Last week, MASc candidate Hubert Villeneuve presented his thesis work, giving a talk titled “height estimation of a blimp unmanned aerial vehicle using inertial measurement unit and infrared camera”. The work was done under the supervision of Dr. Eric Lanteigne, who was also present for the talk. Well done, Hubert, congrats on presenting your work!
And coming up next on the 19th May at 10:00am is Charles Blouin. Again, we’ll be in CBY B012 and I’ve been told there will be master coffee making and expert cookie tasting to go along with the seminar.
Charles will be talking about “Trajectory Optimization for a Small Airship Using an Optimal Control Solver”.
Date: May 19th, 2015
Location: CBY B012
Presented by: Charles Blouin, M.A.Sc. Candidate
Supervisors: Eric Lanteigne, Wail Gueaieb
Airships demonstrate long endurance and long range capabilities. Those characteristics make them ideal for telecommunication, surveillance and long endurance missions. To maximize flight time or reduce the time required to attain an objective, optimal trajectories are computed before a dirigible performs a maneuver. This seminar will demonstrate how the optimal trajectory problem can be formulated as a general optimization problem, and how it can be solved with a pseudo-spectral optimal control solver. Experimental and simulation results will be presented and discussed. In general, optimal control solvers can be used to optimize dynamical systems with respect to a performance index and subject to time varying inputs.
So, after a break for reading week, our MCG seminars are back. Fiirst up, we had Arian Panah presenting last Friday. Sadly, supervisor Dr Davide Spinello was AWOL… So I only have photos of our soon-to-be finishing student.
Jin Bai presented his MASc thesis seminar last Friday on “Robot Navigation using Velocity Potential Fields and Particle Filters for Obstacle Avoidance”. Abstract of the talk is below along with photos of Jin and his supervisor, Dr. Dan Necsulescu.
Robot navigation using the Particle Filter based FastSLAM approach for obstacle avoidance derived from a modified Velocity Potential Field method was investigated and will be introduced. A switching controller was developed to deal with robot’s efficient turning direction when close to obstacles. The determination of the efficient turning direction is based on the local map robot derived from its on-board local sensing. The estimation of local map and robot path was implemented using the FastSLAM approach. A particle filter was utilized to obtain estimated robot path and obstacles (local map). When robot sensed only obstacles, the estimated robot positions was regarding to obstacles based the measurement between the robot and obstacles. When the robot detected the goal, estimation of robot path will switch to estimation with regard to the goal. Both simulation and experimental results illustrated that estimation with regard to the goal performs better than estimation regarding only to obstacles, because when robot travelled close to the goal, the residual error between estimated robot path and the ideal robot path becomes monotonously decreasing. When robot reached the goal, the estimated robot position and the ideal robot position converge. We investigated our proposed approach in two typical robot navigation scenarios. Simulations were accomplished using MATLAB, and experiments were conducted with the help of both MATLAB and LabVIEW. In simulations and experiments, the robot successfully chose efficiently turning direction to avoid obstacles and finally reached the goal.
The next speaker in our student seminar series will be Farid Sheikhi. Farid will be giving a talk titled “Entropy Filter for Anomaly Detection with Eddy Current Remote Field Sensors”.
Where: CBY B205
Date: Wednesday June 19th, 2013
The seminar of the talk is given below. Hope to see you all there!
Entropy Filter for Anomaly Detection with Eddy Current Remote Field Sensors
Candidate: Farid Sheikhi
Supervisor: Dr. Davide Spinello
We consider the problem of extracting a specific feature from a noisy signal generated by a multi-channels Remote Field Eddy Current Sensor. The sensor is installed on a mobile robot whose mission is the detection of anomalous regions in metal pipelines. Given the presence of noise that characterizes the data series, anomaly signals could be masked by noise and therefore difficult to identify in some instances. In order to enhance signal peaks that potentially identify anomalies we consider an entropy filter built on a posteriori probability density functions associated with data series. Thresholds based on the Neyman-Pearson criterion for hypothesis testing are derived. The algorithmic tool is applied to the analysis of data from a portion of pipeline with a set of anomalies introduced at predetermined locations. Critical areas identifying anomalies capture the set of damaged locations, demonstrating the effectiveness of the filter in detection with Remote Field Eddy Current Sensor.