We propose a generic multi-robot planning mechanism that combines an optimal
task planner and an optimal path planner to provide a scalable solution for
complex multi-robot planning problems. The Integrated planner, through the
interaction of the task planner and the path planner, produces optimal
collision-free trajectories for the robots. We illustrate our general algorithm
on an object pick-and-drop planning problem in a warehouse scenario where a
group of robots is entrusted with moving objects from one location to another
in the workspace. We solve the task planning problem by reducing it into an
SMT-solving problem and employing the highly advanced SMT solver Z3 to solve
it. To generate collision-free movement of the robots, we extend the
state-of-the-art algorithm Conflict Based Search with Precedence Constraints
with several domain-specific constraints. We evaluate our integrated task and
path planner extensively on various instances of the object pick-and-drop
planning problem and compare its performance with a state-of-the-art
multi-robot classical planner. Experimental results demonstrate that our
planning mechanism can deal with complex planning problems and outperforms a
state-of-the-art classical planner both in terms of computation time and the
quality of the generated plan.

我们提出了一个通用的多机器人规划机制，它将最优任务规划器和最优路径规划器相结合，以提供可扩展的解决方案来解决复杂的多机器人规划问题。

多机器人接货和交货的最优综合任务和路径规划及其应用

Optimal Integrated Task and Path Planning and Its Application to  Multi-Robot Pickup and Delivery

This paper presents a fully decentralized approach for realtime
non-cooperative multi-robot navigation in social mini-games, such as navigating
through a narrow doorway or negotiating right of way at a corridor
intersection. Our contribution is a new realtime bi-level optimization
algorithm, in which the top-level optimization consists of computing a fair and
collision-free ordering followed by the bottom-level optimization which plans
optimal trajectories conditioned on the ordering. We show that, given such a
priority order, we can impose simple kinodynamic constraints on each robot that
are sufficient for it to plan collision-free trajectories with minimal
deviation from their preferred velocities, similar to how humans navigate in
these scenarios.
We successfully deploy the proposed algorithm in the real world using F$1/10$
robots, a Clearpath Jackal, and a Boston Dynamics Spot as well as in simulation
using the SocialGym 2.0 multi-agent social navigation simulator, in the doorway
and corridor intersection scenarios. We compare with state-of-the-art social
navigation methods using multi-agent reinforcement learning, collision
avoidance algorithms, and crowd simulation models. We show that $(i)$ classical
navigation performs $44\%$ better than the state-of-the-art learning-based
social navigation algorithms, $(ii)$ without a scheduling protocol, our
approach results in collisions in social mini-games $(iii)$ our approach yields
$2\times$ and $5\times$ fewer velocity changes than CADRL in doorways and
intersections, and finally $(iv)$ bi-level navigation in doorways at a flow
rate of $2.8 - 3.3$ (ms)$^{-1}$ is comparable to flow rate in human navigation
at a flow rate of $4$ (ms)$^{-1}$.

本文提出了一种全面去中心化的方法，用于社交小游戏中的实时非合作多机器人导航，使用新的实时双层优化算法来规划基于优先顺序的最优轨迹，并证明了该算法的有效性。

通过双层优化的去中心化非合作机器人社交导航

Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level  Optimization

Quadrotors are agile. Unlike most other machines, they can traverse extremely
complex environments at high speeds. To date, only expert human pilots have
been able to fully exploit their capabilities. Autonomous operation with
on-board sensing and computation has been limited to low speeds.
State-of-the-art methods generally separate the navigation problem into
subtasks: sensing, mapping, and planning. While this approach has proven
successful at low speeds, the separation it builds upon can be problematic for
high-speed navigation in cluttered environments. Indeed, the subtasks are
executed sequentially, leading to increased processing latency and a
compounding of errors through the pipeline. Here we propose an end-to-end
approach that can autonomously fly quadrotors through complex natural and
man-made environments at high speeds, with purely onboard sensing and
computation. The key principle is to directly map noisy sensory observations to
collision-free trajectories in a receding-horizon fashion. This direct mapping
drastically reduces processing latency and increases robustness to noisy and
incomplete perception. The sensorimotor mapping is performed by a convolutional
network that is trained exclusively in simulation via privileged learning:
imitating an expert with access to privileged information. By simulating
realistic sensor noise, our approach achieves zero-shot transfer from
simulation to challenging real-world environments that were never experienced
during training: dense forests, snow-covered terrain, derailed trains, and
collapsed buildings. Our work demonstrates that end-to-end policies trained in
simulation enable high-speed autonomous flight through challenging
environments, outperforming traditional obstacle avoidance pipelines.

提出了一种全新的端到端方法，利用卷积神经网络将嘈杂的传感观测直接映射到避撞轨迹，从而实现四旋翼的高速自主飞行。在模拟训练中，该方法可以实现从模拟到真实环境的零 - shot 迁移，成功地使四旋翼在森林、雪地、脱轨的火车和倒塌的建筑物等复杂环境中高速自主飞行，并优于传统方法。

野外快速飞行学习

Learning High-Speed Flight in the Wild

We describe a novel approach for computing collision-free \emph{global}
trajectories for $p$ agents with specified initial and final configurations,
based on an improved version of the alternating direction method of multipliers
(ADMM). Compared with existing methods, our approach is naturally
parallelizable and allows for incorporating different cost functionals with
only minor adjustments. We apply our method to classical challenging instances
and observe that its computational requirements scale well with $p$ for several
cost functionals. We also show that a specialization of our algorithm can be
used for {\em local} motion planning by solving the problem of joint
optimization in velocity space.

使用改进版的 ADMM 算法计算多个机器人运动规划问题，可以并行处理不同代价函数，同时也可以用于解决速度空间中的动态规划问题。