  
Vol 17 (3) ◽  
pp. 737-758
Author(s):  
Zijing Ma ◽  
Shuangjuan Li ◽  
Longkun Guo ◽  
Guohua Wang
Keyword(s):  
Sensor Network ◽  
Mobile Sensors ◽  
Barrier Coverage ◽  
Force Model ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Virtual Force ◽  
Straight Line ◽  
Non Linear ◽  
Single Sensor

K-barrier coverage is an important coverage model for achieving robust barrier coverage in wireless sensor networks. After initial random sensor deployment, k-barrier coverage can be achieved by moving mobile sensors to form k barriers consisting of k sensor chains crossing the region. In mobile sensor network, it is challenging to reduce the moving distances of mobile sensors to prolong the network lifetime. Existing work mostly focused on forming linear barriers, that is the final positions of sensors are on a straight line, which resulted in large redundant movements. However, the moving cost of sensors can be further reduced if nonlinear barriers are allowed, which means that sensors? final positions need not be on a straight line. In this paper, we propose two algorithms of forming non-linear k barriers energy-efficiently. The algorithms use a novel model, called horizontal virtual force model, which considers both the euclidean distance and horizontal angle between two sensors. Then we propose two barrier forming algorithms. To construct a barrier, one algorithm always chooses the mobile sensor chain with the largest horizontal virtual force and then flattens it, called sensor chain algorithm. The other chooses the mobile sensor with the largest horizontal virtual force to construct the barrier, other than the mobile sensor chain, called single sensor algorithm. Simulation results show that the algorithms significantly reduce the movements of mobile sensors compared to a linear k-barrier coverage algorithm. Besides, the sensor chain algorithm outperforms the single sensor algorithm when the sensor density becomes higher.

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