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Integrating multimodal data such as RGB and LiDAR from multiple views significantly increases computational and communication demands, which can be challenging for resource-constrained autonomous agents while meeting the time-critical deadlines required for various mission-critical applications. To address this challenge, we propose CoOpTex, a collaborative task execution framework designed for cooperative perception in distributed autonomous systems (DAS). CoOpTex contribution is twofold: (a) CoOpTex fuses multiview RGB images to create a panoramic camera view for 2D object detection and utilizes 360° LiDAR for 3D object detection, improving accuracy with a lightweight Graph Neural Network (GNN) that integrates object coordinates from both perspectives, (b) To optimize task execution and meet the deadline, CoOpTex dynamically offloads computationally intensive image stitching tasks to auxiliary devices when available and adjusts frame capture rates for RGB frames based on device mobility and processing capabilities. We implement CoOpTex in real-time on static and mobile heterogeneous autonomous agents, which helps to significantly reduce deadline violations by 100% while improving frame rates for 2D detection by 2.2 times in stationary and 2 times in mobile conditions, demonstrating its effectiveness in enabling real-time cooperative perception. 

In network-constrained environments, distributed multi-agent systems—such as UGVs and UAVs—must communicate effectively to support computationally demanding scene perception tasks like semantic and instance segmentation. These tasks are challenging because they require high accuracy even when using low-quality images, and the network limitations restrict the amount of data that can be transmitted between agents. To overcome the above challenges, we propose TAVIC-DAS to perform a task and channel-aware variable-rate image compression to enable distributed task execution and minimize communication latency by transmitting compressed images. TAVIC-DAS proposes a novel image compression and decompression framework (distributed across agents) that integrates channel parameters such as RSSI and data rate into a task-specific "semantic segmentation" DNN to generate masks representing the object of interest in the scene (ROI maps) by determining a high pixel density needed to represent objects of interest and low density to represents surrounding pixels within an image. Additionally, to accommodate agents with limited computational resources, TAVIC-DAS incorporates resource-aware model quantization. We evaluated TAVIC-DAS on platforms such as ROSMaster X3 and Jetson Xavier, which communicated using a low-frequency proprietary Doodle radio operating at 915 MHz. The experimental results show that TAVIC-DAS achieves approximately 7.62% higher PSNR and is about 6.39% more resource efficient compared to state-of-the-art techniques. 

Robust communication is vital for multi-agent robotic systems involving heterogeneous agents like Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) operating in dynamic and contested environments. These agents often communicate to collaboratively execute critical tasks for perception awareness and are faced with different communication challenges: (a) The disparity in velocity between these agents results in rapidly changing distances, in turn affecting the physical channel parameters such as Received Signal Strength Indicator (RSSI), data rate (applicable for certain networks) and most importantly "reliable data transfer", (b) As these devices work in outdoor and network-deprived environments, they tend to use proprietary network technologies with low frequencies to communicate long range, which tremendously reduces the available bandwidth. This poses a challenge when sending large amounts of data for time-critical applications. To mitigate the above challenges, we propose DACC-Comm, an adaptive flow control and compression sensing framework to dynamically adjust the receiver window size and selectively sample the image pixels based on various network parameters such as latency, data rate, RSSI, and physiological factors such as the variation in movement speed between devices. DACC-Comm employs state-of-the-art DNN (TABNET) to optimize the payload and reduce the retransmissions in the network, in turn maintaining low latency. The multi-head transformer-based prediction model takes the network parameters and physiological factors as input and outputs (a) an optimal receiver window size for TCP, determining how many bytes can be sent without the sender waiting for an acknowledgment (ACK) from the receiver, (b) a compression ratio to sample a subset of pixels from an image. We propose a novel sampling strategy to select the image pixels, which are then encoded using a feature extractor. To optimize the amount of data sent across the network, the extracted feature is further quantized to INT8 with the help of post-training quantization. We evaluate DACC-Comm on an experimental testbed comprising Jackal and ROSMaster2 UGV devices that communicate image features using a proprietary radio (Doodle) in 915-MHz frequency. We demonstrate that DACC-Comm improves the retransmission rate by ≈17% and reduces the overall latency by ≈12%. The novel compression sensing strategy reduces the overall payload by ≈56%. 

Recent Internet-of-Things (IoT) networks span across a multitude of stationary and robotic devices, namely unmanned ground vehicles, surface vessels, and aerial drones, to carry out mission-critical services such as search and rescue operations, wildfire monitoring, and flood/hurricane impact assessment. Achieving communication synchrony, reliability, and minimal communication jitter among these devices is a key challenge both at the simulation and system levels of implementation due to the underpinning differences between a physics-based robot operating system (ROS) simulator that is time-based and a network-based wireless simulator that is event-based, in addition to the complex dynamics of mobile and heterogeneous IoT devices deployed in a real environment. Nevertheless, synchronization between physics (robotics) and network simulators is one of the most difficult issues to address in simulating a heterogeneous multi-robot system before transitioning it into practice. The existing TCP/IP communication protocol-based synchronizing middleware mostly relied on Robot Operating System 1 (ROS1), which expends a significant portion of communication bandwidth and time due to its master-based architecture. To address these issues, we design a novel synchronizing middleware between robotics and traditional wireless network simulators, relying on the newly released real-time ROS2 architecture with a master-less packet discovery mechanism. Additionally, we propose a ground and aerial agents’ velocity-aware customized QoS policy for Data Distribution Service (DDS) to minimize the packet loss and transmission latency between a diverse set of robotic agents, and we offer the theoretical guarantee of our proposed QoS policy. We performed extensive network performance evaluations both at the simulation and system levels in terms of packet loss probability and average latency with line-of-sight (LOS) and non-line-of-sight (NLOS) and TCP/UDP communication protocols over our proposed ROS2-based synchronization middleware. Moreover, for a comparative study, we presented a detailed ablation study replacing NS-3 with a real-time wireless network simulator, EMANE, and masterless ROS2 with master-based ROS1. Our proposed middleware attests to the promise of building a largescale IoT infrastructure with a diverse set of stationary and robotic devices that achieve low-latency communications (12% and 11% reduction in simulation and reality, respectively) while satisfying the reliability (10% and 15% packet loss reduction in simulation and reality, respectively) and high-fidelity requirements of mission-critical applications. 

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 SynchroSim, 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.