NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Abstract--Balancing the trade-off between safety and efficiency paths in uncertain environments, especially under nonconvex and is of significant importance for path planning under uncertainty. The initial paths are often of poor Many risk-aware path planners have been developed to explicitly quality as well. Therefore, we propose the NR-RRT algorithm limit the probability of collision to an acceptable bound in to rapidly find near-optimal solutions with guaranteed bounded uncertain environments. It utilizes an informed bidirectional search strategy after uncertainties are usually assumed to make the problem tractable having past experiences in those challenging environments. These assumptions limit the generalization RRT can be applied in not only seen but also unseen uncertain and application of path planners in real-world implementations. However, the algorithm cannot handle entirely unseen In this article, we propose to apply deep learning methods to environments that contain new or additional obstacles. In future the sampling-based planner, developing a novel risk bounded research, we will address the problem of planning under robot near-optimal path planning algorithm named neural risk-aware model uncertainty. Specifically, a deterministic risk contours map is Index Terms--Planning under uncertainty, Sampling-based maintained by perceiving the probabilistic nonconvex obstacles, path planning, Learning from demonstration. and a neural network sampler is proposed to predict the next most-promising safe state.