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
Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations dur...Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.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.
文摘Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.
