Autonomous biped

Autonomous Biped With Self Maneuvering Ability

Project Guide:Ms. Sujata Dubal

Asst. Professor – Electronics Department

Group Members:Nidhi Khetan (33)

Prasad Dugad (15)Yash Sanghvi (48)

Contents

Abstract Controlling a biped robot with a high degree of freedom to achieve

stable and straight movement patterns is a complex problem The Kinematic Equations for the standard 7 DOF kinematic structure

have been established, but the complex nature of the equation make it hard to control

In this project we aim a design of a Partial Passive control of a 4-DOF biped that allows precise control without using complex equations

Introduction Bipedal Locomotion

The technique of using legged mechanism instead of wheels Inspired from the Bipedal Trajectile motion of humans and

similar creatures to develop such systems for specialized application

Biped Implementation: Controlling Methods include: Passive Dynamic Walking Motion

and Gait control – both have their disadvantages

Introduction(Cont’d) Partial Passive Control:

Effective combination of Both Passive Dynamic Walking control and Gait control can allow convenient control.

Active control is achieved whereas the number of motors to be controlled are reduced – easier implementation.

Problem Statement The Aim of the project is to implement a Bipedal Structure that can:

Move using motor control to generate authentic bipedal motion Detect and avoid obstacles during locomotion – capable of

detecting left , right and centre obstacles

Literature Survey

Uses one leg actively controlled by motors and one uncontrolled leg acting as a strut - Reduces the structure to 4-DOF robot

Equations are reduced to three 2x2 matrix equations - same can be verified by simple trigonometry:

Principle Of Operation

Principle Of Operation

Block Diagram of Project

Functions Receives the information from the sensor control board after it

has detected an obstacle. Generates the PWM waves and transmits them to the motor

control board. Microcontroller used is ATMEGA 16

Component Description AVR Based Mother Board

Sensor Control Board

  Functions The high precision IR receiver detects an IR signal The module consists of 358 comparator IC. The

output of sensor is low whenever it receives IR frequency and high otherwise

The power consumption of this module is low. It gives a digital output

Component Description (Cont’d)

Servo MotorComponent Description (Cont’d)

Check Obstacle Presence

Start

Wait till zero point reference created

Get real time data

Forward Move right till no obstacle

Check for Way out

Move left till no obstacle

Repeat

YesNo

NoneLeft Centre

Right

Flow Chart of the Project

Results and DiscussionAnkle Motor

AngleKnee Motor

AngleThigh Motor

AngleStep 1 150 140 175

Step 2 140 95 125

Step 3 165 80 125

Step 4 165 80 175

Step 5 165 110 175

Angle of the three motors for the 5 steps

Results and Discussion (Cont’d)

Angle v/s Time graph for Forward motion

Results and Discussion (Cont’d)

Angle v/s Time graph for Right motion (Left Obstacle)

Results and Discussion (Cont’d)

Angle v/s Time graph for Left motion (Right Obstacle)

Matlab Simulation of Biped Walking Motion

Results and Discussion (Cont’d)

Biped Walking Motion

Results and Discussion (Cont’d)

Obstacle Detection

Results and Discussion (Cont’d)

Conclusion The aim of the project was to construct a prototype of an autonomous biped with additional features like obstacle detection systems and thus enabling it to be used for research and developmental purposes. Implementing these on a small scale prototype will make it easier to adapt to these add-ons in a larger model of the project. It also lead to better understanding of the different parts, their working with each other and the processes involved in addition of previously mentioned features.

Advantage And Disadvantage Advantages

• Autonomy of action• Simple algorithm

Disadvantages• Motion on rugged surfaces• External IR inference due to other sources

Application Thus we are trying to implement an autonomous biped which has

following applications:• Autonomous Manoeuvring of vehicles• Obstacle Detection

Future Scope

Real life model can be implemented. The Wireless module (without adaptor) can be upgraded. Identification of the terrain and recognizing the nature of the

environment in which the robot is supposed to move and it can be done with the help of camera (either single or double) system mounted on board or off shelf. Speed of calculation can be increased and the time to

process can be reduced using a faster microcontroller. 

AcknowledgementThe timely completion of this report is mainly due to the interest and persuasion of Prof Sujata Dubal (A.P.) who provided us with guidance and motivation throughout its making. We also thank her for giving us an opportunity to create this project. We are also thankful to our HOD Poorva Waingankar and also our project cordinator Ms. Sonal Barvey for their guidance.  Finally we would thank our college Thakur College of Engineering and Technology for providing us with a platform and the necessary facilties to make this project.

Publications[1]. Prasad Dugad, Nidhi Khetan, Yash Sanghvi, “Autonomous Biped – A Review” in Multicon-W 2014, ICWAC, ISBN 978-0-9884925- 4-7, Vol. 1 PP. 376-379, February 2014.

[1] Fumihiko Asano, Zhi-Wei Luo, and Masaki Yamakita,.” Biped Gait Generation and Control Based on a Unified Property of Passive Dynamic Walking”, IEEE Transactions On Robotics, VOL. 21, NO. 4, August 2005[2] Jong Hyeon Park, Member, IEEE, and Eung Seo Kim , “Foot and Body Control of Biped Robots to Walk on Irregularly Protruded Uneven Surfaces”, IEEE Transactions On Systems, Man, And Cybernetics—PART CYBERNETICS, VOL. 39, NO. 1, February 2009 [3] Hyung-Kew Lee, Sun-Il Chang, and Euisik Yoon, “Dual-Mode Capacitive Proximity Sensor for Robot Application: Implementation of Tactile and Proximity Sensing Capability on a Single Polymer Platform Using Shared Electrodes”, IEEE Sensors Journal, VOL. 9, NO. 12, December 2009.[4] Nima Shafii1, Siavash Aslani1, Omid Mohamad Nezami1, Saeed Shiry2, “Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization”, Mechatronics Research Laboratoy (MRL), Department of Computer and Electerical Engineering, Qazvin Islamic Azad, April 2010.sing Sensor Fusion, Kalman Filter, and Fuzzy Logic”, IEEE Transactions on Industrial Electronics, VOL. 59, NO. 11, November 2012.

Reference

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