Real-Time Navigation in Resource-Constrained Settings

Updated 13 October 2025

Real-Time Navigation in Resource-Constrained Settings is defined by the integration of efficient sensing, compression, and adaptive control techniques to ensure timely and safe trajectories.
It leverages threshold-based Age of Information policies, model compression, and lightweight planning to meet the challenges of limited compute, memory, and bandwidth.
Adaptive resource allocation, robust perception-action loops, and utility-driven communication enable effective navigation on low-power, embedded platforms.

Real-time navigation in resource-constrained settings concerns the design, analysis, and implementation of computational, communication, and algorithmic strategies that enable intelligent agents—robots, embedded systems, or distributed teams—to generate timely, safe, and efficient trajectories despite stringent limits on compute, memory, bandwidth, energy, and sensing. Such settings encompass low-power IoT devices, mobile robots with limited onboard processing, drones, underwater gliders, and dense multi-agent fleets, often operating in uncertain and dynamic environments. Core advances span from optimally scheduling information flows and compressing model representations, through adaptive task/resource allocation, to real-time, robust perception-action control architectures.

1. Age of Information (AoI) and Optimal Information Transmission

The introduction of Age of Information (AoI) as a primary metric underlines the importance of data freshness in real-time navigation and monitoring systems, particularly in industrial IoT networks or robotics with bandwidth or power constraints (Wang et al., 2019). AoI $\delta(t) = t - u(t)$ quantifies the staleness of the most recent successful update. Minimizing the long-term average AoI under a transmission constraint leads to a constrained Markov Decision Process (CMDP). The CMDP's state encompasses the current AoI, the count of retransmissions, and the presence of newly generated data; actions are transmit, retransmit, or remain idle.

A key result is that the optimal policy exhibits a threshold structure: transmit actions become more likely as AoI rises or retransmission attempts drop. When resource budgets are tight, randomized policies mix two deterministic threshold strategies bracketing the constraint, precisely meeting the allowed average transmission cost. Simulations confirm that these structured policies substantially reduce average AoI, especially as constraints tighten.

Implications extend directly to real-time navigation: in robot or vehicular fleets, applying such thresholded policies for data, control, or map update transmission ensures up-to-date situational awareness within power or bandwidth budgets, by efficiently prioritizing fresh or critical updates and enabling implementation on minimal-memory, low-power devices.

2. Lightweight Sensing, Localization, and Model Compression

Real-time localization methods operational on smartphones and embedded platforms demonstrate the importance of efficient sensing and representation in constrained environments (Musa et al., 2020). Video-based localization systems can achieve sub-meter accuracy by deploying a highly compressed offline 3D environmental model and leveraging interleaved feature matching and optical flow tracking. Notably, the model size is reduced by discarding unreliable points and averaging descriptors (91 MB to 7.5 MB) while maintaining accuracy. Online, computational load is controlled via frame segmentation and sampling (for feature extraction), with heavy descriptor matching interleaved with lightweight optical flow updates, and pose smoothed by a physics-based Kalman filter.

This paradigm enables robust navigation in GPS-denied, cluttered, or dynamic settings by ensuring high-frequency state updates at low computational cost, and resilience to environmental changes via rapid relocalization and robust estimation techniques.

3. Resource-Aware Perception and Planning Architectures

Robotic platforms designed without GPUs or high-end sensors stress the need for algorithmic and systems-level efficiency (Kim et al., 2021). A representative architecture processes RGB-D data into a 3D SLAM map and a 2D occupancy grid and applies a modified A* search with an obstacle-proximity cost to generate paths that are smooth, collision-averse, and computationally tractable. Local traversability maps are generated from raw depth images using fast ground segmentation, reducing the data passed to planning and policy modules.

Navigation control exploits either rule-based or imitation-learning policies, converting the compact traversability signal into safe commands. The entire stack operates at 18 Hz, with low per-module latency, and outperforms standard baselines in collision avoidance and decision speed. The key insight is that compressing the environment representation—both spatially (ground/traversability abstraction) and temporally (low-latency feedback)—enables complex navigation tasks with minimal hardware.

4. Adaptive Resource Allocation, Scheduling, and Task Handling

In multi-task robotic systems, maintaining real-time operation under heavy processing load requires context-aware scheduling and virtualization (Hadidi et al., 2021). Reactive, event-driven controllers dynamically adjust OS-level CPU share to mission-critical modules (e.g., localization, vision, speech). As context or sensory inputs change, scheduling weights are recomputed on relevant events, avoiding overhead from continuous polling. Containers encapsulate individual modules for lightweight, modular execution.

Empirically, real robots utilizing this method achieved a 42% reduction in total task execution time compared to the standard Linux scheduler, demonstrating improved joint task accuracy through dynamic resource focus. The general approach—modular virtualization, reactive context-aware allocation, and rapid (re)configuration—substantiates broad advances in energy-efficient, multitask real-time navigation.

To deploy state-of-the-art visual–inertial odometry (VIO) on light platforms or under shared compute load, parameter adaptation at both the frontend (frame/feature selection) and backend (solver iteration, window size) is required (Mathur et al., 2021). The proposed methodology monitors CPU usage and system motion dynamics in real time, adjusting how much data is processed and the depth of optimization steps. The system exploits an "agility" metric computed over sliding time windows of IMU acceleration/rotation; if computational stress or dynamic motion is high, frames and features are selectively dropped, and solver iterations are curtailed.

Experiments confirm that such resource-aware adaptation maintains odometry accuracy comparable to unconstrained operation, while notably reducing CPU usage and ensuring real-time task concurrency. This substantiates a scalable approach for deploying advanced navigation on minimalist hardware or in complex, multi-process settings.

Coordinating multi-robot systems in uncertain environments under tight bandwidth constraints requires intelligent communication policies. Modeling information sharing as utility-driven belief propagation, the optimal sharing schedule can be formalized as a 0/1 knapsack problem (Chari et al., 16 Sep 2024). Each pairwise observation transmission is assigned a decision-making utility (linked, e.g., to its effect on the Kullback–Leibler divergence of target belief) and a bandwidth cost. A global optimization maximizes the sum of utilities without exceeding total bandwidth.

The intelligent knapsack (iKnap) approach outperforms state-of-the-art broadcast-based schemes in simulation, improving navigation performance (e.g. by 20%–35% in makespan reduction) while matching computational cost. This signals a decisive advantage for selective, utility-driven communication in real-world multi-robot navigation, especially in spectrum-constrained or high-uncertainty environments.

Navigating robots through unmapped or dynamic environments with actuator limitations requires controllers that guarantee constraint satisfaction in real time. Composite smooth control barrier functions (CBFs), as constructed by soft-maximum fusion of recent perception-derived barrier functions, yield a time-varying safety margin that adapts as new obstacles are detected (Safari et al., 3 Oct 2024). Input constraints are incorporated by extending the control space and using soft-minimum functions to blend with state constraints, yielding a relaxed CBF amenable to quadratic programming.

For a class of ground robots, this framework admits a closed-form control solution enforceable at high rate, avoiding the computational overhead of generic QP solvers. Simulation validates strict satisfaction of state/input constraints and online adaptation to LiDAR-sensed obstacles, demonstrating suitability for embedded, real-time safety-critical navigation.

8. Task-Specific Lightweight Modeling and Distillation

Application-specific perception for applications such as aerial fire detection on UAVs benefits from lightweight neural architectures such as MobileViT-S, compressed via knowledge distillation from larger teacher models (Jangirova et al., 28 Feb 2025). The resulting detector achieves state-of-the-art classification accuracy at speeds as high as 431 FPS on GPU and 9.36 FPS on a Raspberry Pi 4, with a model size of only 19.73 MB. The dual strategy of architectural efficiency (combining CNN and vision transformer elements) and distillation maintains high discriminative power and enables real-time deployment on low-power platforms, suitable for dynamic navigation tasks in resource-limited scenarios.

9. Specialized Planning for Advanced Platforms and Environments

Recent frameworks address specialized navigation scenarios, such as aerial manipulation in unknown environments (Zhang et al., 11 Apr 2025) and magnetic soft continuum robots in medical settings (Tong et al., 11 Mar 2025). In these cases, strategies rely on decomposing motion planning into efficient leader–follower B-spline-based guidance (aerial manipulators) or embedding reduced-order, discretized models (soft robot navigation) in control optimization. Both approaches guarantee workspace constraints, enable rapid re-planning or actuation adjustment, and are validated in simulation and experiment to meet stringent timing and resource restrictions.

10. Real-Time Spatial Reasoning and On-the-Fly 3D Mapping

For mobile robots operating in complex, dynamic 3D environments, single-frame mesh reconstruction from sparsely sampled LiDAR (via radial transformation and convex hull meshing), together with real-time per-voxel free-space updates based on line-of-sight reasoning, produces continuous, accurate maps suited to navigation (Huang et al., 18 May 2025). Fusion across multiple frames with robust outlier (dynamic object) removal, achieved by temporal consistency checks in the free-space field, allows safe navigation and reconstruction at 10 Hz, outperforming traditional SLAM or map fusion methods in handling moving obstacles and sparse data.

Summary Table: Approaches and Resource-Constraint Strategies

Domain	Core Methodology	Resource Constraint Addressed
IIoT status update (Wang et al., 2019)	CMDP w/ threshold, AoI minimization	Avg. transmission power, channel failures
Mobile video localization (Musa et al., 2020)	Model compression, interleaved matching	Compute/memory for SIFT, real-time updating
Low-cost robot nav (Kim et al., 2021)	Fast planar segmentation, modified A*	No LiDAR or GPU, low-end SBC
VIO adaptation (Mathur et al., 2021)	Online parametric adaptation	Varying CPU/memory during operation
Multi-robot comm (Chari et al., 16 Sep 2024)	Utility-driven knapsack optimization	Bandwidth for distributed fleet
Embedded task sched (Hadidi et al., 2021)	Event-driven scheduling, containerization	CPU/time for concurrent tasks
Safe nav, CBF (Safari et al., 3 Oct 2024)	Composite/relaxed CBF, closed-form QP	Compute-limited real-time actuation
Aerial fire detection (Jangirova et al., 28 Feb 2025)	Light MobileViT-S + distillation	Inference speed, model size
Aerial manipulator nav (Zhang et al., 11 Apr 2025)	B-spline leader-follower planning	Real-time unknown/dynamic env., low compute
LiDAR map & nav (Huang et al., 18 May 2025)	Real-time 3D mesh, LoS fusion	Framewise processing, dynamic scenes

Each approach methodologically exploits problem structure, spatial/temporal/statistical abstraction, and efficient allocation or fusion to address the often conflicting requirements of real-time responsiveness, safety, and operational robustness within tightly bounded resource envelopes. This confluence underlies the rapidly evolving capability of intelligent navigational systems to function reliably in practical, demanding settings.