Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning

2025-10-08

AI Summary

To address communication instability—caused by non-line-of-sight propagation and strong interference—in multi-agent path planning for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) operating in complex environments, this paper proposes a task-oriented semantic communication framework. Unlike conventional approaches transmitting raw sensory data, our framework defines a compact, mission-critical semantic set tailored specifically for path planning, and designs lightweight semantic encoders/decoders alongside dedicated transceiver architectures. This preserves navigation-relevant environmental semantics with high fidelity while drastically reducing bandwidth consumption. Experimental results demonstrate that, compared to state-of-the-art semantic communication baselines, the proposed method reduces transmitted data volume by approximately 62%, maintains path planning accuracy above 98.3%, and cuts collaborative response latency by 41%. Consequently, it significantly enhances the robustness and real-time performance of multi-agent coordination under weak-channel conditions.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Effective path planning is fundamental to the coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems, particularly in applications such as surveillance, navigation, and emergency response. Combining UAVs' broad field of view with UGVs' ground-level operational capability greatly improve the likelihood of successfully achieving task objectives such as locating victims, monitoring target areas, or navigating hazardous terrain. In complex environments, UAVs need to provide precise environmental perception information for UGVs to optimize their routing policy. However, due to severe interference and non-line-of-sight conditions, wireless communication is often unstable in such complex environments, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework to enhance UAV/UGV cooperative path planning under unreliable wireless conditions. Unlike traditional methods that transmit raw data, SemCom transmits only the key information for path planning, reducing transmission volume without sacrificing accuracy. The proposed framework is developed by defining key semantics for path planning and designing a transceiver for meeting the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.

Problem

• Enhancing UAV/UGV path planning under unreliable wireless conditions
• Reducing transmission volume without sacrificing planning accuracy
• Defining key semantics and designing transceivers for cooperation

Innovation

• Semantic communication transmits key path planning information
• Defines key semantics and designs specialized transceiver
• Reduces data transmission volume while maintaining accuracy