Simultaneous Localization and mapping

Simultaneous localization and mapping (SLAM) is an important problem in signal processing with a number of industry applications. This project will commence work on the implementation of SLAM in complex environments. In this project we will consider a single robot that can navigate autonomously in a

Project Title

Simultaneous Localization and mapping

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Simultaneous localization and mapping (SLAM) is an important problem in signal processing with a number of industry applications. This project will commence work on the implementation of SLAM in complex environments. In this project we will consider a single robot that can navigate autonomously in an unknown static environment and simultaneously build a 2- dimensional map of the environment in real-time. This project will involve implementation of sophisticated signal processing algorithms for robot localization, collision avoidance and trajectory generation subject to robot dynamics and energy constraints.

Project Objectives

1.Implement recursive estimation algorithms in nonlinear filtering for robot localization.
2 Implement trajectory generation algorithms subject to robot dynamics and energy
constraints with collision avoidance.
3. Build a robot capable of navigating autonomously in an unknown static environment
simultaneously building a map of the environment in real-time.

Project Implementation Method

Each student will work independently on one of the 3 aims of this project. This will ensure progress on each of the 3 aims. Student1 will focus on recursive estimation algorithms; Student2 will focus on trajectory generation algorithms; Student3 will focus on hardware implementation. In the initial phase, Student1 will study recursive estimation algorithms. This will involve developing an understanding of Kalman filtering and its extension to nonlinear models, the extended Kalman filter. Student2 will review the literature on trajectory generation. This will involve developing skills to mathematically formulate the problem and solve constrained optimization problems. Student3 will study hardware platforms and how to map the algorithm for onboard execution. In the next phase, Student1 will conduct simulation studies using MATLAB to demonstrate robot localization. Student2 will conduct simulation studies using MATLAB to generate trajectories subject to constraints including collision avoidance. Student3 will build the robot and conduct preliminary tests on the robot. In the final phase, Student1 will map the algorithm for robot localization in Python for onboard execution. Student2 will map the algorithm for trajectory generation in Python for onboard execution. S3 will integrate the software for onboard execution and conduct tests to demonstrate SLAM.

Benefits of the Project

There are a number of engineering applications including unmanned sensor networks, for example, autonomous underwater vehicles (AUVs). Industry applications include exploration, security patrols, scouting and hunting missions, search and rescue.

Technical Details of Final Deliverable

1.MATLAB & Python software for extended Kalman filtering. 2.MATLAB & Python software trajectory generation & collision avoidance.

3.Robot capable of navigating in an unknown environment.

Final Deliverable of the Project

Hardware System

Core Industry

Education

Other Industries

Manufacturing

Core Technology

Robotics

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com