Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments

A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing work typically assumes no wheel slip or uses the expected traction for motion planning, where the predicted trajectories provide a poor indication of the actual performance if the terrain traction has high uncertainty. In contrast, this work proposes to analyze terrain traversability with the empirical distribution of traction parameters in unicycle dynamics, which can be learned by a neural network in a self-supervised fashion. The probabilistic traction model leads to two risk-aware cost formulations that account for the worst-case expected cost and traction. To help the learned model generalize to unseen environment, terrains with features that lead to unreliable predictions are detected via a density estimator fit to the trained network's latent space and avoided via auxiliary penalties during planning. Simulation results demonstrate that the proposed approach outperforms existing work that assumes no slip or uses the expected traction in both navigation success rate and completion time. Furthermore, avoiding terrains with low density-based confidence score achieves up to 30% improvement in success rate when the learned traction model is used in a novel environment.Comment: To appear in IROS23. Video and code: https://github.com/mit-acl/mppi_numb

RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation

A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan in, due to inaccurate representation of unknown space. Additionally, existing planners such as MPPI do not consider speeds in known free and unknown space separately, leading to slower overall plans. The RAMP pipeline proposed here solves these issues using new mapping and planning methods. This work first presents ground point inflation with persistent spatial memory as a way to generate accurate occupancy grid maps from classified pointclouds. Then we present an MPPI-based planner with embedded variability in horizon, to maximize speed in known free space while retaining cautionary penetration into unknown space. Finally, we integrate this mapping and planning pipeline with risk constraints arising from 3D terrain, and verify that it enables fast and safe navigation using simulations and hardware demonstrations.Comment: 7 pages submitted to ICRA 202

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision, but challenges remain to properly quantify and mitigate risks due to uncertainties in learned models. This work efficiently quantifies both aleatoric and epistemic uncertainties by learning discrete traction distributions and probability densities of the traction predictor's latent features. Leveraging evidential deep learning, we parameterize Dirichlet distributions with the network outputs and propose a novel uncertainty-aware squared Earth Mover's distance loss with a closed-form expression that improves learning accuracy and navigation performance. The proposed risk-aware planner simulates state trajectories with the worst-case expected traction to handle aleatoric uncertainty, and penalizes trajectories moving through terrain with high epistemic uncertainty. Our approach is extensively validated in simulation and on wheeled and quadruped robots, showing improved navigation performance compared to methods that assume no slip, assume the expected traction, or optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project website: https://xiaoyi-cai.github.io/evora

Trans-Neptunian objects found in the first four years of the Dark Energy Survey

We present a catalog of 316 trans-Neptunian bodies (TNOs) detected from the first four seasons ("Y4" data) of the Dark Energy Survey (DES). The survey covers a contiguous 5000 deg(2) of the southern sky in the grizY optical/NIR filter set, with a typical TNO in this part of the sky being targeted by 25-30 Y4 exposures. This paper focuses on the methods used to detect these objects from the 60,000 Y4 exposures, a process made challenging by the absence of the few-hour repeat observations employed by TNO-optimized surveys. Newly developed techniques include: transient/moving object detection by comparison of single-epoch catalogs to catalogs of "stacked" images; quantified astrometric error from atmospheric turbulence; new software for detecting TNO linkages in a temporally sparse transient catalog, and for estimating the rate of spurious linkages; use of faint stars to determine the detection efficiency versus magnitude in all exposures. Final validation of the reality of linked orbits uses a new "sub-threshold confirmation" test, wherein we demand the object be detectable in a stack of the exposures in which the orbit indicates an object should be present, but was not individually detected. This catalog contains all validated TNOs which were detected on >= 6 unique nights in the Y4 data, and is complete to r less than or similar to 23.3 mag with virtually no dependence on orbital properties for bound TNOs at distance 30 au d 0.3 mag more depth, and arcs of >4 yr for nearly all detections.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Hexabot for Geology data collection and Soil Science

Geology data collection and soil science are interconnected but two different field of work. As the name suggests Geology focuses on study of minerals and Formation of earths and rocks, while soil science investigate the composition and formation of soil. The escalating interest for Scientist to Explore more diverse Environment rose a issue of safety of monitoring crew and cost of expensive operators have also been taken into accounts. In such conditions Autonomous Vehicle are being assigned to perform such task such that the data is collected without having Human interventions, but at some point, these vehicle fails due to some technical failures and their adaptability with the Environment. In this paper we have researched different types of robots and proposed Hexabot for geological data collection. How technology helps to function and behave with humans and some unique technologies life can be easy. The technology used after a collision or how the robots can be prepared to survive in different parameters and various deployable techniques for the robots. Other various technologies with the help of them the robots will be easier to control by normal citizen. Enhances the capability of survival while facing any major collisions

Developing an Optimized UI for Traffic Incident Managers

Traffic Incident Managers (TIMs) coordinate first responders and help resolve traffic-related incidents. Currently, some use over fifteen different software applications with unique functionalities across three monitors to manage incidents, leading to redundant data entry, unnecessary task switching, and delayed responses. 40 hours of TIMs’ screens were recorded during their normal work hours at the Iowa Department of Transportation (DoT). The resulting task analysis from these videos greatly influenced the design of a simplified, web-based, user interface (UI) prototype. The new UI offers a 42.9% reduction in the steps required to manage an incident by combining the functionality of the fifteen different applications used in the existing system into a single, structured UI. This research approach offers a UI model to other DoTs that can lead to faster and more effective incident management.This is a manuscript of a proceeding published as Helgerson, Andrina, Jamiahus Walton, Celia Loya, Christopher Kawell, Katherine Atwell, Quinn Monaghan, Lakshay Ahuja, Hesham Hassan, Stephen B. Gilbert, and Anuj Sharma. "Developing an Optimized UI for Traffic Incident Managers." Proceedings of the Human Factors and Ergonomics Society 62, no. 1 (2018): 292-296. DOI: 10.1177%2F1541931218621067. Posted with permission.</p