Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains

• URL: http://arxiv.org/abs/2303.01169v1
• Date: Thu, 2 Mar 2023 11:19:44 GMT
• Title: Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
• Authors: Masafumi Endo, Tatsunori Taniai, Ryo Yonetani, Genya Ishigami
• Abstract summary: We propose a new path planning algorithm that explicitly accounts for erroneous traversability prediction. The proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.
• Score: 11.060425537315087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.

Related papers

• Trajectory Volatility for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy [34.19779754333234]
  This work proposes a unified framework to learn uncertainty-aware traction model and plan risk-aware trajectories. We parameterize Dirichlet distributions with the network outputs and propose a novel uncertainty-aware squared Earth Mover's distance loss. Our approach is extensively validated in simulation and on wheeled and quadruped robots.
  arXiv  Detail & Related papers  (2023-11-10T18:49:53Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles [82.65261980827594]
  We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. A multi-mode predictive control approach considers the possible intentions of the human drivers.
  arXiv  Detail & Related papers  (2023-03-08T17:14:57Z)
• RAP: Risk-Aware Prediction for Robust Planning [21.83865866611308]
  We introduce a new prediction objective to learn a risk-biased distribution over trajectories. This reduces the sample complexity of the risk estimation during online planning.
  arXiv  Detail & Related papers  (2022-10-04T04:19:15Z)
• Improving Diversity of Multiple Trajectory Prediction based on Map-adaptive Lane Loss [12.963269946571476]
  This study proposes a novel loss function, textitLane Loss, that ensures map-adaptive diversity and accommodates geometric constraints. Experiments performed on the Argoverse dataset show that the proposed method significantly improves the diversity of the predicted trajectories.
  arXiv  Detail & Related papers  (2022-06-17T09:09:51Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps [29.67295706224478]
  In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories. We learn the distribution with symmetric cross-entropy using occupancy grid maps as an explicit and scene-compliant approximation to the ground-truth distribution. In experiments, our method achieves state-of-the-art performance on the Stanford Drone dataset and Intersection Drone dataset.
  arXiv  Detail & Related papers  (2022-03-31T09:24:32Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks [58.29502185344086]
  In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks. It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
  arXiv  Detail & Related papers  (2021-09-22T12:46:04Z)
• Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics [0.0]
  Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. High level terrain classification is not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap. Online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover.
  arXiv  Detail & Related papers  (2020-09-21T21:56:44Z)

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.