S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameter...S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.展开更多
This paper presents a self-assembly control strategy for the swarm modular robots. Simulated and physical experiments are conducted based on the Sambot platform, which is a novel self-assembly modular robot having the...This paper presents a self-assembly control strategy for the swarm modular robots. Simulated and physical experiments are conducted based on the Sambot platform, which is a novel self-assembly modular robot having the characteristics of both the chain-type and the mobile self-reconfigurable robots. Multiple Sambots can autonomously move and connect with one another through self-assembly to form robotic organisms. The configuration connection state table is used to describe the configuration of the robotic structure. A directional self-assembly control model is proposed to perform the self-assembly experiments. The self-assembly process begins with one Sambot as the seed, and then the Docking Sambots use a behavior-based controller to achieve connection with the seed Sambot. The controller is independent of the target configuration. The seed and connected Sambots execute a configuration comparison algorithm to control the growth of the robotic structure. Furthermore, the simul- taneous self-assembly of multiple Sambots is discussed. For multiple configurations, self-assembly experiments are conducted in simulation platform and physical platform of Sambot. The experimental results verify the effectiveness and scalability of the self-assembly algorithms.展开更多
基金Supported by the 863 Project under Grant No.2008AA092301the Fundamental Research Foundation of Harbin Engineering University under Grant No.2007001
文摘S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.
基金supported by the National High Technology Research and Development Program of China ("863" Program) (Grant Nos. 2009AA043901 and 2012AA041402)National Natural Science Foundation of China (Grant No. 61175079)+1 种基金Fundamental Research Funds for the Central Universities (Grant No. YWF-11-02-215)Beijing Technological New Star Project (Grant No. 2008A018)
文摘This paper presents a self-assembly control strategy for the swarm modular robots. Simulated and physical experiments are conducted based on the Sambot platform, which is a novel self-assembly modular robot having the characteristics of both the chain-type and the mobile self-reconfigurable robots. Multiple Sambots can autonomously move and connect with one another through self-assembly to form robotic organisms. The configuration connection state table is used to describe the configuration of the robotic structure. A directional self-assembly control model is proposed to perform the self-assembly experiments. The self-assembly process begins with one Sambot as the seed, and then the Docking Sambots use a behavior-based controller to achieve connection with the seed Sambot. The controller is independent of the target configuration. The seed and connected Sambots execute a configuration comparison algorithm to control the growth of the robotic structure. Furthermore, the simul- taneous self-assembly of multiple Sambots is discussed. For multiple configurations, self-assembly experiments are conducted in simulation platform and physical platform of Sambot. The experimental results verify the effectiveness and scalability of the self-assembly algorithms.
