The Autonomous Vehicle Laboratory is a cross-discipline, cross-group research team at UC San Diego led by the Contextual Robotics Institute Director, Dr. Henrik I. Christensen. The group’s research focus is to explore and develop robust autonomous car systems and architectures. From this, AVL aims to develop self-driving cars for mail-delivery and micro-transit applications on campus while collaborating with its network of industry experts. Please visit: http://avl.ucsd.edu
How would a robot understand its environment and make sense of semantics in the environment to better collaborate with a human world? How can multiple robots use the knowledge of these abstractions to communicate with each other and achieve a mission with minimal cost? These are a few of the questions that inspire the MSM team. This a cross-discipline group where we do full-stack robot research on topics ranging from perception to behavior learning all the way to multi-robot coordination.
Grounded within a manufacturing context, the QAP team focuses on precision execution, creative task planning and flexible autonomy for manufacturing systems. We want to break the traditional paradigm of hyper-calibrated, fully-programmed manufacturing systems by marrying knowledge-based algorithms with data-driven techniques for transferring solutions across the manufacturing space. Furthermore, as the world moves towards Industry 4.0, and generally robots being ubiquitous everywhere, we need to have more advanced, grounded theories of Human-Robot Interaction with a focus on communication for coordination. This project serves as a testbed for such experiments, as well.
Once a robot is deployed into an environment, there are various reliability issues that must be guaranteed and sometimes matter more than the algorithmic capacities of a robot. In the wild, communication security and execution reliability are the most base assumptions any robotic system relies on. To that end, we tackle topics like communication security for robots over the ROS communication layer and reliability assurance via context-based resource management for mobile robots with limited resources.