Human-inspired dynamic obstacle and inter-collision avoidance algorithm for humanoid biped robots

In order to maximize humanoid robot navigation, this paper introduces the Enhanced DAYANI Arc Contour Intelligent (EDACI) Method, which integrates Dynamic Window Approach (DWA) to choose the best walking parameters for avoiding obstacles and smooth trajectory management. EDACI algorithm provides the best response to guide humanoid robots to the goal by avoiding obstacles and preparing a smooth trajectory. Further, DWA optimizes the walking pattern of humanoid robots by controlling their velocity while encountering an obstacle and finding a smooth trajectory. The performance of the proposed controller is examined by implementing it in humanoid NAOs for navigation in several simulated and experimental terrains. It is implemented on a single humanoid robot for navigation in static and dynamic environments and on multiple humanoid robots on a single platform. Navigation of multiple robots has to deal with the situation of conflict where one robot behaves as a dynamic obstacle to the other. It is solved by setting a Dining Philosopher Controller (DPC) in the base technique. The results obtained from the simulations and experiments have a divergence below 5 %, which demonstrates a satisfactory relation between them. The proposed controller's efficacy is demonstrated by comparing the torque developed at different joints with contrast to the inbuilt controller of NAO. The results show good improvement in torque produced at all joints. In addition, it is compared with an existing controller for navigation, which displays superiority of the proposed controller.