An Approach to Optimize the Path of Humanoids using Adaptive Ant Colony Optimization  

作　　者：Chinmaya Sahu Dayal R. Parhi Priyadarshi Biplab Kumar 

机构地区：[1]Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela-769008, Odisha, India

出　　处：《Journal of Bionic Engineering》2018年第4期623-635,共13页仿生工程学报（英文版）

摘　　要：In the emerging area of humanoid robotics, path planning and autonomous navigation have evolved as one of the most promising area of research. This paper deals with the design and development of a novel navigational controller to guide humanoids in cluttered envi- ronments. The basic parameters of the ant colony optimization technique have been modified to have enhanced control as Adaptive Ant Colony Optimization （AACO）. The controller that has been implemented in the humanoids receives sensory information about obstacle distances as inputs and provides required turning angle as output to reach the specified target position. The proposed controller has been tested in both simulated and experimental environments created trader laboratory conditions, and a good agreement has been observed between the simulation and experiment results. Here, both static and dynamic path planning have been attempted. Finally, the proposed controller has also been tested against other existing techniques to validate the efficiency of the AACO in path planning problems.In the emerging area of humanoid robotics, path planning and autonomous navigation have evolved as one of the most promising area of research. This paper deals with the design and development of a novel navigational controller to guide humanoids in cluttered envi- ronments. The basic parameters of the ant colony optimization technique have been modified to have enhanced control as Adaptive Ant Colony Optimization （AACO）. The controller that has been implemented in the humanoids receives sensory information about obstacle distances as inputs and provides required turning angle as output to reach the specified target position. The proposed controller has been tested in both simulated and experimental environments created trader laboratory conditions, and a good agreement has been observed between the simulation and experiment results. Here, both static and dynamic path planning have been attempted. Finally, the proposed controller has also been tested against other existing techniques to validate the efficiency of the AACO in path planning problems.

关 键 词：navigation path planning humanoid robot AACO PETRI-NET BIONICS 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] P444[自动化与计算机技术—计算机科学与技术]