Rob Mahoney

SENSOR BASED CONTROL OF AUTONOMOUS ROBOTS: 

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

Active topics in robotics: 

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

Active topics in robotics: 

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

Autonomy in robotic systems: 

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.

Example: Aerial robot: 

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

Classical control approach: 

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.

Issues with classical control approach : 

Issues with classical control approach REAL WORLD State estimates Observations Sensors The mapping from observation to state estimate is non-linear over-determined ill-conditioned Task error Task error is naturally conditioned relative to proximity to environment! Easy to represent in terms of sensor measurements. 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.

Sensor based control : 

Sensor based control 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.

Bio-mimetic systems: 

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. Optical flow field  of textured surface under direct approach 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)

Challenges to sensor based control.: 

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.

Stabilisation of aerial robot relative to image. : 

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.

Collaborators: 

Collaborators Tarek Hamel Peter Corke Francois Chaumette Odile Bourquardez Nicolas Guenard (many other honours and stagiere students)

Dynamic image based visual servo control: 

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 space dynamics and control: 

Sensor space dynamics and control