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Laboratory Exercises

Attendance is mandatory. Laboratory exercise 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-331)
#1 39. 23.09. - Lab01 - Introduction to V-REP and Open-Loop Robot Locomotion Control
#2 40. 30.09. - Lab02 - Sensing and Reactive-based Obstacle Avoidance
#3 41. 07.10. - Lab03 - Grid-based Path Planning
#4 42. 14.10. - Lab04 - Path Planning and Incremental Path Planning
#5 43. 21.10. - Lab05 - Mobile Robot Exploration
44. 28.10. - National holidays (28.10.)
#6 45. 04.11. - Lab06 - Multi-goal Planning
#7 46. 11.11. - Lab07 - Data Collection Path Planning - Decoupled approach
#8 47. 18.11. - Lab08 - Curvature-constrained Data Collection Path Planning ((D)TSPN)
#9 48. 25.11. - Lab09 - Randomized Sampling-based Algorithms
#10 49. 02.12. - Lab10 - Game Theory in Robotics - Greedy Policy
#11 50. 09.12. - Lab11 - Game Theory in Robotics - MCTS
#12 51. 16.12. - Lab12 - Game Theory in Robotics - Value Iteration
52. 23.12. - Winter holidays (23.12. - 5.1.)
01. 30.12. - Winter holidays (23.12. - 5.1.)
#13 02. 06.01. - Lab13 - Semestral project evaluation (consulation)
TBA - Ungraded Assessment Deadline!
courses/b4m36uir/labs/start.txt ยท Last modified: 2019/11/01 10:30 by pragrmi1