Summer Research Programme (SRP) - Proposal Form

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14-01-SRP

Summer Research Programme for Undergraduate Students Academic Year: 2015-2016 Is it a Resubmission?

Date: 4/18/2016 Yes ☐

No☒

Title of Proposed Project:

Intelligent exploration for an Autonomous Lawn Mower. Faculty Mentor: Professor Abu Bakr Muhammad Faculty Mentor – Email ID: [email protected]

Department / School: Electrical Engineering, SSE

Student Name (Team Lead): Mohammad Taha Ahmad School: SSE Email: [email protected]

Roll Number: 2017-10-0239 Major: EE Mobile Number: 0331 4969932

Student Name (Team Member 2): Muhammad Umar Javed School: SSE Email: [email protected]


Student Name (Team Member 3): Muhammad Barkat Saifee School: SSE Email: [email protected]


PROJECT DETAILS Project Summary

Consumer market for autonomous lawn mower is fast growing, where consumers are looking for products that assist their work in daily life. For this fast growing market, we aim to introduce a user friendly autonomous lawn mower which is capable of maintaining the lawn automatically. The autonomous lawn mower is a very easy to use product, where the user only needs to leave the grass mower in an open lawn. The bot itself plans how to efficiently mow the lawn using its intelligent algorithm. In future, we also aim to add further features to make it more user friendly. We plan to build a mobile application, from which the user can preplan the days the lawn should be mowed. The application also gives important data to the user, for example, it updates the user after completing a task. It also shows battery life and thereafter predicts the number of times it can be used again before recharging. We hope to launch this product in the market to transform the way grass is traditionally being mowed.

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Goals/Objectives/Hypothesis/Basis of Research

We propose to build a lawn mower which will mow the lawn using different sensor data and computer software algorithm. In this Summer we will achieve the following goals,  Implement an algorithm to optimally traverse the entire lawn.  Avoid obstacles in its way.  Terrain classification using laser sensors.  Make sure not to traverse the already mowed lawn.  Differentiate between moving and non-moving objects for safety measures.  Terrain exploration.  Minimal Power Consumption.

Introduction

In this technologically advanced era consumers are looking for products that are autonomous which help in saving their time and efforts. In the race for autotomizing things, we aim to introduce a user friendly autonomous lawn mower which is capable of maintaining the lawn automatically. The algorithm will differentiate between already mowed grass to save time and power consumption. The autonomous lawn mower does not require any prior external data for the lawn being mowed. It automatically collects data and creates a map in real time using Robot Operating System (ROS). The algorithm then uses this map to optimally traverse the entire lawn while also avoiding obstacles in its way. The autonomous lawn mower is an intelligent system employed in outdoor terrain to cut a certain patch of grass based on machine vision and mobile robotics. The previous final year project have been done using SLAM (simultaneous localization and mapping) on the terrain with the help of laser scanner and a low level control utilizing Arduino and UMI odometry, however, the implementation and testing of the robot was done in indoor alone. The mechanical infrastructure has been done and so are the machine circuits. The task here is to understand the already done work in ROS and simulate further algorithms in Gazeabo for grass detection and mowing. Laser scanner will be used to capture data which will be processed by blob algorithm to detect grass. Grass mowing will be done in an optimal way while keeping track of the area that has already been mowed. While keeping track of its current position, it will explore the terrain. There is one to one correspondence between position in mapping and either grass is mowed or not i.e. a position memory algorithm is to be established so that a point is not intercepted twice during exploration so that the whole map is traversed only once with maximum performance output. The other scheme which can be helpful is by modular inspection of laser scanner data on each input batch to divide the overall optimal problem into segments using greedy path

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tracking.

Background/Literature Review of the Research Problem(s) to be Addressed (Not to exceed two pages) A large amount of work has been done on autonomous robots to introduce the cognitive abilities of biological systems into artificial systems. Grass mower is one application of this phenomena and are one of the most in demand example of assistive robotics. Lawn mowers acquire unsupervised data from exteroceptive sensors and based on the retrieved data, compute an easiest path to traverse the terrain. The robot is based on highly advanced structure of software and hardware implementations, some of which are discussed as under: ROS (Robot Operating System) is widely used for programming the robot because it provides libraries and tools to help software developers create robot applications. It also provides hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, and more. The models built in ROS are simulated in Gazeabo, a 3-D simulation environment to analyze the algorithmic implementation firsthand. In the last few decades a large amount of work has been carried out in the field of SLAM [1]–[3], in which a robot generates a geometric model of its environment based on observations from a laser scanner. Generally robot's position problem is solved by a GPS which provides a good accuracy for the robot. However, in places where the GPS data is not available, or not reliable enough, we need some other reliable method to estimate the robot’s position precisely. Odometry is the use of data from motion sensors to estimate change in position over time [4]. Odometry constitutes low level operation of the robot for pose detection and obstacle avoidance purposes. Gyro-correction of the 3-axis magnetometer of course requires 3-axis gyroscopes as well, and our simple two-sensor complementary Kalman filter becomes much more complex. In addition, stabilization of the gyroscopes in 3 axis requires an additional vertical correction which can be derived from a set of 3 orthogonally oriented accelerometers, arranged to measure the accelerations of the Earth's gravity (i.e., "down"). The combination of these 9 sensors with a Kalman state estimator yields a highly stable orientation sensor, often called an Inertial Measurement Unit (IMU), which can accurately determine the robot's heading and position in 3 dimensions, irrespective of the angle and movement of the robot. [5]

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Robot control is done by using a PID controller which takes reference from the laser scanner and then uses the encoders in feedback to maintain the desired posture of the robot. To distinguish green grass from road, pavement etc, blob algorithms is applied to cluster the similar features of the terrain. These regions could signal the presence of objects or parts of objects in the image domain with application to object recognition and object tracking. Another common use of blob descriptors is as main primitives for texture analysis and texture recognition. Navigation by optimal exploration is done by Q-learning to find an optimal path policy to minimize the terrain cost function. For this purpose the dilemma of exploration and exploitation is implemented to design a robot to efficiently traverse any random area. To implement the idea, vector fields based policy iteration is done on mapped outputs of SLAM to generate and follow the traces of vector and extract the information of present and future position co-ordinates. The algorithm ensures a basic memory of all the traversed points so that a point is not mowed twice while simultaneously exploring the terrain with high performance. Hokuyo Laser range finder sensor is used for obstacle detection. The sensor has a scan range of 270⁰ with an angular resolution of 0.25⁰. Its detectable range varies from 100 mm to 30 m with an accuracy of 30 mm. High power components are used in H-bridge to drive the motor. On the hardware side there were a number of modifications done by the previous sproj group. The body of the lawn mower was altered to improve stability. Also an additional circuit box was attached to improve user friendly interface. The box contained circuit breakers which allows the user to use them as emergency switches. The batteries which are housed inside the body had to be removed in order to charge them, now the circuit box has extended battery terminals for easy charging. For further improving the interface, LCD display has been installed which displays all the necessary information. In addition to this, H-bridges of higher current rating (20A), and increased drive pulley ratio for higher torque are now being used. References: [1] S. Kohlbrecher, J. Meyer, O. von Stryk, and U. Klingauf, “A flexible and scalable slam system with full 3D motion estimation,” in Proc. IEEE Int. Symp. Safety Sec. Rescue Robot. (SSRR), Nov. 2011, pp. 155–160. [2] G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with Rao-Blackwellized particle filters,” IEEE Trans. Robot., vol. 23, no. 1, pp. 34–46, Feb. 2007. [3] M. Kaess, A. Ranganathan, and F. Dellaert, “iSAM: Incremental smoothing and mapping,” IEEE Trans. Robot., vol. 24, no. 6, pp. 1365–1378, Dec. 2008. [4] Visual-Inertial Odometry on Resource-Constrained Systems by Li, Mingyang, Ph.D., UNIVERSITY OF CALIFORNIA, RIVERSIDE, 2014, 224 pages; 3682009

[5] http://www.geology.smu.edu/~dpa-www/robo/Encoder/imu_odo/

Expected Outcome [Enable Macros to select suitable option(s) OR remove which not applicable] Research Paper ☐ Book ☐ Case ☐ Documentary ☐ Hardware ☒ Software ☒

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Other ☐ ______________________________ (Details):

Proposed Budget (itemized) Supplies & Equipment

Quantity

Amount (in PKR)

Justification

Max. PKR 30,000

Miscellaneous electronics/Cables

X1

10,000

Battery upgrade (12V, 26 Ah)

X1

14,000

Zigbee, Arduino

x1

5000

PIR sensor, Laser sensor

X1

1000

Subtotal --------------------------------------------------------------------------

Proposal ID: Is the proposal according to the spirit of SRP? ☒

Desk Rejection

Last Revised:

Yes ☐

No ☐

30,000

FOR OFFICIAL USE ONLY

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