Deploying multiple robots for target search and tracking has many practical
applications, yet the challenge of planning over unknown or partially known
targets remains difficult to address. With recent advances in deep learning,
intelligent control techniques such as reinforcement learning have enabled
agents to learn autonomously from environment interactions with little to no
prior knowledge. Such methods can address the exploration-exploitation tradeoff
of planning over unknown targets in a data-driven manner, eliminating the
reliance on heuristics typical of traditional approaches and streamlining the
decision-making pipeline with end-to-end training. In this paper, we propose a
multi-agent reinforcement learning technique with target map building based on
distributed Gaussian process. We leverage the distributed Gaussian process to
encode belief over the target locations and efficiently plan over unknown
targets. We evaluate the performance and transferability of the trained policy
in simulation and demonstrate the method on a swarm of micro unmanned aerial
vehicles with hardware experiments.

基于分布式高斯过程的多智能体强化学习技术能够利用深度学习的最新进展，在未知目标上规划和决策，以解决传统方法所依赖的启发式问题，并在硬件实验中展示了在无人机群组上的应用性能和可迁移性。

分布式多智能体目标搜索和跟踪的高斯过程与强化学习

Distributed multi-agent target search and tracking with Gaussian process  and reinforcement learning

Equipping drones with target search capabilities is desirable for
applications in disaster management scenarios and smart warehouse delivery
systems. Instead of deploying a single drone, an intelligent drone swarm that
can collaborate with one another in maneuvering among obstacles will be more
effective in accomplishing the target search in a shorter amount of time. In
this work, we propose a data-efficient reinforcement learning-based approach,
Adaptive Curriculum Embedded Multi-Stage Learning (ACEMSL), to address the
challenges of carrying out a collaborative target search with a visual drone
swarm, namely the 3D sparse reward space exploration and the collaborative
behavior requirement. Specifically, we develop an adaptive embedded curriculum,
where the task difficulty level can be adaptively adjusted according to the
success rate achieved in training. Meanwhile, with multi-stage learning, ACEMSL
allows data-efficient training and individual-team reward allocation for the
collaborative drone swarm. The effectiveness and generalization capability of
our approach are validated using simulations and actual flight tests.

本文提出了一种新的数据高效强化学习方法（Adaptive Curriculum Embedded Multi-Stage Learning，ACEMSL）来解决协作目标搜索的挑战，该方法可在可视无人机群中实现协作任务，并通过模拟和实际飞行测试验证了其有效性和泛化能力。

使用视觉无人机群进行协作目标搜索：一种自适应嵌入多阶段强化学习方法

Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multi-stage Reinforcement Learning Approach

Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.

本研究提出了一种基于高斯过程模型的无人机自适应信息路径规划算法（OA-IPP），该算法可以在复杂的三维环境中进行目标搜索，该算法与现有的路径规划算法相比表现更好，可以应用于实际的城市搜救情景。