Journal ArticleDOI
Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments
TLDR
This work presents a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional environments, including rough outdoor terrain, multilevel facilities, and more complex geometries, using a novel, constraint‐aware trajectory optimization paradigm.Abstract:
We present a practical approach to global motion planning and terrain assessment for ground robots in generic three-dimensional 3D environments, including rough outdoor terrain, multilevel faciliti...read more
Citations
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Journal ArticleDOI
Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning
TL;DR: This letter proposes a method to collect data from robot-terrain interaction and associate it to images, and shows that data collected can be used to train a convolutional network for terrain property prediction as well as weakly supervised semantic segmentation.
Posted Content
Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion
Amanda Bouman,Muhammad Fadhil Ginting,Nikhilesh Alatur,Matteo Palieri,David D. Fan,Thomas Touma,Torkom Pailevanian,Sung-Kyun Kim,Kyohei Otsu,Joel W. Burdick,Ali-akbar Agha-mohammadi +10 more
TL;DR: This paper discusses the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems and demonstrates the performance of the proposed solutions on physical systems in real-world scenarios.
Journal ArticleDOI
Safe Robot Navigation Via Multi-Modal Anomaly Detection
TL;DR: This work evaluates multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions and shows that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best.
Journal ArticleDOI
A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain
TL;DR: In this article, a sim-to-real pipeline for a mobile robot to learn how to navigate real-world 3D rough terrain environments is presented, using a deep reinforcement learning architecture to learn a navigation policy from training data obtained from the simulated environment and a unique combination of strategies to directly address the reality gap for such environments.
Journal ArticleDOI
Path Planning With Local Motion Estimations
TL;DR: A novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge, trained from a dataset of trajectories acquired in a self-supervised way is introduced.
References
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Journal ArticleDOI
A note on two problems in connexion with graphs
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Journal ArticleDOI
Real-time obstacle avoidance for manipulators and mobile robots
TL;DR: This paper reformulated the manipulator con trol problem as direct control of manipulator motion in operational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and geometric transformation.
Journal ArticleDOI
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Journal Article
Rapidly-exploring random trees : a new tool for path planning
TL;DR: The Rapidly-exploring Random Tree (RRT) as discussed by the authors is a data structure designed for path planning problems with high degrees of freedom and non-holonomic constraints, including dynamics.
Journal ArticleDOI
Sampling-based algorithms for optimal motion planning
Sertac Karaman,Emilio Frazzoli +1 more
TL;DR: In this paper, the authors studied the asymptotic behavior of the cost of the solution returned by stochastic sampling-based path planning algorithms as the number of samples increases.