Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, and planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions and respond before an issue can occur. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encodes fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://youtu.be/aydHxLGmnx8

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC’s effectiveness by bench-marking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 gram quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance. TinyMPC is publicly available at https://tinympc.org.

We introduce a novel algorithm for ride-hailing services, called Predictive Leafcutter Ant optimization for Networks (PLAN), that is shown to reduce vehicular fuel costs and customer wait times, especially for clustered requests. PLAN combines traditional ant colony optimization, network flow optimization, and task-partitioning inspired by leafcutter ant foraging patterns. At a high level, PLAN integrates these methods to predictively station vehicles at locations of high activity (hot spots) to minimize both customer wait times and vehicle dispatch costs and to selectively disregard requests if they exceed a dynamic cost-to-benefit ratio. The algorithm is tested on a modeled city with simulated ride requests as well as on real-world New York City commuter data. The performance of PLAN is compared to that of a control algorithm, and the conditions under which PLAN outperforms the control algorithm are identified. Results show that for the New York City data with the presence of quantifiably significant hot spots, PLAN can decrease customer wait times by up to 36% (and proportionally dispatch fuel costs) in a test case with a limited service fleet and produce higher reductions with larger fleets.