Recently, the collective motion (coordination of movement) of multiple vehicle systems has received considerable attention because of its broad range of engineering applications. The accomplishment of many robotic tasks requires the coordination of a group of mobile robots which generally outpe
Coordinated Control for Collective Motion
Recently, the collective motion (coordination of movement) of multiple vehicle systems has received considerable attention because of its broad range of engineering applications. The accomplishment of many robotic tasks requires the coordination of a group of mobile robots which generally outperforms robots operating independently. This project will extend ongoing work by developing state-of-the-art techniques for:
(i) collision avoidance under energy and vehicle dynamics constraints;
(ii) nonlinear filtering for tracking a maneuvering robot;
(iii) formation topologies for collective motion.
The project will be undertaken by 3 students. Each student will work independently on one of the 3 aims of this project (see Project Objectives). This will ensure progress on each of the 3 aims.
Student (S)1 will focus on trajectory generation and re-planning for collision avoidance; S2 will focus on the target tracking algorithm for robot localization; S3 will focus on hardware development and mapping the algorithms for onboard execution.
In the initial phase, S1 will study a special class of curves, namely the Pythagorean hodograph (PH) Bézier curves for trajectory generation and review the literature on collision avoidance under constraints. S2 will review the literature on target tracking for nonlinear state space models. S3 will study robot design, hardware platforms and how to map the algorithms for onboard execution.
In the next phase, S1 will conduct simulation studies using MATLAB to demonstrate trajectory generation and collision avoidance. S2 will conduct simulation studies using MATLAB to demonstrate target tracking for nonlinear models. S3 will build a prototype of the robot and conduct preliminary tests.
In the final phase, S1 will map the algorithm for trajectory generation in Python for onboard execution. S2 will map the algorithm for robot tracking in Python for onboard execution. S3 will integrate the software for onboard execution and conduct tests to demonstrate 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.
Unmanned ground vehicles (UGVs) with advanced imaging can be used to cheaply and effectively map and accelerate clearing of minefields.
In the manufacturing industry, UGVs (also known as automated ground vehicles [AGVs]) are used for transporting heavy equipment. For example, the aerospace industry uses AGVs for precision positioning and transporting heavy, bulky pieces between manufacturing stations, which is less time-consuming than using large cranes.
The final deliverables will include:
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry Pi model B+ | Equipment | 3 | 9000 | 27000 |
| Motor Kit | Equipment | 3 | 1200 | 3600 |
| Ultrasonic Sensor | Equipment | 12 | 600 | 7200 |
| GPS Sensor | Equipment | 3 | 1600 | 4800 |
| Rechargeable 12V LiPo Battery | Equipment | 3 | 2500 | 7500 |
| Body Kit | Equipment | 3 | 6000 | 18000 |
| Total in (Rs) | 68100 |
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