Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
- URL: http://arxiv.org/abs/2203.13429v1
- Date: Fri, 25 Mar 2022 03:08:02 GMT
- Title: Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
- Authors: Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How
- Abstract summary: This work proposes a new representation of traversability based exclusively on robot speed that can be learned from data.
The proposed algorithm learns to predict a distribution of speeds the robot could achieve, conditioned on the environment semantics and commanded speed.
Numerical simulations demonstrate that the proposed risk-aware planning algorithm leads to faster average time-to-goals.
- Score: 39.54575497596679
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motion planning in off-road environments requires reasoning about both the
geometry and semantics of the scene (e.g., a robot may be able to drive through
soft bushes but not a fallen log). In many recent works, the world is
classified into a finite number of semantic categories that often are not
sufficient to capture the ability (i.e., the speed) with which a robot can
traverse off-road terrain. Instead, this work proposes a new representation of
traversability based exclusively on robot speed that can be learned from data,
offers interpretability and intuitive tuning, and can be easily integrated with
a variety of planning paradigms in the form of a costmap. Specifically, given a
dataset of experienced trajectories, the proposed algorithm learns to predict a
distribution of speeds the robot could achieve, conditioned on the environment
semantics and commanded speed. The learned speed distribution map is converted
into costmaps with a risk-aware cost term based on conditional value at risk
(CVaR). Numerical simulations demonstrate that the proposed risk-aware planning
algorithm leads to faster average time-to-goals compared to a method that only
considers expected behavior, and the planner can be tuned for slightly slower,
but less variable behavior. Furthermore, the approach is integrated into a full
autonomy stack and demonstrated in a high-fidelity Unity environment and is
shown to provide a 30\% improvement in the success rate of navigation.
Related papers
- RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving [13.101416329887755]
We present RoadRunner, a framework capable of predicting terrain traversability and an elevation map directly from camera and LiDAR sensor inputs.
RoadRunner enables reliable autonomous navigation, by fusing sensory information, handling of uncertainty, and generation of contextually informed predictions.
We demonstrate the effectiveness of RoadRunner in enabling safe and reliable off-road navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.
arXiv Detail & Related papers (2024-02-29T16:47:54Z) - Neural Potential Field for Obstacle-Aware Local Motion Planning [46.42871544295734]
We propose a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint.
Our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings.
Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
arXiv Detail & Related papers (2023-10-25T05:00:21Z) - Incremental 3D Scene Completion for Safe and Efficient Exploration
Mapping and Planning [60.599223456298915]
We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning.
We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy.
Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
arXiv Detail & Related papers (2022-08-17T14:19:33Z) - Learning High-Speed Flight in the Wild [101.33104268902208]
We propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds.
The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion.
By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments.
arXiv Detail & Related papers (2021-10-11T09:43:11Z) - Integrating Deep Reinforcement and Supervised Learning to Expedite
Indoor Mapping [0.0]
We show that combining the two methods can shorten the mapping time, compared to frontier-based motion planning, by up to 75%.
One is the use of deep reinforcement learning to train the motion planner.
The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor.
arXiv Detail & Related papers (2021-09-17T12:07:07Z) - Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense
Forest Canopy [48.51396198176273]
We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments.
We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models.
A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
arXiv Detail & Related papers (2021-09-14T07:24:53Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z) - Online search of unknown terrains using a dynamical system-based path
planning approach [0.0]
This study introduces a new scalable technique that helps a robot to steer away from the obstacles and cover the entire space in a short period of time.
Using this technique resulted in 49% boost, on average, in the robot's performance compared to the state-of-the-art planners.
arXiv Detail & Related papers (2021-03-22T14:00:04Z) - End-to-end Interpretable Neural Motion Planner [78.69295676456085]
We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios.
We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations.
We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
arXiv Detail & Related papers (2021-01-17T14:16:12Z) - Autonomous UAV Exploration of Dynamic Environments via Incremental
Sampling and Probabilistic Roadmap [0.3867363075280543]
We propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM)
Our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.
arXiv Detail & Related papers (2020-10-14T22:52:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.