Leonardo Bobadilla

Sourav Raxit, Abdullah Al Redwan Newaz, Paulo Padrao et al.

This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often struggle with kinodynamic constraints such as velocity and acceleration limits, the BOW Planner excels by concentrating on a planning window of reachable velocities and employing CBO to sample control inputs efficiently. This approach enables the planner to manage high-dimensional objective functions and stringent safety constraints with minimal sampling, ensuring rapid and secure trajectory generation. Theoretical analysis confirms the algorithm's asymptotic convergence to near-optimal solutions, while extensive evaluations in cluttered and constrained settings reveal substantial improvements in computation times, trajectory lengths, and solution times compared to existing techniques. Successfully deployed across various real-world robotic systems, the BOW Planner demonstrates its practical significance through exceptional sample efficiency, safety-aware optimization, and rapid planning capabilities, making it a valuable tool for advancing robotic applications. The BOW Planner is released as an open-source package and videos of real-world and simulated experiments are available at https://bow-web.github.io.

Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes et al.

Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels of precision in different areas. For example, precise localization may be crucial near obstacles but less critical in open spaces. We present GUIDE (Generalized Uncertainty Integration for Decision-Making and Execution), a framework that integrates these task-specific requirements into navigation policies via Task-Specific Uncertainty Maps (TSUMs). By assigning acceptable uncertainty levels to different locations, TSUMs enable robots to adapt uncertainty management based on context. When combined with reinforcement learning, GUIDE learns policies that balance task completion and uncertainty management without extensive reward engineering. Real-world tests show significant performance gains over methods lacking task-specific uncertainty awareness.

Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes et al.

Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth operations or resource-constrained settings, accessing high-precision localization comes at a significant cost, forcing robots to rely primarily on less precise state estimates. Our key observation is that different tasks require varying levels of precision in different regions: a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precision elsewhere. In this paper, we present a planning method for integrating task-specific uncertainty requirements directly into navigation policies. We introduce Task-Specific Uncertainty Maps (TSUMs), which abstract the acceptable levels of state estimation uncertainty across different regions. TSUMs align task requirements and environmental features using a shared representation space, generated via a domain-adapted encoder. Using TSUMs, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into robot decision-making. We find that TSUMs provide an effective way to abstract task-specific uncertainty requirements, and conditioning policies on TSUMs enables the robot to reason about the context-dependent value of certainty and adapt its behavior accordingly. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies that effectively balance task completion and uncertainty management without explicit reward engineering. We evaluate GUIDE on various real-world robotic navigation tasks and find that it demonstrates significant improvement in task completion rates compared to baseline methods that do not explicitly consider task-specific uncertainty.

Opeyemi S. Orioke, Tauhidul Alam, Joseph Quinn et al.

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new long-range autonomous underwater vehicle called Tethys is adapted to study different oceanic phenomena. Additionally, an ocean environment has external forces and moments along with changing water currents which are generally not considered in a vehicle kinematic model. In this scenario, it is not enough to generate a simple trajectory from an initial location to a goal location in an uncertain ocean as the vehicle can deviate from its intended trajectory. As such, we propose to compute a feedback plan that adapts the vehicle trajectory in the presence of any modeled or unmodeled uncertainties. In this work, we present a feedback motion planning method for the Tethys vehicle by combining a predictive ocean model and its kinematic modeling. Given a goal location, the Tethys kinematic model, and the water flow pattern, our method computes a feedback plan for the vehicle in a dynamic ocean environment that reduces its energy consumption. The computed feedback plan provides the optimal action for the Tethys vehicle to take from any location of the environment to reach the goal location considering its orientation. Our results based on actual ocean model prediction data demonstrate the applicability of our method.

Franklin Abodo, Andrew Berthaume, Stephen Zitzow-Childs et al.

Compute and memory constraints have historically prevented traffic simulation software users from fully utilizing the predictive models underlying them. When calibrating car-following models, particularly, accommodations have included 1) using sensitivity analysis to limit the number of parameters to be calibrated, and 2) identifying only one set of parameter values using data collected from multiple car-following instances across multiple drivers. Shortcuts are further motivated by insufficient data set sizes, for which a driver may have too few instances to fully account for the variation in their driving behavior. In this paper, we demonstrate that recent technological advances can enable transportation researchers and engineers to overcome these constraints and produce calibration results that 1) outperform industry standard approaches, and 2) allow for a unique set of parameters to be estimated for each driver in a data set, even given a small amount of data. We propose a novel calibration procedure for car-following models based on Bayesian machine learning and probabilistic programming, and apply it to real-world data from a naturalistic driving study. We also discuss how this combination of mathematical and software tools can offer additional benefits such as more informative model validation and the incorporation of true-to-data uncertainty into simulation traces.

Tauhidul Alam, Gregory Murad Reis, Leonardo Bobadilla et al.

The underactuated vehicles are apposite for the long-term deployment and data collection in spatiotemporally varying marine environments. However, these vehicles need to estimate their positions (states) with intrinsic sensing in their long-term trajectories. In previous studies, autonomous underwater vehicles have commonly used vision and range sensors for autonomous state estimation. Inspired by the intrinsic sensing and the persistent deployment, we investigate the localization problem (state estimation) for an inexpensive and underactuated drifting vehicle called a drifter. In this paper, we present a localization method for the drifter making use of the observations of a proprioceptive sensor, i.e., compass. We create the water flow pattern within a given region from ocean model predictions, develop a stochastic motion model, and analyze the persistent water flow behavior. Given a distribution of initial deployment states of the drifter at a particular depth of the water column within the region and the water flow pattern, our method finds attractors and their transient groups at the given depth as the persistent behavior of the water flow. A most-likely localized trajectory of the drifter for a sequence of compass observations is generated based on the persistent behavior of the water flow and hidden Markov model. Our simulation results based on data from ocean model predictions substantiate good performance of our proposed localization method with a low error rate of the state estimation in the long-term trajectory of the drifter.

Md Mahbubur Rahman, Franklin Abodo, Leonardo Bobadilla et al.

In communication-denied or contested environments, Line-of-Sight (LoS) communication (e.g free space optical communication using infrared or visible light) becomes one of the most reliable and efficient ways to send information between geographically scattered mobile units. In this paper, we consider the problem of planning optimal locations and trajectories for a group of autonomous vehicles to see a set of units that are dispersed in an environment with obstacles. The contributions of the paper are the following: 1) We propose centralized and distributed algorithms to verify that the vehicles and units form a connected network through LoS; 2) We present an algorithm that can maintain visibility-based connectivity, if possible, by relocating a single vehicle; and 3) We study the computational