In unstructured environments like parking lots or construction sites, due to
the large search-space and kinodynamic constraints of the vehicle, it is
challenging to achieve real-time planning. Several state-of-the-art planners
utilize heuristic search-based algorithms. However, they heavily rely on the
quality of the single heuristic function, used to guide the search. Therefore,
they are not capable to achieve reasonable computational performance, resulting
in unnecessary delays in the response of the vehicle. In this work, we are
adopting a Multi-Heuristic Search approach, that enables the use of multiple
heuristic functions and their individual advantages to capture different
complexities of a given search space. Based on our knowledge, this approach was
not used previously for this problem. For this purpose, multiple admissible and
non-admissible heuristic functions are defined, the original Multi-Heuristic A*
Search was extended for bidirectional use and dealing with hybrid
continuous-discrete search space, and a mechanism for adapting scale of motion
primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A*
algorithm is benchmarked against a very popular heuristic search-based
algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in
both terms, computation efficiency and motion plan (path) quality.

采用多启发式搜索方法，在相比常规算法更复杂的搜索空间中获得更高效和更高质量的实时路径规划。

基于多启发式搜索的自动停车运动规划

A Multi-Heuristic Search-based Motion Planning for Automated Parking

Trajectory optimizers for model-based reinforcement learning, such as the
Cross-Entropy Method (CEM), can yield compelling results even in
high-dimensional control tasks and sparse-reward environments. However, their
sampling inefficiency prevents them from being used for real-time planning and
control. We propose an improved version of the CEM algorithm for fast planning,
with novel additions including temporally-correlated actions and memory,
requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x
in high-dimensional control problems.

本文提出了改进的 CEM 算法，包括时间相关动作和内存等创新性的增加，可以在高维控制问题中使用，减少了采样次数并提高了 1.2-10 倍的性能表现。

实时规划的高样本利用率交叉熵方法

Sample-efficient Cross-Entropy Method for Real-time Planning

Robotic motion planning problems are typically solved by constructing a
search tree of valid maneuvers from a start to a goal configuration. Limited
onboard computation and real-time planning constraints impose a limit on how
large this search tree can grow. Heuristics play a crucial role in such
situations by guiding the search towards potentially good directions and
consequently minimizing search effort. Moreover, it must infer such directions
in an efficient manner using only the information uncovered by the search up
until that time. However, state of the art methods do not address the problem
of computing a heuristic that explicitly minimizes search effort. In this
paper, we do so by training a heuristic policy that maps the partial
information from the search to decide which node of the search tree to expand.
Unfortunately, naively training such policies leads to slow convergence and
poor local minima. We present SaIL, an efficient algorithm that trains
heuristic policies by imitating "clairvoyant oracles" - oracles that have full
information about the world and demonstrate decisions that minimize search
effort. We leverage the fact that such oracles can be efficiently computed
using dynamic programming and derive performance guarantees for the learnt
heuristic. We validate the approach on a spectrum of environments which show
that SaIL consistently outperforms state of the art algorithms. Our approach
paves the way forward for learning heuristics that demonstrate an anytime
nature - finding feasible solutions quickly and incrementally refining it over
time.

使用 SaIL 特有的算法，训练启发式策略来遍历搜索树中的节点，以减少搜索次数。实验证明 SaIL 算法在实时规划环境中优于现有算法，这为学习体现 ' 快速找到可行解并随时间增量调整 ' 的启发式方法铺平了道路。

通过模仿学习启发式搜索

Learning Heuristic Search via Imitation

Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored
environments require fast and flexible local planning, which can replan when
new parts of the map are explored. Trajectory optimization methods fulfill
these needs, but require obstacle distance information, which can be given by
Euclidean Signed Distance Fields (ESDFs).
We propose a method to incrementally build ESDFs from Truncated Signed
Distance Fields (TSDFs), a common implicit surface representation used in
computer graphics and vision. TSDFs are fast to build and smooth out sensor
noise over many observations, and are designed to produce surface meshes.
Meshes allow human operators to get a better assessment of the robot's
environment, and set high-level mission goals.
We show that we can build TSDFs faster than Octomaps, and that it is more
accurate to build ESDFs out of TSDFs than occupancy maps. Our complete system,
called voxblox, will be available as open source and runs in real-time on a
single CPU core. We validate our approach on-board an MAV, by using our system
with a trajectory optimization local planner, entirely on-board and in
real-time.

本文提出了一种从 TSDFs 中增量式构建 ESDFs 的方法，这是一种用于计算机图形学和视觉的常见隐式表面表示方法，该方法可以快速、准确且实时地进行路径规划和实时计划，特别适用于在未探索的环境中操作的微型飞行器。