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STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
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TLDR
In this article, the authors proposed an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time, which relies on rapid uncertainty-aware mapping and traversability evaluation, tail risk assessment using the Conditional Value-at-Risk (CVaR), and efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based predictive control.Abstract:
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube.read more
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PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
Sung-Kyun Kim,Amanda Bouman,Gautam Salhotra,David D. Fan,Kyohei Otsu,Joel W. Burdick,Ali-akbar Agha-mohammadi +6 more
TL;DR: In this article, a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadmaps), is proposed to bridge the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives.
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NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge.
Ali Agha,Kyohei Otsu,Benjamin Morrell,David D. Fan,Rohan Thakker,Angel Santamaria-Navarro,Sung-Kyun Kim,Amanda Bouman,Xianmei Lei,Jeffrey A. Edlund,Muhammad Fadhil Ginting,Kamak Ebadi,Matthew Anderson,Torkom Pailevanian,Edward Terry,Michael Wolf,Andrea Tagliabue,Tiago Stegun Vaquero,Matteo Palieri,Scott Tepsuporn,Yun Chang,Arash Kalantari,Fernando Chavez,Brett T. Lopez,Nobuhiro Funabiki,Gregory Miles,Thomas Touma,Alessandro Buscicchio,Jesus Tordesillas,Nikhilesh Alatur,Jeremy Nash,William Walsh,Sunggoo Jung,Hanseob Lee,Christoforos Kanellakis,John Mayo,Scott Harper,Marcel Kaufmann,Anushri Dixit,Gustavo J. Correa,Carlyn Lee,Jay Gao,Gene Merewether,Jairo Maldonado-Contreras,Gautam Salhotra,Maira Saboia da Silva,Benjamin Ramtoula,Yuki Kubo,Seyed Abolfazl Fakoorian,Alexander Hatteland,Taeyeon Kim,Tara Bartlett,Alex Stephens,Leon Kim,Chuck Bergh,Eric Heiden,Thomas Lew,Abhishek Cauligi,Tristan Heywood,Andrew Kramer,Henry A. Leopold,Hyungho Chris Choi,Shreyansh Daftry,Olivier Toupet,Inhwan Wee,Abhishek Thakur,Micah Feras,Giovanni Beltrame,George Nikolakopoulos,David Hyunchul Shim,Luca Carlone,Joel W. Burdick +71 more
TL;DR: NeBula as mentioned in this paper is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states).
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Risk-Averse Stochastic Shortest Path Planning.
TL;DR: In this article, the authors consider the stochastic shortest path planning problem in MDPs, i.e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
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Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
TL;DR: In this article, the authors proposed a non-sampling based method to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents' futures are generated by deep neural networks (DNNs).
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Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Nitish Dashora,Daniel Shin,Dhruv Shah,Henry A. Leopold,David D. Fan,Ali-akbar Agha-mohammadi,Nicholas Rhinehart,Sergey Levine +7 more
TL;DR: In this paper, the learning and non-learning-based components are designed in a way such that they can be effectively combined in a self-supervised manner in an open-world off-road navigation task.
References
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Stereo vision and rover navigation software for planetary exploration
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