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.

Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots

Robotic systems need advanced mobility capabilities to operate in complex, three-dimensional environments designed for human use, e.g., multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e.g., automatically ``returning home'' if communication between an operator and robot is lost during deployment. This work presents a novel method that enables mobile robots to robustly operate in multi-level environments by making it possible to autonomously locate and climb a range of different staircases. We present results wherein a wheeled robot works together with a quadrupedal system to quickly detect different staircases and reliably climb them. The performance of this novel staircase detection algorithm that is able to run on the heterogeneous platforms is compared to the current state-of-the-art detection algorithm. We show that our approach significantly increases the accuracy and speed at which detections occur.