Exam Questions - Autonomous Systems Lab

Questions, Lecture 'Autonomous Mobile Robots', R. Siegwart, ETH Zurich
The examiners select three questions (one from each column) from the list below. The exam will start with an application question from the first column for which the sutdent has to propose the general concept,
the locomotion concept, the sensors, and the navigation concept. It will then be followed by two specific questions (Basics 1 & 2) on the lecture from the 2nd and 3rd columns.
Application
Basics 1
11
Agricultural robot for pest and weed control
11 2.2.1-3: Leg configuration, dynamics, stability and gaits, locomotion
with 1, 2, 4 or 6 legs
11 5.1-2: Sensor noise and aliasing, the general schematics for mobile
robot localization; implementation (fig. 5.2)
11
12
All terrain demining in unstructured environments
12 3.2.3: Derivation of the wheel equation and how to apply it to different 12 5.1-2: Sensor noise and aliasing, odometric position estimation and
wheel types (focus on fixed and omni wheels)
error model for a differential drive robot and their use in Markov and
EKF localization (5.2.4)
12
13
All terrain robot for handicapped peoples
Transportation in production plants (AGV)
13 5.4: Belief representation (metric, grid, topologic, single and multiple
hypotheses)
14 5.5: Map representation: continuous, decomposition in cells
13
14
13 3.2.5: Forward, differential, and inverse kinematics of articulated
robotic systems
14 3.3: Robot mobility, steerability and maneuverability: definition,
(geometric) interpretation (including ICR), the five basic types of
three-wheel configurations (fig. 3.14)
15
Autonomous car parking
15 3.4.2 & slides: Holonomic and non-holonomic constraints; examples
and interpretation
15 5.6.1-2: Probability theory applied to robot localization with focus on
the Bayesian update formula
15
16
Transportation in Hospitals
16
Autonomous lawnmower
21 5.6.7: Markov localization: overview
21
22
Autonomous lawnmower for football fields
16 3.4.2 & slides: Holonomic and non-holonomic constraints; examples
and interpretation
21 3.4.3: Path and trajectory considerations; the omni drive and twosteered robot example
22 3.6.1-2: Open loop and closed loop control, diff. drive example.
Alternative control approaches for non-holonomic vehicles?
16 5.5: Map representation: continuous, decomposition in cells
21
22 5.6.7.4-5: Case Studies: Markov localization using a grid/topological
map
22
23
Autonomous loading of pallet on a truck
23 4.1.4-7: Sensors for odometric update: Wheel encoders, heading
sensors (Compass), Gyroscope, IMU
23 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
23
24
Tour-Guide robot for exhibitions
24 4.1.8: Ground based beacons, GPS. 4.1.9.2: Triangulation Sensor
1D, 2D; accuracy as function of distance and disparity
24 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
24
25
Autonomous vehicle to plant seedlings on an open
agriculture field
25 4.1.9.1: Range sensors: Measurement principles, performance (laser, 25 5.6.8.4: Kalman filter applied to mobile robots
ultrasonic, time-of-flight)
25
26
Autonomous wheelchair for handicapped peoples
26 4.2.3 & slides: Thin lens equation (derivation), Pinhole Camera
Model
26 5.8.4-6 & slides: EKF SLAM method, equations (e.g. MonoSLAM)
and drawbacks. 5.8.8 Particle Filter SLAM.
26
31
Car driver support for advanced safety
31 4.2.3: Perspective Projection (from 3D world to pixel coords), Radial
Distortion, Camera Calibration
31 5.8.4-6 & slides: EKF SLAM method, equations (e.g. MonoSLAM)
and drawbacks. 5.8.8 Particle Filter SLAM.
31
32
Car for autonomous operation in cities
32 4.2.5: Stereo vision. Depth estimation for 2 identical, horizontally
aligned cameras. Disparity Map. Triangulation.
32 6.3.1: Configuration space, graph construction, graph search with
focus on the Depth First and Dijkstra algorithms
32
33
Car for autonomous operation on freeways
33 4.3.1 & slides: Image Filtering: linear, shift-invariant filters. 1D
Correlation for averaging, smoothing, taking derivatives and template
matching (NCC, ZNCC).
33 6.3.1: Configuration space, graph construction, graph search with
focus on the Breadth First and A* algorithms
33
34
Cleaning in a family home
34 4.3.1 & slides: 2D Correlation, separable filters, Gaussian smoothing, 34 5.6.8: General scheme of Kalman filter localization: Static problem,
Correlation vs. Convolution
fusion of probability density of two estimates
34
35
Collection of tennis balls
35 4.4: Image Features: what and why. 4.5.3 Harris Corner Detector
(idea, methodology, properties)
35 6.4: Obstacle avoidance: bug, VFH and DWA in detail. Other
approaches overview only
35
36
Container transportation in harbors
36 4.5 & slides: SIFT (methodology, properties). Application to
object/scene recognition
36 6.4: Obstacle avoidance: Reciprocal Velocity Obstacles. Other
approaches overview only
36
14
Application
Basics 1
Basics 2
41
Exploration of Antarctic
41 2.2.1-3: Leg configuration, dynamics, stability and gaits, locomotion
with 1, 2, 4 or 6 legs
41 5.1-2: Sensor noise and aliasing, the general schematics for mobile
robot localization; implementation (fig. 5.2)
41
42
Exploration of desert
42 3.2.3: Derivation of the wheel equation and how to apply it to different 42 5.1-2: Sensor noise and aliasing, odometric position estimation and
wheel types (focus on fixed and omni wheels)
error model for a differential drive robot and their use in Markov and
EKF localization (5.2.4)
42
43
Fire fighting robot
43 3.2.5: Forward, differential, and inverse kinematics of articulated
robotic systems
43 5.4: Belief representation (metric, grid, topologic, single and multiple
hypotheses)
43
44
Guide robot at ETH
44 3.3: Robot mobility, steerability and maneuverability: definition,
(geometric) interpretation (including ICR), the five basic types of
three-wheel configurations (fig. 3.14)
44 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
44
45
Home surveillance
45 3.4.2 & slides: Holonomic and non-holonomic constraints; examples
and interpretation
45 5.6.1-2: Probability theory applied to robot localization with focus on
the Bayesian update formula
45
46
Inspection of sewage canal
46 3.4.2 & slides: Holonomic and non-holonomic constraints; examples
and interpretation
46 5.5: Map representation: continuous, decomposition in cells
46
51
Inspection of ventilation system
51 3.4.3: Path and trajectory considerations; the omni drive and twosteered robot example
51 5.6.7: Markov localization: overview
51
52
Mining
52 3.6.1-2: Open loop and closed loop control, diff. drive example.
Alternative control approaches for non-holonomic vehicles?
52 5.6.7.4-5: Case Studies: Markov localization using a grid/topological
map
52
53
Mobile robot (rover) for unmanned exploration of Mars
53 4.1.4-7: Sensors for odometric update: Wheel encoders, heading
sensors (Compass), Gyroscope, IMU
53 5.6.1-2: Probability theory applied to robot localization with focus on
the Bayesian update formula
53
54
Mobile robot for indoor post delivery
54 4.1.8: Ground based beacons, GPS. 4.1.9.2: Triangulation Sensor
1D, 2D; accuracy as function of distance and disparity
54 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
54
55
Mobile robot for post delivery in structured housing area
55 4.1.9.1: Range sensors: Measurement principles, performance (laser, 55 5.6.8.4: Kalman filter applied to mobile robots
ultrasonic, time-of-flight)
55
56
Rescue robot for natural disasters (e.g. earthquake, flood)
56 4.2.3 & slides: Thin lens equation (derivation), Pinhole Camera
Model
56 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
56
61
Surveillance in office building
61 4.2.5: Epipolar Geometry. The Epipolar constraint, the
Correspondence Problem and Epipolar Rectification.
61 5.8.4-6 & slides: EKF SLAM method, equations (e.g. MonoSLAM)
and drawbacks. 5.8.8 Particle Filter SLAM.
61
62
The autonomous hotel butler
62 4.2.5: Stereo vision. Depth estimation for 2 identical, horizontally
aligned cameras. Disparity Map. Triangulation.
62 5.6.8: General scheme of Kalman filter localization: Static problem,
fusion of probability density of two estimates
62
63
Tour guide robot
63 4.3.1 & slides: Image Filtering: linear, shift-invariant filters. 1D
Correlation for averaging, smoothing, taking derivatives and template
matching (NCC, ZNCC).
63 6.3.1: Configuration space, graph construction, graph search with
focus on the Breadth First and A* algorithms
63
64
Tour-Guide robot for exhibitions
64 4.3.1 & slides: 2D Correlation, separable filters, Gaussian smoothing, 64 5.8.4-6 & slides: EKF SLAM method, equations (e.g. MonoSLAM)
Correlation vs. Convolution
and drawbacks. 5.8.8 Particle Filter SLAM.
64
65
Transportation in Hospitals
65 4.4: Image Features: what and why. 4.5.3 Harris Corner Detector
(idea, methodology, properties)
65 6.4: Obstacle avoidance: bug, VFH and DWA in detail. Other
approaches overview only
65
66
Transportation in production plants (AGV)
66 4.5 & slides: SIFT (methodology, properties). Application to
object/scene recognition
66 6.4: Obstacle avoidance: Reciprocal Velocity Obstacles. Other
approaches overview only
66

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