Adaptive Sensor Management in Resource-Constrained Unmanned Systems: Conceptual Framework and Simulation Study

Abstract

Resource-constrained unmanned systems operating on limited battery power, computational capacity, and communication bandwidth require intelligent sensor management strategies that dynamically allocate resources to maximize mission performance while respecting operational constraints. This paper presents a comprehensive conceptual framework for adaptive sensor management and provides extensive simulation validation across diverse unmanned system scenarios. We examine the fundamental sensor management problem formulation as a sequential decision-making process where the system must determine which sensors to activate, at what sampling rates, with what processing fidelity, and how to allocate communication bandwidth for transmitting sensor data—all while balancing information gain against resource consumption. The conceptual framework systematically addresses multiple resource dimensions: energy consumption from sensor operation and data processing, computational load from perception algorithms competing for limited onboard processing, memory constraints for buffering high-rate sensor streams, and communication bandwidth for transmitting data to ground stations or collaborating agents. We analyze sensor management objectives spanning information-theoretic metrics (entropy reduction, mutual information maximization), task-specific performance measures (target tracking accuracy, map quality), and resource efficiency indicators (energy per information bit, mission duration extension). The framework incorporates predictive models of sensor utility that estimate information gain from activating specific sensors given current belief states, environmental conditions, and mission context, enabling proactive rather than reactive management. Particular emphasis is placed on handling uncertainty in sensor utility predictions and resource consumption estimates, proposing robust optimization formulations and risk-aware decision criteria. We examine algorithmic approaches to sensor management including rule-based heuristics offering interpretability and computational efficiency, optimization-based methods (integer programming, dynamic programming) providing optimality guarantees for tractable problem instances, and learning-based approaches (reinforcement learning, contextual bandits) that adapt policies from operational experience. The simulation study implements the proposed framework across representative scenarios: UAV surveillance missions balancing camera, LiDAR, and thermal imaging against battery constraints; multi-robot exploration coordinating sensor usage across the team to maximize area coverage; and underwater vehicle inspection adaptively managing sonar resolution and sampling rate based on feature density. Simulation environments incorporate realistic resource models including sensor-specific power consumption profiles, processing algorithm complexity, and battery discharge characteristics. Performance evaluation examines mission success metrics (target detection rates, map completeness), resource utilization efficiency (energy consumption, computational load distribution), and adaptability to dynamic conditions (sensor failures, unexpected targets, environmental changes). Comparative analysis reveals that learning-based adaptive management achieves 25-40% mission duration extension compared to static sensor configurations while maintaining equivalent perception performance. We investigate the impact of prediction horizon length, demonstrating that longer horizons enable better resource allocation but increase computational burden and sensitivity to model errors. The paper examines multi-objective formulations balancing competing goals such as maximizing information gathering while minimizing detection risk in contested environments. Advanced techniques are analyzed including hierarchical sensor management decomposing decisions across temporal scales, distributed management for multi-agent coordination, and meta-learning approaches that rapidly adapt management policies to novel mission types.