Robotics Practical 2

Uitgebreide vaknaam	Robotics Practical 2
Leerdoelen	At the end of the course, the student is able to: 1) Design and implement a supervised learning method (i.e., support vector machine) for the classification of features extracted by a simulated lidar in a simulated environment. 2) Design and implement a human-in-the-loop control for the navigation of a simulated unicycle mobile robot that follows simulated human’s legs in a simulated environment. 3) Design and implement a centralized autonomous coordination control for a swarm of simulated unicycle mobile robots to keep a predefined formation in a simulated environment. 4) Design and implement an autonomous control for the simulated unicycle mobile robots for static obstacle avoidance in a simulated environment. 5) Design and implement an overall system behavior in which the simulated unicycle mobile robots follow a human’s legs in a simulated environment, while keeping a predefined formation and avoiding static obstacles.
Omschrijving	The students gain basic knowledge on the control of a swarm of unicycle mobile robots (mobile base and lidar) to follow a human, while keeping a formation and avoiding obstacles in a simulated environment, and gain practical experience. Students focus on: - A supervised learning method for the classification of features extracted by the lidar in simulation. Students learn: 1) To process simulated lidar data (collect data, cluster them, extract geometrical features); 2) To design and implement a support vector machine on the extracted features to classify simulated human’s legs. Tools: Stage, RViz. - A human-in-the-loop control algorithm for the navigation of the robots to follow the human’s legs in simulation. Students learn: 1) To compute the central point (on the ground) between two clusters classified as human legs; 2) To design/implement a velocity control for the robots to follow the computed central point by keeping a requested distance. - A centralized autonomous coordination control algorithm for the swarm to keep a predefined formation in simulation. Students learn: 1) To compute the centroid of the formation; 2) To design/implement a velocity control for the swarm to keep a requested distance from the centroid. - An autonomous control algorithm for the robots to avoid static obstacles in simulation. Students learn: 1) To detect additional static obstacles with the lidar; 2) To design/implement a velocity control for each robot to avoid obstacles by keeping a requested distance from them. - An overall system behavior of the swarm in simulation. Students learn: 1) To build a control architecture, based on a null-space approach, that assigns priorities to the three tasks (obstacle avoidance, follow-me, formation control); 2) To develop wellstructured software. Tools: Python, NumPy, Git.
Uren per week
Onderwijsvorm	Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC)
Toetsvorm	Opdracht (AST), Verslag (R) (The final report grade should be above or equal to 5. The final assignment grade should be above or equal to 5. The final weighted grade (30% report + 70% assignment) should be at least 5.50. This course has mandatory presence: At least 75% of the lectures/tutorials/practical work is mandatory. This implies that missing more than 25% results in failure of the course.)
Vaksoort	bachelor
Coördinator	prof. dr. R. Carloni
Docent(en)	prof. dr. R. Carloni
Verplichte literatuur	Titel Auteur ISBN Prijs Lecture notes, tutorial instructions, few research papers.
Entreevoorwaarden	Mandatory: Robotics Practical 1 (WBAI029-05). Strongly advised: Calculus for AI, Linear Algebra and Multivariable Calculus. Recommended: Autonomous systems, Signals and Systems, Neural Networks, Statistics, Imperative Programming. Prior knowledge of Python and Git is useful, but not required. If the mandatory requirements are not met, only the Board of Examiners of the AI BSc may grant an exemption. Exchange students are assumed to have gone through this through their Learning Agreement; pre-master's students through the Board of Admissions - other external students are judged case-by-case.
Opmerkingen	A (recent) computer is required to work from home and run a Virtual Machine. This course unit has a capacity limit. More information about capacity-limit courses can be found here. This course has an intended limit of 40 participants. Artificial Intelligence (BSc) is a Fixed Quota (Numerus Fixus) programme. As a consequence, their courses (course code WBAI) are closed for students that are not registered under the AI BSc programme, unless the course is part of the mandatory curriculum of their programme. If you wish to take this course in your minor – or as part of a so-called ‘unofficial’ pre-master’s – please use the official procedure through the Board of Examiners form.
Opgenomen in	Opleiding Jaar Periode Type BSc Artificial Intelligence 2 semester II b keuzegroep BSc Artificial Intelligence 3 semester II b keuzegroep