Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments (2210.00153v3)

Abstract: 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.

• The paper’s main contribution is using empirical traction distributions within a probabilistic framework to inform risk-aware motion planning in off-road environments.
• It introduces two risk-aware cost models, CVaR-Cost and CVaR-Dyn, that account for worst-case traction scenarios to enhance navigation efficiency.
• Simulation results show up to a 30% improvement in navigation success by avoiding low-confidence terrains using a self-supervised neural network approach.

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

The paper "Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments" addresses the challenges of off-road robotic navigation, which often suffers from the uncertainty inherent in varied terrain traction properties. The research introduces a novel approach that goes beyond the typical assumptions of no slip or expected traction, instead employing a probabilistic model of terrain traversability using a traction distribution within a unicycle dynamic model.

The core contribution of this research is the use of empirical traction distributions to inform motion planning decisions. The traction properties in off-road terrains can vary significantly, even within visually or semantically similar classes. This research leverages self-supervised learning to capture the traction distributions, which are crucial for assessing terrain traversability in a stochastic sense. The use of neural networks to learn these distributions allows for a more nuanced analysis than existing models which typically rely on point estimates, assuming uniformity in traction properties.

Two risk-aware cost formulations are introduced to account for the uncertainty in terrain traction, aiming to improve navigation success and efficiency. The worst-case expected cost, or CVaR-Cost, computes the expected costs in the upper quantile of sampled traction values, incorporating a comprehensive evaluation of potential navigation outcomes. Meanwhile, the worst-case expected system parameters, or CVaR-Dyn, consider the worst-case expected traction for path planning, simplifying the computational demands while maintaining risk sensitivity.

Significant performance improvements were observed in simulations, where the proposed approach outperformed existing methods that assume nominal traction or employ expected traction for path planning. Importantly, avoiding terrains with low confidence scores, derived from a Gaussian Mixture Model (GMM) fitted to the latent space of the trained NN, contributed to a marked increase in navigation success rates, with improvements of up to 30% noted when the model was employed in unseen environments.

Theoretical implications of this research suggest a shift in how autonomous systems could approach uncertain and risky environments. By integrating notions of risk with probabilistic modeling, the paper provides a framework that could be extended to similar domains where environmental conditions are unpredictable and varied.

Practically, this paper's approach could lead applications in diverse autonomous systems, including those designed for exploration in remote or extreme environments such as planetary rovers or search and rescue robots. The ability to account for environmental uncertainty in a nuanced, probabilistic manner may lead to more robust and reliable autonomous systems capable of adapting to unforeseen conditions.

Looking forward, future research could delve into integrating these frameworks with more sophisticated perception systems or investigating other forms of uncertainty quantification that might produce less conservative behaviors without compromising navigation success. Additionally, transitioning these methods from simulated environments to real-world implementations would be paramount in validating the robustness of these models.

In conclusion, the paper presents an important advancement in the field of autonomous navigation by aligning modern neural network capabilities with probabilistic and risk-aware motion planning, thereby setting a foundation for future exploration into more flexible and resilient autonomous systems.