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 c...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.展开更多
文摘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.
