An Improved Fast Flocking Algorithm with Obstacle Avoidance for Multiagent Dynamic Systems

Abstract

Flocking behavior is a common phenomenon in nature, such as flocks of birds and groups of fish. In order to make the agents effectively avoid obstacles and fast form flocking towards the direction of destination point, this paper proposes a fast multiagent obstacle avoidance (FMOA) algorithm. FMOA is illustrated based on the status of whether the flocking has formed. If flocking has not formed, agents should avoid the obstacles toward the direction of target. If otherwise, these agents have reached the state of lattice and then these agents only need to avoid the obstacles and ignore the direction of target. The experimental results show that the proposed FMOA algorithm has better performance in terms of flocking path length. Furthermore, the proposed FMOA algorithm is applied to the formation flying of quad-rotor helicopters. Compared with other technologies to perform the localization of quad-rotor helicopter, this paper innovatively constructs a smart environment by deploying some wireless sensor network (WSN) nodes using the proposed localization algorithm. Finally, the proposed FMOA algorithm is used to conduct the formation flying of these quad-rotor helicopters in the smart environment.

Development of a hybrid multi-layer control architecture for a cooperative team of N − homogeneous robots

“…The data communication was implemented by DDS RTI middleware via publishsubscribe communication model and the formation control scheme was implemented in C++ language. Wang et al (2014) validated the proposed behavior-based formation control architecture where ArduCopters were used as the quadrotor helicopter platform in the constructed smart indoor environment. The data and video frames were transmitted to remote PC by Video Transmitter Kit where the controller was implemented sending back the control signals through Radio Telemetry Kit.…”

Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

“…Despite significant modifications in their areas of implementation, both modeling methods are almost compatible. Certain unique features such as collision prevention were carried out with improved variants of comparable algorithms [21]. Similarly, Yuan et al [22] used a decentralized predictive control algorithm in the broadcast network mode with the ability to form a certain equilibrium range, analogous to virtual force models.…”

Multi-Restricted Area Avoidance Scenario Using Hybrid Dynamical Model and Its Predictive Controller

“…There are some published articles which described the restricted use of some systems, such as vehicles and mobile robots [6][7][8][9][10][11]. The more complicated problem comprises of several plants which are controlled by applying a multi-agent concept like flocking scheme, which was used in [12,13].…”