Tag: Jonathan West

  • Cooperative 3-D Mapping

    Cooperative 3-D Mapping

    Students: Jonathan West, Shakeeb Ahmad, Joseph Kloeppel

    Funding: MAST-CTA

    The capstone for the project under Army Research Labs (ARL) Micro-Autonomous Systems and Technology (MAST) included the task of exploring an unknown environment using a heterogeneous robotics test-bed. The problem motivates from the fact that in usual search and rescue operations, there is a need for cheap robots and sensors to be deployed without worrying much about their loss or damage. For that purpose, a set of bio-inspired robots, the miniROaCHes are assembled at the MARHES Lab. They are built out of chassis from Kamigami robots by Dash Robotics. They are made capable of running Linux and hence the Robot Operating System (ROS) by mounting Raspberry Pi Zeros on-board. They are also assembled so that a forward camera can be mounted on-board to capture images. The idea is to explore an unknown environment by utilizing this camera on each of the deployed miniROaCH. These pictures are taken from different random poses. In order to overcome the problem of low memory on the ground robots, the quadrotor hovers over each one of them turn-by-turn to copy these pictures and dump them to the base station via an optical communication link. This base station is connected to a cloud server where the 3-D map is generated. ROS is used as the main software framework for all robots.

  • F1/10 Racing

    F1/10 Racing

    Students (from left to right): Carolina Gomez, Rebecca Kreitinger, Greg Brunson, Jonathan West 

    Support: The University of New Mexico Department of Electrical & Computer Engineering, Sandia National Labs

    On the web: The Official Home of F1/10

    The UNM MARHES Lab racing team, LoboRacers won 2nd place in the first-ever F1/10 International Autonomous Racing Competition at Carnegie Mellon University on October 1-2, 2016. The F1/10 Competition involves assembling, programming, and racing an autonomous car of the one-tenth scale of an actual Formula One race car. The objective of this competition is to motivate students to develop advanced algorithms that could be applied to self-driving cars in the future. The cars are programmed utilizing Python and Robot Operating System.

    At the moment, the LoboRacers’ car runs a simple wall following an algorithm that uses distance measurements from five different angles collected with the LiDAR sensor. This way the car can determine how close it is to obstacles and at what rate is that distance from the obstacle changing, such that it may adjust its speed and/or turn accordingly to avoid collisions. The LoboRacers team is currently working on improving the car’s localization and mapping algorithms in preparation for the next race in 2017. The localization and mapping driving approach involves first creating a map of the desired path or “racetrack” by manually driving the car through remote control. Afterward, the driving algorithm is applied, and through the car’s odometry, it will be able to know where it is on the map and expect any upcoming turns, again adjusting its speed to avoid colliding with the edges of the track. This method should allow the car to drive faster and more efficiently, as it will no longer need to “sense” obstacles in real-time and can instead anticipate them and be prepared to execute its speed and direction altering commands.

    Related Articles:
    UNM Electrical Engineering Team Places 2nd in Racing Contest, UNM Newsroom

    UNM Electrical Engineering Students to Compete in National Racing Competition, UNM Newsroom

    Related Videos:
    F1/10 Autonomous Racing