Multi‐Objective Route Outlining and Collision Avoidance of Multiple Humanoid Robots in a Cluttered Environment

Abstract

In robotics, navigating a humanoid robot through a cluttered environment is challenging. The present study aims to enhance the footstep and determine optimal paths regarding the robot's route length. The objective function for navigation of multiple humanoid robots is presented to optimize the route length and travel time. A hybrid technique using a probabilistic roadmap (PRM) and firefly algorithm (FA) is presented for humanoid robot navigation in a cluttered environment with static and dynamic obstacles. Sensory information, such as barrier range in the left, right, and front directions, is fed into the PRM framework that allows the humanoid robot to walk steadily with an initial steering angle. It finds the shortest path using the Bellman–Ford algorithm. The FA technique is used for efficient guidance and footstep modification in a cluttered environment to find a smooth and optimized path. To avoid static obstacles, the suggested hybrid technique provides optimum steering angles and ensures the minimum route length by taking the output of PRM as its input. A 3D simulator and a real‐world environment have been used for simulation and experiment in a cluttered environment utilizing the developed model and standalone methods. The humanoid robot achieves the target in all scenarios, but the FA‐tuned PRM technique is advantageous to this purpose, as shown by the convergence curve, route length, and travel duration. Multiple humanoid robot navigation has an additional self‐collision issue, which is eliminated by employing a dining philosopher controller as the base technique. In addition, the proposed controller is evaluated in contrast to the existing technique. The developed strategy ensures effectiveness and efficacy depending on these findings.