In the field of resource-constrained robots and the need for effective place
recognition in multi-robotic systems, this article introduces RecNet, a novel
approach that concurrently addresses both challenges. The core of RecNet's
methodology involves a transformative process: it projects 3D point clouds into
depth images, compresses them using an encoder-decoder framework, and
subsequently reconstructs the range image, seamlessly restoring the original
point cloud. Additionally, RecNet utilizes the latent vector extracted from
this process for efficient place recognition tasks. This unique approach not
only achieves comparable place recognition results but also maintains a compact
representation, suitable for seamless sharing among robots to reconstruct their
collective maps. The evaluation of RecNet encompasses an array of metrics,
including place recognition performance, structural similarity of the
reconstructed point clouds, and the bandwidth transmission advantages, derived
from sharing only the latent vectors. This reconstructed map paves a
groundbreaking way for exploring its usability in navigation, localization,
map-merging, and other relevant missions. Our proposed approach is rigorously
assessed using both a publicly available dataset and field experiments,
confirming its efficacy and potential for real-world applications.

该研究介绍了一种名为 RecNet 的新方法，通过将三维点云投影到深度图像并使用编码器 - 解码器框架进行压缩，从而同时解决资源受限机器人和多机器人系统中有效的位置识别问题。通过提取该过程中的潜在向量用于高效的位置识别任务，RecNet 不仅取得了可比较的位置识别结果，而且保持了紧凑的表示形式，适合在机器人之间实现无缝共享以重建其集体地图。通过对 RecNet 进行包括位置识别性能、重建点云的结构相似性和仅共享潜在向量而得到的带宽传输优势在内的一系列指标进行评估，该方法为在导航、定位、地图合并和其他相关任务中的应用开辟了新的途径。通过公开数据集和实地实验对所提出的方法进行了严格评估，证实其在真实世界应用中的有效性和潜力。

RecNet: 基于范围图像嵌入的可逆点云编码用于多机器人地图共享与重建

RecNet: An Invertible Point Cloud Encoding through Range Image  Embeddings for Multi-Robot Map Sharing and Reconstruction

Resource-constrained robots often suffer from energy inefficiencies,
underutilized computational abilities due to inadequate task allocation, and a
lack of robustness in dynamic environments, all of which strongly affect their
performance. This paper introduces DREAM - Decentralized Reinforcement Learning
for Exploration and Efficient Energy Management in Multi-Robot Systems, a
comprehensive framework that optimizes the allocation of resources for
efficient exploration. It advances beyond conventional heuristic-based task
planning as observed conventionally. The framework incorporates Operational
Range Estimation using Reinforcement Learning to perform exploration and
obstacle avoidance in unfamiliar terrains. DREAM further introduces an Energy
Consumption Model for goal allocation, thereby ensuring mission completion
under constrained resources using a Graph Neural Network. This approach also
ensures that the entire Multi-Robot System can survive for an extended period
of time for further missions compared to the conventional approach of randomly
allocating goals, which compromises one or more agents. Our approach adapts to
prioritizing agents in real-time, showcasing remarkable resilience against
dynamic environments. This robust solution was evaluated in various simulated
environments, demonstrating adaptability and applicability across diverse
scenarios. We observed a substantial improvement of about 25% over the baseline
method, leading the way for future research in resource-constrained robotics.

资源受限的机器人经常面临能源低效、计算能力利用不足、动态环境中的鲁棒性不足等问题，本论文介绍了一种名为 DREAM（分散式强化学习用于多机器人系统的探索和高效能源管理）的综合框架，通过优化资源分配以实现高效探索，提出了一种操作范围估计的强化学习方法来进行探索和避障，另外引入了一个能源消耗模型来进行目标分配，从而确保在资源有限的情况下完成任务，实验表明，与传统方法相比，在各种模拟环境中，我们的方法改进了约 25%，为资源受限机器人领域未来的研究开辟了新的方向。

DREAM: 多机器人系统中的分布式强化学习用于探索和高效能源管理

DREAM: Decentralized Reinforcement Learning for Exploration and  Efficient Energy Management in Multi-Robot Systems

This paper presents a novel hybrid Automatic Speech Recognition (ASR) system
designed specifically for resource-constrained robots. The proposed approach
combines Hidden Markov Models (HMMs) with deep learning models and leverages
socket programming to distribute processing tasks effectively. In this
architecture, the HMM-based processing takes place within the robot, while a
separate PC handles the deep learning model. This synergy between HMMs and deep
learning enhances speech recognition accuracy significantly. We conducted
experiments across various robotic platforms, demonstrating real-time and
precise speech recognition capabilities. Notably, the system exhibits
adaptability to changing acoustic conditions and compatibility with low-power
hardware, making it highly effective in environments with limited computational
resources. This hybrid ASR paradigm opens up promising possibilities for
seamless human-robot interaction. In conclusion, our research introduces a
pioneering dimension to ASR techniques tailored for robotics. By employing
socket programming to distribute processing tasks across distinct devices and
strategically combining HMMs with deep learning models, our hybrid ASR system
showcases its potential to enable robots to comprehend and respond to spoken
language adeptly, even in environments with restricted computational resources.
This paradigm sets a innovative course for enhancing human-robot interaction
across a wide range of real-world scenarios.

提出了一种新颖的混合自动语音识别（ASR）系统，专为资源受限的机器人设计。该系统将隐马尔可夫模型（HMM）与深度学习模型相结合，并利用套接字编程来有效地分配处理任务。通过在机器人内部进行基于 HMM 的处理，同时由独立的计算机处理深度学习模型，实现了 HMM 和深度学习之间的协同，显著提高了语音识别准确性。通过在各种机器人平台上进行实验，演示了实时精确的语音识别能力。该系统在适应不断变化的声学环境和兼容低功耗硬件方面具有灵活性，使其在计算资源有限的环境中非常有效。这种混合 ASR 范式为无缝人机交互提供了有前景的可能性。总之，我们的研究为针对机器人的 ASR 技术引入了一种开创性的维度。通过利用套接字编程在不同设备上分配处理任务，并巧妙地组合 HMM 和深度学习模型，我们的混合 ASR 系统展示了它在使机器人能够熟练理解和响应口语，甚至在计算资源受限的环境中的潜力。这种范式在各种现实场景下提升人机交互的创新方向。