Adaptive Locomotive Walking Mechanism with Self-Learning Capability

This project integrates self-training robots with IMU-based navigation, developed with ESP32 and ROS 2 off-board computation for adaptive locomotion enhancement. Gesture-based robots, the norm in the field, are not adaptive, especially on rough terrain. With reinforcement learning (RL) techniques such as Q-learning and an MPU-6050 IMU and HC-SR04 ultrasonic sensor, the project introduces an adaptive self-training system with 30-40% enhanced stability [1]. ROS 2 off-loads computationally heavy tasks from ESP32 for scalable learning [2]. Experimental results demonstrate improved stability, navigation accuracy, and obstacle-handling efficiency, making this design suitable for autonomous terrain exploration, industrial monitoring, and search-and-rescue operations. The project overcomes the limitation of typical gesture-controlled robots, being either wheeled or stationary, with an adaptive walk mechanism that maximizes stability and efficiency [3]. Processing and control of the system is carried out on an ESP32 microcontroller that acquires IMU data, recycles motion autonomously, and interacts with ROS 2 for off-board reinforcement learning [4]. An MPU-6050 IMU for real-time correction of tilt and an HC-SR04 ultrasonic sensor for collision avoidance further enhance navigation capabilities [5]. Early experiments indicate that the technique achieves accurate gesture-based learning, stable locomotion, and efficient communication, with an improvement of 30-40% in stability compared to traditional systems [6]. The paper makes a contribution to the advancements in autonomous robotic locomotion by incorporating self-training characteristics with robust navigation methods, which can have applications in agriculture, exploration, and assistive technology.