Six-legged autonomous robot with sophisticated control systems and sensor fusion
The Advanced Hexapod Robot Platform represents a sophisticated integration of mechanical engineering, control systems, and artificial intelligence. This six-legged autonomous robot demonstrates advanced locomotion capabilities through real-time inverse kinematics, dynamic gait generation, and comprehensive sensor fusion using Kalman filtering techniques.
Inspired by nature's most efficient walkers, this hexapod robot addresses the challenge of navigating complex, uneven terrain where wheeled vehicles fail. The project showcases bio-inspired robotics principles while implementing cutting-edge control algorithms for stable, adaptive locomotion in real-world environments.
We selected Raspberry Pi for its balance of computational power and energy efficiency, essential for mobile robotics. ROS 2 provided the robust middleware for sensor integration and navigation stack, while Blender was used for creating detailed 3D models and URDF files for accurate kinematic simulation.
Complete hexapod platform with all sensors and actuators
Placeholder: ../images/robotics/hexapod-assembled.jpgHexapod robot demonstrating various gait patterns
Placeholder: ../videos/robotics/hexapod-walking-demo.mp4CAD model showing leg mechanisms and joint structure
Placeholder: ../images/robotics/hexapod-cad-design.pngBlock diagram of the control system and sensor integration
Placeholder: ../images/robotics/hexapod-control-diagram.pngRobot navigating around obstacles using sensor feedback
Placeholder: ../videos/robotics/hexapod-obstacle-navigation.mp4Data visualization of different walking gaits and stability
Placeholder: ../images/robotics/hexapod-gait-analysis.pngThe hexapod employs a distributed control architecture with a central Raspberry Pi managing high-level decision making and six servo controllers handling individual leg movements. The system implements a three-layer hierarchy: reactive control for immediate responses, tactical control for gait coordination, and strategic control for path planning and navigation.
Each leg implements a 3-DOF (degrees of freedom) kinematic chain with coxa, femur, and tibia segments. The inverse kinematic solution uses geometric methods combined with iterative optimization to ensure reachable positions while maintaining joint constraints. The forward kinematics model validates leg positions and provides feedback for closed-loop control.
Computing inverse kinematics for six legs simultaneously while maintaining real-time performance (50Hz control loop) on limited hardware proved computationally intensive. We solved this by optimizing algorithms using lookup tables for trigonometric functions, implementing multi-threading for parallel leg computation, and using approximation methods where precision requirements allowed.
Maintaining balance while transitioning between different gait patterns, especially during turns and speed changes, required sophisticated stability analysis. We implemented a dynamic stability margin calculation based on zero moment point (ZMP) theory, combined with predictive control to adjust gait parameters in real-time for optimal stability.
Raw sensor data from IMU, ultrasonic sensors, and encoders contained significant noise that affected control precision. Our solution involved implementing an Extended Kalman Filter (EKF) that models sensor uncertainties and provides smooth, reliable state estimates for position, orientation, and velocity.
This project provided comprehensive experience in multidisciplinary engineering, combining mechanical design, embedded programming, and advanced control theory. I gained deep understanding of kinematic modeling, real-time systems programming, and the practical challenges of implementing theoretical algorithms in physical systems. The project significantly enhanced my skills in sensor integration, signal processing, and robotics system architecture.
Future enhancements would include implementing adaptive gait learning using reinforcement learning algorithms, integrating computer vision for enhanced environmental perception, and developing energy-efficient control strategies. Adding force sensors to the feet would enable more sophisticated terrain adaptation and improved stability on uneven surfaces.
This hexapod robot project exemplified effective interdisciplinary collaboration, combining expertise in mechanical design, electrical engineering, and software development to create a complex robotic system.
Team Members:
Supervisor: Dr. Eman Omar — +962 77 105 0610
Institution: Hashemite University, Mechatronics Engineering Department
We followed an iterative design process with clearly defined milestones: mechanical prototype, electronics integration, basic control implementation, and advanced feature development. Regular testing sessions and design reviews ensured that mechanical, electrical, and software components worked harmoniously together.