MASc candidate Seyed Alireza Mirghasemi presented his thesis seminar titled “Fractional-Order Flight Controller for Quadcopter Subjected to Ground Effect” last Friday. Supervisor Professor Dan Necsulescu was in attendance for the presentation. Congrats, Alireza!
Welcome back to everyone for another academic year of MCG seminars! It was good to see such a full house on our first day.
To kick things off for the semester, MASc candidate Nathaniel Mailhot presented his thesis seminar titled “Pupil Tracking and Control of a Laser Based Power System for a Vision Restoring Retinal Implant”. A really interesting talk! Supervisor Dr. Spinello was also present for the seminar and photo-op. Congrats, Nathaniel!
For the last seminar of winter 2018, Jasmeet Singh Ladoiye presented his MASc work at our MCG seminar series. Jasmeet’s talk was titled “control of surgical robot using model predictive control with time delay”. Unfortunately neither thesis supervisor (Drs. Dan Necsulescu and Jurek Sasiadek) could be in attendance. Congratulations Jasmeet on your seminar and on getting closer to completing your MASc! Thanks to everyone that participated in this semester’s seminar series. See you next semester!
Yesterday, Aliakbar Baldiwala presented his MASc Thesis work, titled “IOT for fleet management”. Supervisor Dr. Dan Necsulescu was in attendance. Thank you to Aliakbar and Dr. Necsulescu for an informative seminar!
Mohit Sain, working under the supervision of Dr. Dan Necsulescu presented his MASc seminar yesterday. The title of his talk was “portable monitoring and navigation control system for helping visually impaired people”. It was a very interesting talk! Congrats, Mohit!
PhD candidate Chengming Luo presented his thesis seminar titled “Automatic Guidance of Agricultural Wide-Span Implement Carrier” last Tuesday. Chengming worked under the supervision of our very own former Dean, Dr. Claude Laguë. Dr. Laguë was present for the seminar and I got the chance to take a photo of student with supervisor. Thanks for an interesting talk and congratulations to Chengming for getting close to finishing his PhD!
On Friday, Ahmad Alsayed presented his MASc seminar. The title of his talk was “Pitch and altitude control of an unmanned airship with sliding gondola”. Thesis supervisor, Dr. Eric Lanteigne was present – well done Ahmad and Dr. Lanteigne!
Last Friday, Hamid Fallah Haghmohammadi presented his thesis seminar on “fever detection for dynamic human environment”. Supervisor Dr. Dan Necsulescu was present in the audience. Congratulations for a successful thesis seminar presentation.
Honours of the last seminar of the year went to Adekunle Adepegba. Ade presented his thesis seminar on “Multi-Agent Area Coverage Control using Reinforcement Learning”. Supervisor Dr. Davide Spinello was in attendance.
Congrats to everyone who presented a seminar this year and thanks to everyone who participated. I hope everyone has a great and restful holiday season and here’s to seeing you all in 2016!
…and adding to my slightly late seminar posts, Kai Yan presented his MASc thesis work last week with supervisor Dr. Davide Spinello on hand. Pic below!
Xuqing Le presented his MASc work on Fire detecting robots last week. Supervisor Dr. Necsulescu was on hand for the presentation.
Last Friday, Mostafa Fallah presented the results of his MASc research with co-supervisors Dr. Davide Spinello and Dr. Suruz Miah in attendance. Mostafa’s wife also came out to support her husband. Pics below!
All are cordially invited to a graduate seminar talk to be given by Mostafa Fallah. He will give talk titled “Coordinated Deployment of Multiple Autonomous Agents in Area Coverage Problems with Evolving Risk.”
Date: Friday March 20th
Room: CBY D207
Coordinated missions with platoons of autonomous agents are rapidly becoming popular because of technological advances in computing, networking, miniaturization and combination of electromechanical systems. These multi-agents networks coordinate their actions to perform challenging spatially-distributed tasks such as search, survey, exploration, and mapping. Environmental monitoring and locational optimization are among the main applications of the emerging technology of wireless sensor networks where the optimality refers to the assignment of sub-regions to each agent, in such a way that a suitable coverage metric is maximized. Usually the coverage metric encodes a distribution of risk defined on the area, and a measure of the performance of individual robots with respect to points inside the region of interest. The risk density can be used to quantify spatial distributions of risk in the domain.
The solution of the optimal control problem in which the risk measures are not time varying is well known in the literature, with the optimal configuration of the robots given by the centroids of the Voronoi regions forming a centroidal Voronoi tessellation of the area. In other words, when the set of mobile robots converge to the corresponding centroids of the Voronoi tessellation dictated by the coverage metric, the coverage itself is maximized.
In this work, we consider a time-varying risk density function evolving according to a diffusion equation with varying boundary conditions that quantify a time-varying risk on the border of the workspace. Boundary conditions model a time varying flux of external threats coming into the area, averaged over the boundary length, so that we do not consider the individual kinematics of incoming threats but rather their averaged, distributed effect. This approach is similar to the one commonly adopted in continuum physics, in which kinematic descriptors are averaged over spatial domain and suitable continuum fields are introduced to describe their evolution. By adopting a first gradient constitutive relation between the flux and the density, we obtain a simple diffusion equation. Asymptotic convergenceand optimality of the non-autonomous system are studied by means of Barbalat’s Lemma and connections with varying boundary conditions are established. Some criteria on time-varying boundary conditions and its evolution are established to guarantee the stabilities of agents’ trajectories. A set of numerical simulations illustrate the theoretical results.
Everyone is cordially invited to hear Arian Panah present a talk on his MASc work: “Nonuniform Coverage with Time-Varying Risk Density Function”. The abstract of the talk is attached. The talk will start promptly at 2:30pm.
Date: Friday February 27
Room: CBY D207
Multi-agent systems are extensively used in several civilian and military applications, such as surveillance, space exploration, cooperative classification, and search and rescue, to name a few. An important class of applications involves the optimal spatial distribution of a group of mobile robots on a given area, where the optimality refers to the assignment of subregions to the robots, in such a way that a suitable coverage metric is maximized. Typically the coverage metric encodes a risk distribution defined on the area, and a measure of the performance of individual robots with respect to points inside the region of interest. The risk density can be used to assign spatial distributions of risk in the region, as for example typically happens in surveillance applications in which high value units have to be protected against external threats coming into a given area surrounding them.
The solution of the optimal control problem in which the metric is autonomous (a function of time only through the state of the robots) is well known in the literature, with the optimal location of the robots given by the centroids of the Voronoi regions forming a Voronoi tessellation of the area. In other words, when the set of mobile robots configure themselves as the centroids of the Voronoi tessellation dictated by the coverage metric, the coverage itself is maximized.
In this work we advance on this result by considering a generalized area control problem in which the coverage metric is non-autonomous, that is the coverage metric is time varying independently of the states of the robots. This generalization is motivated by the study of coverage control problems in which the coordinated motion of a set of mobile robots accounts for the kinematics of objects penetrating from the outside. Asymptotic convergence and optimality of the non-autonmous system are studied by means of Barbalat’s Lemma, and connections with the kinematics of the moving intruders is established. Several numerical simulation results are used to illustrate theoretical predictions.
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.