Luka Antonyshyn

Alexander Krawciw, Luka Antonyshyn, Sven Lilge et al.

A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to the Moon will need to be moved from a landing site to other key locations that may be up to 5 km away. Teach and repeat navigation is well suited to this task as utility rovers will need to repeat these cargo routes many times. One of the most significant challenges involves the modules that will be assembled together to form the habitat. Canada is studying potential Lunar Utility Vehicle (LUV) designs to carry these large payloads between the landing site and the location of the habitat. As the details of the cargo continue to evolve, using two, smaller LUVs to carry cargo together would provide high capacity and mission flexibility. In this paper, we develop and implement a distributed model-predictive controller that allows vehicles to carry cargo that is shared between them. The algorithm is compared to baselines in small-scale before being implemented onboard two 800 kg path-to-flight rovers and field tested carrying a 475 kg cargo between them. A custom cargo coupling decouples the kinematics of each vehicle while fully supporting the cargo's mass. In our field test, the rovers maintain a relative separation error of 9.2 cm and maximum error of 33.4 cm. This multi-vehicle control architecture retains the high-quality path tracking of lidar teach and repeat for each rover. We demonstrate that kinematic freedom of the vehicles allows a single controller to provide mission improvements for other operations as well.

Luka Antonyshyn, Paulo Ricardo Marques de Araujo, Sidney Givigi

This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication, a common coordinate system, or global positioning. The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process, allowing accurate estimation of a leader vehicle's relative trajectory on the SE(2) manifold. A geometric model predictive controller is further incorporated to fully exploit the manifold-based trajectory predictions for improved control performance. The system can handle arbitrary nonlinear trajectories with varying speeds and motion profiles while reducing the need for expert-based domain knowledge for the design of trajectory following systems, even under long trajectory delays. The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations, as well as in experiments using real robotic vehicles.