Design and Simulation of Bio-Inspired Robotic Systems for Autonomous Navigation in Complex Environments Using Multisensory Data Fusion

https://doi.org/10.5281/zenodo.20279784

Abstract

Autonomous navigation in complex and unstructured environments remains a critical challenge in modern robotics due to dynamic obstacles, sensor uncertainty, and nonlinear system behavior. This paper presents the design and simulation of a bio-inspired robotic system that integrates multisensory data fusion with an intelligent hybrid control architecture to achieve robust and adaptive navigation. Inspired by biological organisms that effectively process and respond to diverse sensory inputs, the proposed system combines visual, inertial, and proximity sensors to enhance environmental perception and decision-making. A unified multisensory data fusion framework is developed to integrate heterogeneous sensor data for improved localization, mapping, and obstacle detection. To enable intelligent control, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based controller is employed for high-level decision-making under uncertainty, mimicking human-like reasoning and learning capabilities. This is further integrated with a Model Predictive Control (MPC) scheme for optimal trajectory planning in dynamic environments, while a Proportional–Integral–Derivative (PID) controller is utilized for low-level motor control and system stability. The proposed system is validated through simulation in complex virtual environments characterized by dynamic obstacles, irregular terrains, and varying sensory conditions. Experimental results demonstrate that the hybrid control strategy significantly enhances navigation accuracy, obstacle avoidance efficiency, and trajectory optimization compared to conventional single-controller approaches. Moreover, the integration of multisensory data fusion improves robustness against noise and environmental uncertainties. The findings highlight the effectiveness of combining bio-inspired design principles with intelligent hybrid control and multisensory fusion for developing advanced autonomous robotic systems. The proposed framework offers a scalable and efficient solution for real-world applications such as search and rescue, autonomous exploration, and smart mobility in complex environments.