embedded firmware development

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This work addresses the growing firmware bloat in multimodal robotic vehicles and the limitations of existing debloating techniques, which suffer from coarse granularity and irreversibility. To overcome these challenges, the authors propose a runtime-adaptive, function-level dynamic debloating approach built on LLVM that integrates static and dynamic analysis to selectively prune non-essential code at runtime based on the active operational mode. This method achieves, for the first time, fine-grained, reversible, and mode-aware firmware reduction for multimodal embedded systems. Evaluation across six real-world and simulated robotic platforms demonstrates that, on average, 85% of functions can be safely removed in a given mode, reducing the call graph size by 45% while maintaining 100% task success rates, with only a 3.9% performance overhead and approximately 0.25 MB of memory overhead.

This study addresses the challenges of material instability and inadequate actuation performance faced by soft robots operating in near-space extreme environments characterized by high vacuum, cryogenic temperatures, and large pressure differentials. The work proposes a rapid UV-induced crosslinking mechanism catalyzed by trimethyl(methylcyclopentadienyl)platinum(IV), which enhances the dielectric properties and thermal stability of silicone elastomers through carbon–carbon bond formation, significantly broadening their operational temperature range and environmental adaptability. The resulting dielectric elastomer actuators were integrated into an autonomously controlled soft gripper system and successfully validated during two stratospheric balloon flights, reaching an altitude of 23.6 km (pressure <0.05 atm, temperature −55°C). Experimental results demonstrate superior performance compared to existing acrylic- and conventional silicone-based systems, marking the first successful demonstration of such actuators in space-analog conditions.

Current robotic systems lack low-cost, scalable, and easily integrable tactile sensing solutions, limiting their performance in fine manipulation and environmental interaction. This work proposes a plug-and-play flexible piezoresistive tactile sensing module featuring a sealed tri-layer composite structure (FPC–Velostat–FPC), enabling high-consistency batch fabrication and excellent mechanical compliance. The module integrates a high-density flexible sensor array, a compact multi-channel readout circuit, low-power serial communication, and flexible printed electrodes, supporting real-time tactile signal transmission at 100 Hz across diverse form factors such as fingertip sensors and large-area tactile pads. Designed for cross-platform compatibility, the system leverages GPU-accelerated tactile simulation and vision–tactile fusion, demonstrating successful applications in contact-aware decision-making, cross-embodiment skill transfer, and sim-to-real fine-tuning tasks.

Large-scale pneumatic soft robots lack scalable, high-precision real-time pressure control systems and dynamic modeling tools suitable for real-time control. Method: This paper introduces PneuDrive—a modular embedded pressure control system—and the first real-time-control-oriented tri-model dynamic modeling toolkit. PneuDrive features a novel scalable RS-485 bus architecture enabling multi-node daisy-chaining, closed-loop control of 16 valves (0–100 psig), and reliable communication over distances exceeding 10 meters. The modeling toolkit integrates data-driven, physics-based, and hybrid models, supporting hysteresis compensation, fluid–structure interaction modeling, and quantitative performance benchmarking. Contribution/Results: Evaluated on a three-segment continuum robot, the system achieves coordinated trajectory tracking across 12 actuation channels. All three model types are experimentally calibrated and validated via real-time simulation, establishing both a hardware platform and a modeling paradigm for real-time control of pneumatic soft robots.

This work addresses the lack of reusable, cross-platform compliant control infrastructure in existing robotic software, which hinders unified algorithm implementation and high-level interfacing. The authors propose a robot-agnostic, modular compliance control framework that decouples controller infrastructure from control laws via a plugin architecture. It supports variable impedance control in both joint and Cartesian spaces and, for the first time, enables online adaptation of the primary compliance direction according to task geometry—overcoming the limitations of fixed coordinate frames. Built upon the ROS ecosystem, the framework leverages Pinocchio to parse URDF models for kinematic and dynamic computations and employs runtime plugin loading with generic wrappers to interface heterogeneous hardware. Real-world and simulated experiments demonstrate significant performance improvements in contact-intensive tasks and seamless transferability across multiple robotic arms.

This study systematically evaluates whether Rust can compete with C in performance and resource efficiency for microcontroller firmware development and assesses its industrial viability. Two teams independently implemented identical industrial IoT firmware—one in Rust and the other in C—and key metrics including development effort, memory footprint, and execution speed were compared on real hardware. This work presents the first systematic comparison of the two languages in a genuine industrial context and introduces Ariel OS, a lightweight Rust-based runtime. Empirical results demonstrate that Rust matches or exceeds C in both resource utilization and execution performance, while Ariel OS exhibits a smaller binary footprint, collectively establishing Rust as a reliable and competitive choice for microcontroller firmware development.

This study addresses the challenges of conventional independently actuated joints in lower-limb exoskeletons—namely, their mechanical complexity, excessive weight, and reliance on torque sensors—by proposing a cable-driven differential architecture tailored for hip–knee flexion–extension movements. The design employs two motors coupled with a linear differential mapping to enable coordinated torque distribution across joints. Combined with a model-based friction compensation strategy, this approach achieves, for the first time in a differential actuation module, high-precision joint torque estimation without the need for physical torque sensors. Experimental validation demonstrates that the proposed method substantially reduces system complexity and mass, offering an effective sensorless torque control solution for lightweight exoskeletons.

This work addresses the challenge of unstable execution in real-world IoT firmware rehosting, where cross-layer dependencies—such as boot scripts, persistent configurations, and network settings—often prevent the system from reaching an externally observable state. To overcome this, the authors propose an evidence-guided multi-agent collaborative framework that reframes rehosting as an iterative environment reconstruction process. By integrating similarity-based retrieval with execution feedback, the framework dynamically restores the file system, initialization scripts, persistent states, and network configurations while avoiding arbitrary modifications by large language models, thereby resolving dependency conflicts. Experimental results on the LFwC dataset demonstrate significant improvements: web service reachability increases from 25.49% to 52.39%, network reachability rises from 39.30% to 71.93%, and the average number of detectable services per firmware grows from 0.86 to 1.62, enabling effective RouterSploit interaction and protocol-aware fuzz testing.

This work addresses the lack of general-purpose support for cross-domain, scalable data-driven applications in existing cyber-physical system (CPS) frameworks. We propose and open-source SDNator—the first domain-agnostic, scalable, data-driven control framework tailored for CPS—featuring an application- and data-centric design that enables applications to act as both producers and consumers in collaboratively defining controller workflows. SDNator integrates dual data storage backends to uniformly support event-driven and data-driven programming paradigms and adopts an SDN-like centralized architecture to enable flexible workflow orchestration and dynamic scheduling. Experimental results demonstrate that SDNator achieves scalability on par with the mainstream SDN controller Ryu, significantly reduces production cycle times and enhances robustness to anomalies in additive manufacturing and networking scenarios, and efficiently responds to urgent task demands.