Yesterday, Rocio Dominguez Medrano presented her MASc thesis seminar, titled “development of a sustainable solution for the elimination of helium in the copper cold spray process for used nuclear fuel containers”. Supervisor Professor Bertrand Jodoin was in attendance for the seminar but had to leave right away to be Uber-papa. So, we took the traditional photo with her lab-mates who were all there in support. Congrats, Rocio!
Last Friday, MASc candidate Clayton Fox presented his thesis work titled “Reaction models for hydrocarbon mixtures using continuous thermodynamics”. Thesis supervisor Dr. Bill Hallett was in attendance for the seminar. Thanks to both for an interesting seminar!
This past Friday, Cameron Frazier presented his MASc thesis seminar, with a talk titled “Re-Active Vector Equilibrium (RAVE): A Novel Method of Autonomous Rover Local Navigation Using Potential Fields.” The abstract of the seminar is below. Check out the photo of Cameron with his happy supervisor! 😉
The use of potential eld based navigation schemes in robotics has been limited by inherent local minima issues. Local minima traps, small passages, unstable motion, and targets positioned near objects all pose major concerns when using potential fields for local vehicle control. This work proposes a new algorithm, “Re-Active Vector Equilibrium” (RAVE) that mitigates many of these issues. The vehicle representation model is expanded to use multiple points and the addition of two forces, a velocity dependent risk force and a velocity and direction dependent tangential force. Expanding the vehicle representation model from a single reactive point to a series of points that define the vehicle body is also done, providing better and simpler vehicle control. This has the effect of simplifying the required calculations at the cost of increasing the calculation count. The risk force allows for dynamic adaptation to the immediate environment by acting in opposition to the net obstacle force, and is
inversely proportional to the vehicle speed. The tangential force encourages better wall-following behaviour and provides a biasing mechanism to resolve obstacle aligned with target local minima issues. Presented here is a brief background on the topic, a description of the proposed algorithm, presentation of simulations and results, and presentation of implementation videos.
Cameron and supervisor (Dr. Baddour)