High Speed Convoy in Unstructured Indoor Environments

Practical operations of coordinated fleets of mobile robots in different environments reveal benefits of maintaining small distances between robots as they move at higher speeds. This is counter-intuitive in that as speed increases, increased distances would give robots a larger time to respond to sudden motion variations in surrounding robots. However, there is a desire to have lower inter-robot distances in examples like autonomous trucks on highways to optimize energy by vehicle drafting or smaller robots in cluttered environments to maintain communication, etc. This work introduces a model based control framework that directly takes non-linear system dynamics into account. Each robot is able to follow closer at high speeds because it makes predictions on the state information from its adjacent robots and biases it's response by anticipating adjacent robots' motion. In contrast to existing controllers, our non-linear model based predictive decentralized controller is able to achieve lower inter-robot distances at higher speeds. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots.

Safe Adaptation Using Energy Functions

Adaptation has long been considered to be an important capability for autonomous systems. As these systems modify their behavior, questions regarding how to develop safety guarantees for such systems continue to be of interest. We propose a novel adaptive safe control methodology that allows a robotic system to adapt in a constrained environment without unintentionally colliding with that environment. A methodology for designing a scalar energy function (safety index) for an adaptive system with parametric uncertainty is described in this paper, and we demonstrate our methodology by realizing a safety controller for a simulated two-link robotic manipulator.