In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).

翻译：本文研究了一种结合非线性模型预测控制（MPC）与基于模型强化学习（RL）的混合方案，用于自主模型车在无预设地图的非结构化越野地形中的导航规划。我们的创新方法受BADGR（一种主要聚焦环境建模的基于LSTM的网络）启发，但通过用Transformer替代LSTM模块显著提升了模型性能。针对系统中的不确定性，我们训练了一个预测模型集成，并估计模型权重与输出之间的互信息，通过引入可变速度实现动态时域规划。为进一步增强方法，我们集成了考虑车辆模型与状态复杂性的非线性MPC控制器。基于模型的RL模块生成转向角并量化固有不确定性，而非线性MPC则建议最优油门设定，在目标达成速度与受速度影响的模型不确定性管理之间取得平衡。在研究中，我们的方法持续在预测未来事件方面取得更高指标值，并无缝集成车辆运动学模型以增强决策能力，从而超越现有基线。代码与评估数据见https://github.com/FARAZLOTFI/offroad_autonomous_navigation/。