Two Wheel Feedback Stable System with Control Moment Gyroscope

This concept shows a self-balancing two-wheeled vehicle that is controlled by a control moment gyroscope module. These robots can move faster, turn sharper, and are more portable than other types of robots, such as four-legged robots and humanoid robots. As a result, two wheeled robots are the most

Project Title

Two Wheel Feedback Stable System with Control Moment Gyroscope

Project Area of Specialization

Robotics

Project Summary

This concept shows a self-balancing two-wheeled vehicle that is controlled by a control moment gyroscope module. These robots can move faster, turn sharper, and are more portable than other types of robots, such as four-legged robots and humanoid robots. As a result, two wheeled robots are the most practical alternative in this circumstance for serving as a platform. The disruptions and stabilisation for the two wheeled robots, on the other hand, constitute a disadvantage. When the robot is influenced by an unbalanced aggravation, the robot must stabilise itself and not collapse over. In the event that the aggravation exceeds the robot's reaction capabilities, the robot will become unstable. As a result, the robot's safety is jeopardized in such situations. To address these difficult issues, a robot was created that includes a control moment gyroscope module to improve balance. When an unsettling disturbance is applied to the robot, an inertial measurement unit (IMU) assesses the aggravation, and the control moment gyroscope subsequently settles the disturbance. The robot can keep up offset with just minor wheel movements because to the custom-designed CMG module. Tests and recreations were used to verify improved execution and strength.

When it comes to localization, positioning, and automated navigation, environmental mapping is one of the most significant components of robotics research. Range sensors and Lidar can be used to create a real-time high-resolution 3D mapping map of unstructured terrain. The main disadvantage of employing radar and sonar range sensors is that they have a low frequency and low precision due to their longer wavelength, which makes it impossible to create an exact 3D map. Lidar, on the other hand, will be utilised, which has a very high frequency and precision, making it particularly useful for measuring short distances. A pan tilt mechanism will be created for mounting sensors in order to create a precise 3D map using SLAM written in MATLAB and the Processing IDE.

Project Objectives

A two-wheeled self-balancing stabilising robot with smooth non-oscillating stabilisation performed by a control moment gyroscope will be constructed. It will not only address the issue of prior balancing techniques that relied solely on large body tilting and wheel movement, but it will also address unanticipated abrupt disturbances. Similarly, the major problem with using CMG modules is that they cannot deal with continuous disturbances. This problem will be handled in this project by changing the topography of numerous CMG modules to provide high linear torque for a longer length of time.Additionaly, a machine learning algorithm will be used to balance the two wheel robot to obtain a high accuracy.

Through 3D mapping techniques that use LIDAR and range sensors to construct high resolution maps for unstructured terrain, a real-time perception of the environment will be generated, making path planning, localization, and teleoperation easier for the robot. LIDAR sensors are ideal for robot mapping and navigation because of their high data density and accuracy. The mapping Lidar system will be designed in such a way that it will be able to represent both stationary and mobile portions of the environment simultaneously and in real-time. The LIDAR-based SLAM algorithm collects a series of scattered point cloud data with accurate angle and distance information collected by LIDAR, matches it using ICP (iterative nearest neighbour algorithm), and then calculates the distance and attitude change of LIDAR relative motion by matching point cloud data at different times to complete the localization of the mobile robot. The SLAM method will generate a 3D picture of the environment, which will be transferred to the MATLAB GUI for user efficiency and autonomous navigation via landmark and position state estimation. On the Processing IDE, a more in-depth study of the map generated will be performed in real time.

Project Implementation Method

A self-adjusting robot will be created like a Segway model which are driven by two high torque encoded DC motors, with a joined CMG module on the upper end associated with servo in halfway. The CMG module comprises gimbal and pivoting freewheel which will produce force in one course with explicit load analysis being done. Like this a second gimbal will be associated with another end for cancellation of forces and simplicity of turn. A control mode would be used for stabilization. At first PID would be proposed and at later stages slide mode control could be established. Control models will be made for convex, concave and hull-shaped obstacles. The algorithms and transfer functions will then be tested in MATLAB and changes will be made accordingly until the desired output of stability is achieved.

For a clear depth investigation of the environment and the generation of high resolution 3D maps, a 2D lidar will be employed. The LIDAR-based SLAM algorithm collects a series of scattered point cloud data with accurate angle and distance information collected by LIDAR, matches it using ICP (iterative nearest neighbour algorithm), and then calculates the distance and attitude change of LIDAR relative motion by matching point cloud data at different times to complete the localization of the mobile robot. In order to generate an exact point cloud, the servo motors in a pan-tilt system must be correctly positioned.

Benefits of the Project

The benefits of this project include :

• Integration into bicycles to create self-balancing bicycles that can help prevent fatal collisions.
•  3D Mapping and Navigations.
•  Autonomous Delivery.
• Terrain Mapping.
• Attitude Control in Satellites 

Technical Details of Final Deliverable

The final prototype will be a two wheeled robot with a CMG module consisting of dual CMG's and environment and depth perception capabilities using a camera and LiDAR range finder. The controller for the robot will employ an algorithm that uses torque from the wheels and CMG to stabilise the robot. With environment perception it will be able to judge how 'confined" the immediate space around it is, this will allow the robot to use the CMG to balance itself without displacing its position. The robot will also be able to use the CMG to negate any sudden disturbances which may directly act upon the robot frame.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Transportation

Other Industries

Core Technology

Others

Other Technologies

Artificial Intelligence(AI), Robotics

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

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