Labs

Attendance is mandatory. Laboratory exercises mainly servers to acquire practical experience of the applied AI techniques in the selected robotic tasks.

The aim of the exercise is to apply the topics explained during the lectures in practical deployment in solving selected robotic problems. In the exercises, students shall develop software to control mobile robots using the algorithms and existing implementations and software libraries.

Exercises at glance

#TWeek	Week	Monday (Room No. KN:E-307 @12:45 and @14:30. Room No. KN:A-420 @16:15.)	Instructor
#1	39.	22. 09. - Lab01 - Introduction to CoppeliaSim and Open-Loop Robot Locomotion Control	Jiří Kubík
#2	40.	29. 09. - Lab02 - Exteroceptive sensing and Reactive-based Obstacle Avoidance	Jiří Kubík
#3	41.	06. 10. - Lab03 - Grid and Graph-based Path Planning	Jiří Kubík
#4	42.	13. 10. - Lab04 - Mapping	Jiří Kubík
#5	43.	20. 10. - Lab05 - Mobile Robot Exploration	Jiří Kubík
#6	44.	27. 10. - Lab06 - Incremental Path Planning	David Valouch
#7	45.	03. 11. - Lab07 - Data Collection Path Planning with Remote Sensing (TSPN)	David Valouch
#8	46.	10. 11. - Lab08 - Curvature-constrained Data Collection Path Planning (DTSPN)	David Valouch
#9	47.	17. 11. - Struggle for Freedom and Democracy Day
#10	48.	24. 11. - Lab09 - Randomized Sampling-based Algorithms	David Valouch
#11	49.	01. 12. - Lab10 - Curvature-constrained Local Planning with RRT-based algorithms	David Valouch
#12	50.	08. 12. - Lab11 - Risk-aware Planning	David Valouch
#13	51.	15. 12. - Lab12 - Reinforcement Learning with an Inchworm Robot	Jiří Kubík
	52.	22. 12. - Winter holidays (23.12. - 5.1.)
	01.	29. 12. - Winter holidays (23.12. - 5.1.)
#14	02.	05. 01. - Lab13 - Semestral Project Discussion and Inchworm Deployment	Jiří Kubík
11. 01. @ 23:59 PST - Ungraded Assessment Deadline!