Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-and-conquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven owing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the state-of-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints.
翻译:安全和效率之间的权衡平衡对于在不确定情况下进行路径规划十分重要。许多风险意识路径规划人员已经制定,以明确将碰撞概率限制在不确定环境中可接受的约束范围内;然而,人们通常认为,曲线障碍或高斯不确定因素通常会使问题在现有方法中易于处理。这些假设限制了路径规划人员在现实世界实施过程中的普遍化和应用。在本条中,我们提议对取样规划人员采用深层次的学习方法,开发一种具有新颖风险的近最佳路径规划算法,名为神经风险识别RRT(NRR-RRRT ),明确将碰撞概率概率限制在可接受的约束范围内。具体地说,通过观察概率性非曲线障碍或高斯不确定因素来维持确定性风险轮廓图,并提议建立神经网络取样员来预测下一个最有希望的安全状态。此外,我们提议采用反复的鸿沟和征服性规划和双向搜索战略来加速接近最优化的解决方案与有保证约束性约束风险的近最佳解决方案的融合。最坏的理论保证也能够证明,因为有备用安全保证性安全保证的不稳妥性计划,同时利用统一的常规分析环境来展示稳定的分配风险。