1. SENSOR BASED CONTROL OF AUTONOMOUS ROBOTS
Robert Mahony
Department of Engineering
Australian National University
Department of Engineering Research Forum,
November, 2006.

2. Active topics in robotics
Physical control of vehicle/machine
Sensory perception of environment
Interaction of vehicle with environment
AI (artificial intelligence)
Autonomy
Co-ordination
Data interpretation
Multiple robots
Multiple tasks
Human-robot interfaces
Inter-robot interfaces
Data fusion
SLAM (simultaneous localisation and mapping)
Object recognition, sensor segmentation
Sensor based control of autonomous robots
13-May-19

3. Active topics in robotics
Physical control of vehicle/machine
Sensory perception of environment
Interaction of vehicle with environment
AI (artificial intelligence)
Autonomy
SENSOR BASED
CONTROL
Co-ordination
Data interpretation
Multiple robots
Multiple tasks
Human-robot interfaces
Inter-robot interfaces
Data fusion
SLAM (simultaneous localisation and mapping)
Object recognition, sensor segmentation
Sensor based control of autonomous robots
13-May-19

4. Autonomy in robotic systems
An autonomous robot is capable of moving about within an unstructured (or partially) structured environment independently.
Unstructured environment:
No map available.
Partially structured environment:
There is a map but it does not contain all objects – and is not necessarily accurate.
In all cases the robot must regulate its motion with respect to the local environment.
Sensor based control of autonomous robots
13-May-19

5. Sensor based control of autonomous robots
Example: Aerial robot
A common task that is considered in aerial robotics is regulation of the vehicle relative to an observed feature.
Other important tasks
Obstacle avoidance
Close approach and landing
Sensor based control of autonomous robots
13-May-19

6. Classical control approach
Classical control theory provides a standard approach to regulation problems
Model the dynamics of the system.
Represent the dynamics in terms of a minimal state.
Represent the task in terms of a state error.
Design a control algorithm to drive the state error to zero.
Measure something.
Estimate the system state on-line.
Input the state estimate into the control algorithm to close the loop.
Sensor based control of autonomous robots
13-May-19

7. Issues with classical control approach
REAL WORLD
Observations
Sensors
The mapping from observation to state estimate is
non-linear
over-determined
ill-conditioned
Task error
State estimates
Computing a state estimate from the observations requires:
Model of the environment (SLAM)
Model of the system dynamics
Estimates tends to be ill-conditioned when the vehicle is distant from local features.
Task error is naturally conditioned relative to proximity to environment!
Easy to represent in terms of sensor measurements.
Sensor based control of autonomous robots
13-May-19

8. Sensor based control of autonomous robots
Sensor based control is a paradigm that is only subtly different from the classical approach.
Model the dynamics of the system
Use this model to determine the dynamic response of the sensor signals based on the expected environment.
Represent the task in terms of a sensor error
Design a control algorithm to drive the sensor error to zero based on analysis of the sensor dynamics
Input the sensor measurements into the control algorithm to close the loop.
Sensor based control of autonomous robots
13-May-19

9. Sensor based control of autonomous robots
Bio-mimetic systems
One of the major motivations for sensor based control of autonomous robots is the growing evidence for simple sensor based control algorithms in biological system.
A honey bee regulates its thrust in landing approach in proportion to a measure of divergence of the observed optic flow (Srinivasan et al. 2000, Moffit et al. 2006)
Optical flow field  of textured surface under direct approach
Sensor based control of autonomous robots
13-May-19

10. Challenges to sensor based control.
Sensor dynamics tend to be highly non-linear.
Very challenging control problems.
Sensor data tends to be high dimensional – much higher dimensional than the state vector.
Leads to non-minimal system representations.
Early work in this area has depended on finding good features (eg average flow divergence s div ) that provide a low dimensional “sensor state” representation.
Overcoming these problems leads to highly robust and effective task based control of autonomous systems.
Sensor based control of autonomous robots
13-May-19

11. Stabilisation of aerial robot relative to image.
Observed closed-loop error evolution in the sensor based task criterion.
Regulation of position in task space.
Computed from inverse pose algorithm.
Sensor based control of autonomous robots
13-May-19

12. Sensor based control of autonomous robots
Collaborators
Peter Corke
Tarek Hamel
Odile Bourquardez
Nicolas Guenard
(many other honours and stagiere students)
Francois Chaumette
Sensor based control of autonomous robots
13-May-19

13. Dynamic image based visual servo control
Consider the problem of stabilising an aerial robot relative to some physical object who’s image is easily segmented.
Observed object
Image on spherical image plane
Spherical centroid is the integral of observed image on the sphere.
Sensor based control of autonomous robots
13-May-19

14. Sensor space dynamics and control
Sensor based control of autonomous robots
13-May-19