# Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network

arXiv: https://arxiv.org/abs/2412.09877 · HTML: https://mlsanity.com/paper/2412.09877/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [A Unified Objective for Novel Class Discovery](https://mlsanity.com/paper/2108.08536/) (2021)
- [SwitchTab: Switched Autoencoders Are Effective Tabular Learners](https://mlsanity.com/paper/2401.02013/) (2024)
- [ReConTab: Regularized Contrastive Representation Learning for Tabular Data](https://mlsanity.com/paper/2310.18541/) (2023)
- [Residual-based Language Models are Free Boosters for Biomedical Imaging](https://mlsanity.com/paper/2403.17343/) (2024)
- [Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task](https://mlsanity.com/paper/2405.03026/) (2024)
- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies](https://mlsanity.com/paper/2403.10720/) (2024)
- [Utilizing Large Language Models for Information Extraction from Real Estate Transactions](https://mlsanity.com/paper/2404.18043/) (2024)
- [Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC](https://mlsanity.com/paper/2407.12887/) (2024)
- [Decentralized Adaptive Aerospace Transportation of Unknown Loads Using A Team of Robots](https://mlsanity.com/paper/2407.08084/) (2024)
- [TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data](https://mlsanity.com/paper/2411.11148/) (2024)

## Built on by (most cited first, 3 of 3)

- [NeRF-Based defect detection](https://mlsanity.com/paper/2504.00270/) (2025)
- [AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation](https://mlsanity.com/paper/2504.05728/) (2025)
- [GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2504.00946/) (2025)

## Abstract (arXiv)

In this paper, we present an advanced strategy for the coordinated control of a multi-agent aerospace system, utilizing Deep Neural Networks (DNNs) within a reinforcement learning framework. Our approach centers on optimizing autonomous task assignment to enhance the system's operational efficiency in object relocation tasks, framed as an aerospace-oriented pick-and-place scenario. By modeling this coordination challenge within a MuJoCo environment, we employ a deep reinforcement learning algorithm to train a DNN-based policy to maximize task completion rates across the multi-agent system. The objective function is explicitly designed to maximize effective object transfer rates, leveraging neural network capabilities to handle complex state and action spaces in high-dimensional aerospace environments. Through extensive simulation, we benchmark the proposed method against a heuristic combinatorial approach rooted in game-theoretic principles, demonstrating a marked performance improvement, with the trained policy achieving up to 16% higher task efficiency. Experimental validation is conducted on a multi-agent hardware setup to substantiate the efficacy of our approach in a real-world aerospace scenario.