TDMA Based Communications Control Co-Design for Cooperative Carrying: Delay Calibration and Sampling-Rate Optimization
- URL: http://arxiv.org/abs/2608.09556v1
- Date: Mon, 10 Aug 2026 12:53:43 GMT
- Title: TDMA Based Communications Control Co-Design for Cooperative Carrying: Delay Calibration and Sampling-Rate Optimization
- Abstract summary: This paper investigates how adaptive sampling time adjustment informed by measured network delay and strategic leader rotation can distribute wireless load fairly across the team.<n>We use physics based simulation in MuJoCo with realistic wireless modeling, including time division multiple access, medium access control, jitter, queueing, and packet loss.<n>Our results reveal an important trade off: dynamic sampling effectively reduces communications overhead without compromising control performance, while rotating the leader role meaningfully improves how fairly airtime is distributed.
- Score: 1.031361622440068
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi robot teams performing cooperative transportation face a fundamental challenge: maintaining stable control while keeping communications efficient. This paper investigates how adaptive sampling time adjustment informed by measured network delay and strategic leader rotation can distribute wireless load fairly across the team. We use physics based simulation in MuJoCo with realistic wireless modeling, including time division multiple access, medium access control, jitter, queueing, and packet loss, to evaluate three control approaches: fixed sampling with static leadership, dynamic sampling with static leadership, and dynamic sampling with rotating leadership. Our results reveal an important trade off: dynamic sampling effectively reduces communications overhead without compromising control performance, while rotating the leader role meaningfully improves how fairly airtime is distributed all with negligible impact on the team carrying ability. to the best of our knowledge, being among the first to jointly examine dynamic sampling, rotating leadership, and wireless protocol interactions in physicsrealistic multi robot cooperation, this work provides practical guidance for deploying coordinated robotic teams in real world settings where communications resources are limited.
Related papers
- Coupled Control and Wireless World Models for Resilient Remote Robotic Control [26.76550154183091]
This paper proposes a communication-aware remote robotic control framework based on coupled control and wireless Joint Embedding Predictive Architecture (JEPA)<n>The learned latent representations enable predictive communication scheduling by jointly forecasting future robot states and wireless conditions.<n>An adaptive resilience mechanism detects latent prediction discrepancies and efficiently adapts perception embeddings to accommodate wireless and visual environmental changes.
arXiv Detail & Related papers (2026-09-04T08:09:55Z) - A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
arXiv Detail & Related papers (2026-08-11T01:51:08Z) - Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation [45.11184664928145]
Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations.<n>This paper develops a system framework that integrates sampling, transmission, the reconstruction and formulates a communication-rate optimization.
arXiv Detail & Related papers (2026-05-19T03:13:09Z) - Interaction-Aware Whole-Body Control for Compliant Object Transport [33.233203393813376]
This paper proposes an interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum.<n>IO-WBC translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact.
arXiv Detail & Related papers (2026-03-04T05:50:40Z) - Efficient and Compliant Control Framework for Versatile Human-Humanoid Collaborative Transportation [2.4087148947930634]
We present a control framework that enables humanoid robots to perform collaborative transportation tasks with a human partner.<n>The framework supports both translational and rotational motions, which are fundamental to co-transport scenarios.<n>It comprises three components: a high-level planner, a low-level controller, and a stiffness modulation mechanism.
arXiv Detail & Related papers (2025-12-08T18:52:10Z) - STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks [58.13757830013997]
Wireless networks based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications.<n>They face severe signal attenuation in dense environments due to obstructions.<n>To address this critical issue, we consider introducing collaborative beam of UAVs and omni-directional re-altitude beamforming.
arXiv Detail & Related papers (2025-10-25T01:28:37Z) - Deep Learning Optimization of Two-State Pinching Antennas Systems [48.70043547158868]
Pinching antennas (PAs) can dynamically control electromagnetic wave propagation through binary activation states.<n>In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal.
arXiv Detail & Related papers (2025-07-08T17:55:54Z) - From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management [31.080933663717257]
We propose a novel machine learning technique to jointly model and predict the dynamics of the control system.<n>We show that our proposed approach reduces transmit power by over 50% while maintaining control performance comparable to baseline methods.
arXiv Detail & Related papers (2025-06-19T11:08:20Z) - Wireless Human-Machine Collaboration in Industry 5.0 [75.78721184383897]
Wireless Human-Machine Collaboration represents a critical advancement for Industry 5.0.
Stability analysis certifies how the closed-loop system will behave under model randomness.
This paper establishes a fundamental WHMC model incorporating dual wireless loops for machine and human control.
arXiv Detail & Related papers (2024-10-18T03:44:10Z) - Communication-Control Codesign for Large-Scale Wireless Networked Control Systems [80.30532872347668]
Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots.
We propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels.
We develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs.
arXiv Detail & Related papers (2024-10-15T06:28:21Z) - Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust
Closed-Loop Control [63.310780486820796]
We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings.
We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
arXiv Detail & Related papers (2023-10-05T21:44:18Z) - Multi-Agent Reinforcement Learning for Pragmatic Communication and
Control [40.11766545693947]
We propose a joint design that combines goal-oriented communication and networked control into a single optimization model.
Joint training of the communication and control systems can significantly improve the overall performance.
arXiv Detail & Related papers (2023-02-28T08:30:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.