Abstract

Robot Control Systems form the backbone of modern robotics, enabling autonomous and semi-autonomous machines to perform tasks with precision, flexibility, and safety. This publication introduces the design and simulation of a modular robot control system capable of managing motion planning, sensor integration, and task execution. The framework focuses on flexibility, real-time adaptability, and ease of integration with various robotic platforms.

Introduction

Robots are increasingly deployed across industries such as manufacturing, logistics, healthcare, and service applications. A robust control system ensures safe operation and efficient task performance. Traditional robot control relied on predefined commands, but modern approaches integrate sensors, AI, and feedback loops for dynamic adaptation. This work presents a layered control system model with modular components for motor control, perception, and decision-making.

Methodology

The proposed control system consists of:

Low-Level Control: Handles motor drivers, encoders, and PID controllers for precise motion.

Mid-Level Control: Processes sensor inputs (e.g., LiDAR, cameras, IMUs) and applies SLAM (Simultaneous Localization and Mapping).

High-Level Control: Implements AI/ML for path planning, obstacle avoidance, and task scheduling.

Experiments

The system was simulated in Gazebo and tested on a differential-drive robot. Scenarios included simple navigation, obstacle avoidance, and waypoint-based movement. Performance was evaluated by tracking path deviation, execution time, and collision rate.

Results

PID-based low-level control maintained trajectory within ±2 cm deviation.

Path planning algorithms (A*, Dijkstra) successfully avoided dynamic obstacles.

Integration with LiDAR sensors enabled real-time collision avoidance with 95% reliability

Conclusion

The proposed robot control system framework demonstrates the importance of modularity, real-time responsiveness, and AI integration in modern robotics. With continued development, it can support industrial, service, and autonomous robotics applications.

References