Goal-Oriented Semantic Communication for ISAC-Enabled Robotic Obstacle Avoidance

2026-03-02

AI Summary

This work addresses the inefficiency of conventional ISAC systems in UAV obstacle avoidance, where frequent transmission of sensing and control signals leads to excessive communication resource consumption. To this end, the authors propose a goal-oriented semantic communication framework that enables joint optimization across perception, control, and communication in a closed loop. By integrating Kalman filtering to fuse predictions with sensory data, the system enhances position estimation accuracy. The study introduces Mahalanobis distance into the dynamic window approach (MD-DWA) for the first time and derives an analytical expression for safe distance to generate robust control commands. Furthermore, an effectiveness-aware deep Q-network (E-DQN) is designed, leveraging value-of-information (VoI) quantification and uncertainty-based entropy metrics to intelligently schedule signal transmissions. Experimental results demonstrate a 92.4% reduction in sensing and control signal volume and an 85.5% decrease in transmission time slots, while maintaining a 100% obstacle avoidance success rate.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose a goal-oriented semantic communication (GOSC) framework for the BS to transmit sensing and command and control (C&C) signals efficiently and effectively. Our GOSC framework establishes a closed loop for sensing-C&C generation-sensing and C&C transmission: For sensing, a Kalman filter (KF) is applied to continuously predict UAV positions, mitigating the reliance of UAV position acquisition on continuous sensing signal transmission, and enhancing position estimation accuracy through sensing-prediction fusion. Based on the refined estimation position provided by the KF, we develop a Mahalanobis distance-based dynamic window approach (MD-DWA) to generate precise C&C signals under uncertainty, in which we derive the mathematical expression of the minimum Mahalanobis distance required to guarantee collision avoidance. Finally, for efficient sensing and C&C signal transmission, we propose an effectiveness-aware deep Q-network (E-DQN) to determine the transmission of sensing and C&C signals based on their value of information (VoI). The VoI of sensing signals is quantified by the reduction in uncertainty entropy of UAV's position estimation, while the VoI of C&C signals is measured by their contribution to UAV navigation improvement. Extensive simulations validate the effectiveness of our proposed GOSC framework. Compared to the conventional ISAC transmission framework that transmits sensing and C&C signals at every time slot, GOSC achieves the same 100% task success rate while reducing the number of transmitted sensing and C&C signals by 92.4% and the number of transmission time slots by 85.5%.

Problem

• semantic communication
• obstacle avoidance
• integrated sensing and communication
• UAV
• goal-oriented

Innovation

• Goal-Oriented Semantic Communication
• Integrated Sensing and Communication (ISAC)
• Effectiveness-aware Deep Q-Network
• Mahalanobis Distance-based DWA
• Value of Information (VoI)