SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System
Not on the map. Why?
Abstract (arXiv)
With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose textitSynchroSim, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm-specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability (≈11%), and average packet delay (≈10%) compared to the fixed window size-based synchronization approach.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Aug 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learningcited by 7
- 2023A Systematic Study on Object Recognition Using Millimeter-wave Radarcited by 1
- 2024EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learningcited by 1
- 2026Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learningcited by 0
- 2026Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning1 Oct 2026
- 2024EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning29 Mar 2024
- 2023CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning12 Aug 2023
- 2023A Systematic Study on Object Recognition Using Millimeter-wave Radar3 May 2023