Monthly archive - November 2012

Feature Based Localisation and Mapping

Last week Tom Larkworthy of Heriot-Watt University (HWU) visited the University of Girona (UdG) to initiate integration of recent SLAM research onto HWU’s Nessie AUV. At UdG, Sharad Nagappa has been focusing on development of SLAM using recent advances in the field of multi-object estimation.

What is SLAM?

Simultaneous Localisation and Mapping (SLAM) is a way of improving estimates of vehicle position in unknown environments. We estimate the position of landmarks based on the current vehicle position, and we can then use knowledge of these (stationary) landmarks to infer the position of the vehicle. By relying on a fixed reference, we can reduce the error due to drift.

PHD Filter and SLAM

The Probability Hypothesis Density (PHD) filter is a suboptimal Bayes filter used for multi-object estimation. Here, we are required to estimate the number and position of an unknown number of objects. The PHD filter performs this while eliminating the need for data association. We can combine this with SLAM by using the PHD filter to represent the landmarks. More technically, this forms a single cluster process, with the vehicle position as the parent state, and the landmarks as the daughter states conditioned on a vehicle position. This formulation is a form of feature-based SLAM since we approximate landmarks as point features.

Figure: Simulation of SLAM with a combination of detected and undetected landmarks

Detecting and Estimating Map Features

The PHD SLAM formulation only relies on a set of point observations. The algorithm does not change depending on whether we are using sonar or vision. Consequently, this offers the potential to combine these two sources using a single technique – as long as we can detect useful features from the sensors! Currently, we are relying on image feature extractors such as SURF and ORB to detect features from our stereo camera. In the coming months we will consider features from the forward looking sonar as well as apply PHD SLAM to real data.

Challenges For PANDORA

Computational resources are particularly constrained on AUVs. SLAM algorithms are notoriously computing intensive. One option available land robotics is the use of CUDA computing architectures to brute force around the problem, but for the underwater domain there are no suitable embedded CUDA systems. Therefore one big challenge for integration in PANDORA is adapting cutting edge SLAM algorithms to run on our embedded systems.
Another difficulty associated with the underwater domain is combining SLAM with sonar data. Standard forward looking sonars are unable to accurately localise in the depth dimension, thus observations are underconstrained. Furthermore, sonar pings do not have reliable high frequency components that optical vision does – this means that common feature extractors, such as SIFT, do not see anything on sonar data. In PANDORA we will be using next generation sonars to get better sonar data into the SLAM system, and developing new feature detectors that compliment SLAM in an underwater domain better.

Presentation of the Valve Panel and the new Robotic Arm

The robotic arm which will be installed in the Girona500 arrived last week. This will be used to manipulate the valve panel. This arm has 4 Degrees of Freedom(DoF)(slew, elevation, elbow, jaw rotation), and also the opening and close of the jaw. The arm has arrived without a proper control software and some time will be needed to perform the integration of the arm in the Girona500.

robotic arma E5

On the other hand, the valve panel has been built and is ready to start working with it in the water tank at CIRS. The first steps will be focus on the visual detection of the valve panel and the valves positions. Also, the valve panel in the simulator has been updated to match the one built.

robotic arma E5

In this video we want to show the speed and kind of movement which the robotic arm is capable of. The movement will be quite different from the one done when the arm is installed in the robot because the control of the arm will have also take in consideration the DoF of the robots, which can make more movements possible. To control the arm for this video we have used the commercial software provided by the manufacturer.

Learning algorithms for improved AUV control

The IIT team carried out a series of experiments on adaptive control. The aim of this work was to wrap a given controller into a learning layer, able to make corrections to the controller’s output and adapt it to the environment. Even robust controllers, as the ones being developed in this project by NTUA, may have to face unexpected environmental conditions. Such events can make the controllers less effective or unable to perform the task in the most severe cases. The learning layer monitors the agent’s performance, and explores possible corrections should the performance become lower than expected.

The most important component of the learning layer is a learning algorithm. The learning algorithm search the parameter space in order to find a parameter vector that maximizes the agent’s performance. The parameters come from a class of parametric functions selected to represent the corrections to the control actions. The output of the parametric function is added to the given controller as correction. We devised a learning algorithm to perform iterative optimization of the parameters based on a series of experiments in the UWSim simulator of Girona500.

We simulated a very strong current, stronger than the thrusters. The controller we used was a simple PD controller. The task was to reach an object on the seabed and hover at less than 0.5 m above it for as long as possible, to take pictures. Since such a controller reacts to the current when the vehicle is already being carried away, it cannot by itself perform the task. The agent attempts to reach the target object in several trials, going out of the current to the initial location. It managed to correct the given controller by navigating out of the current as long as possible, and trying to resist the current at full power in the last part of the trajectory.

Collaboration between UdG and NTUA: Identification and Control of GIRONA500

Collaboration between UdG and NTUA: Identification and Control of GIRONA500

The NTUA CSL group visited the UdG CIRS Lab from 16/10/12 to 27/10/12 in the context of the PANDORA project. The purpose of the visit was twofold:

• Identification through an appropriate experimental procedure of the parameters of the GIRONA500 AUV dynamic model

• Implementation and testing of model-based robust position and velocity controllers.

The aforementioned goals were achieved successfully. Some additional experiments are still required for the fine tuning of the position control scheme. During this excellent and fruitful collaboration, significant information and expertise was exchanged among the UdG and NTUA partners.

Experiments at UdG (17 Oct 2012 – 26 Oct 2012).

New control-aware planning framework for PANDORA

The KCL team are developing their control-aware planning framework for
the PANDORA setting. A planner is required to generate high-level plans
(especially in the context of an ambition to achieve long-horizon
persistent autonomy), while individual actions are realised through the
behaviour of dedicated controllers. The principle behind control-aware
planning is that a planner must have a model of the task that the
controller faces and some level of understanding of the control
parameters in order to generate appropriate and achievable set-points
for the controllers and also to ensure plans are both executable and not
excessively conservative. Development of the physical models needed to
achieve this in PANDORA is being done in collaboration with Kostas
Kyriakopoulos and his team at NTUA.

Generation of set-points requires an understanding of the capabilities
and the demands of the controllers, while monitoring execution and
diagnosing progress or failure relies on models of nominal behaviours of
the controllers and their interaction with the environment. Early work
on this is currently under preparation for submission.