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

Lecture 11 - Game Theory in Robotics

#TWeek Week Monday (Room No. KN:E-331)
#1 40. 01.10. - Lab01 - Introduction to V-REP and Open-Loop Robot Locomotion Control
#2 41. 08.10. - Lab02 - Exteroceptive sensing, Mapping and Reactive-based Obstacle Avoidance
#3 42. 15.10. - Lab03 - Grid and Graph-based Path Planning
#4 43. 22.10. - Lab04 - Incremental Path Planning
#5 44. 29.10. - Lab05 - Randomized Sampling-based Algorithms
#6 45. 05.11. - Lab06 - Curvature-constrained Local Planning in RRT
#7 46. 12.11. - Lab07 - Asymptotically Optimal Randomized Sampling-based Path Planning
#8 47. 19.11. - Lab08 - Multi-goal Path Planning and Data Collection Path Planning
#9 48. 26.11. - Lab09 - Data Collection Path Planning with Remote Sensing (TSPN)
#10 49. 03.12. - Lab10 - Curvature-constrained Data Collection Path Planning ((D)TSPN)
#11 50. 10.12. - Lab11 - Game Theory in Robotics - Greedy Policy
#12 51. 17.12. - Lab12 - Game Theory in Robotics - MCTS
52. Winter holidays (24.12.)
#13 01. Public holidays - New Year's Day (31.12.)
#14 02. 07.01. - Lab13 - Game Theory in Robotics - Value Iteration
14.01. - Ungraded Assessment Deadline!