ORB-SLAM3 is a state-of-the-art visual SLAM system, but its computational complexity poses major challenges for real-time deployment on embedded platforms. While prior work has largely focused on accelerating front-end tasks like feature extraction, back-end stages such as bundle adjustment remain less explored due to their algorithmic complexity and memory-intensive nature. Furthermore, most existing solutions accelerate specific modules without integrating them into a complete SLAM framework. In this work, we present SoC-SLAM, a novel FPGA-based hardware/software co-design that accelerates both the front-end and back-end stages of ORB-SLAM3 within a unified framework. Profiling identifies ORB feature extraction and local bundle adjustment as the primary performance bottlenecks, both critical for maintaining real-time system responsiveness. To address these, we develop modular FPGA-based accelerators for ORB extraction and key bundle adjustment solver steps, including Schur elimination, Cholesky decomposition, and back substitution, while retaining the remaining pipeline in software. Since the workload predominantly consists of operations on sparse block matrices, we develop and combine several optimization techniques, including matrix partitioning and pipelined block processing, to efficiently handle sparsity and maximize parallelism. Evaluation on the EuRoC MAV dataset shows $8 \times$ and $7.4 \times$ speedups for ORB extraction and local bundle adjustment, resulting in $2.4 \times$ and $3.3 \times$ improvements in the Tracking and Local Mapping threads, respectively. The system operates at 222 MHz, consumes 4.276 W, and achieves an RMSE of 0.02832 m. Our fully integrated pipeline demonstrates competitive performance and power efficiency compared to prior FPGA, ASIC, and GPU-based solutions. The proposed architecture is scalable and generalizable to other bundle adjustment modules, such as global bundle adjustment, welding bundle adjustment, and essential graph optimization, offering an extensible hardware acceleration design for real-time visual SLAM on resource-constrained platforms.

Domain-specific accelerators on Field-Programmable Gate Arrays (FPGAs) have been identified as one potential solution to continue the performance scaling after Moore’s Law ends. However, the design of such accelerators is cumbersome, leading to limited productivity and reduced adoption rates, especially in heterogeneous FPGA systems. Thus, this survey investigated hardware design approaches suited for heterogeneous system developers. High-Level Synthesis (HLS) has been identified as the most fitting category for this objective. Currently, we see the creation of many new HLS-related tools that are hard to classify according to conventional taxonomies. Therefore, this work establishes an explicit definition for HLS approaches based on intended usage. The definition combines the classification of hardware design abstractions with parallel programming models to identify suitable approaches unambiguously. The resulting HLS-related tools are categorized and presented according to a newly developed taxonomy. This taxonomy unifies the current vast ecosystem of HLS-related frameworks, including conventional HLS tools as Backends, embedded Domain Space Exploration (DSE) approaches and system-level integrating tools.

The computational complexity of Nonlinear Model Predictive Control (NMPC) often hinders their application to cyber-physical systems with fast dynamics, such as mobile robots or Unmanned Aerial Vehicles. This complexity overhead comes from the control algorithm's backbone, an iterative solver that must ensure convergence and often takes the form of a highly structured convex Quadratic Program (QP). Such overhead could be overcome using specialized computer architectures. Field Programmable Gate Arrays are good candidates for making hardware accelerators that comply with the realtime constraints of fast-dynamic cyber-physical systems. Nevertheless, QP-solvers have been demonstrated to be complex to implement as a hardware accelerator. With this in mind, the present paper proposes a novel accelerator architecture that uses Knowledge-based Particle Swarm Optimization (PSO) as a solver while exploring its parallel nature. PSO is a stochastic global optimization algorithm that creates a fast and precise solution for NMPC. The proposed strategy in this papergrants system control stability for short sampling frequencies and long prediction horizons. It can also meet realtime constraints while achieving low hardware consumption. Additionally, it is generalized, so it can potentially be adapted to any application and is compatible with the Robot Operating System (ROS). The architecture is tested with two applications: an inverted pendulum swing-up procedure and a quadrotor drone with control and state constraints. Following, we analyze the accelerator performance and highlight our solution's advantages to other works in the literature. Namely, our architecture solves more complex problems with a greater dimension and longer horizon while using similar resources. The proposed solution also has good computational performance (29ms and 11ms) for both the quadrotor and inverted pendulum, respectively, while achieving the realtime requirements (50ms and 100ms, respectively). Parallelly, ad-hoc embedded architectures are important for a low-end, low-cost, and low-power MPSoC+FPGA device. Our solution uses less than 50% of a low-end, low-power MPSoC device (ZU3EG), while others rely on large, more power-hungry devices (e.g., Kintex7 and XC7Z045).

Robotic systems compute data from multiple sensors to perform several actions (e.g., path planning, object detection). FPGA - based architectures for such systems may consist of several accelerators to process compute-intensive algorithms. Designing and implementing such complex systems tends to be an arduous task. This work proposes a modeling approach to generate architectures for such applications, compliant with existing robotics middlewares (e.g., ROS, ROS2). The challenge is to have a compact, yet expressive description of the system with just enough information to generate all required components and to integrate existing algorithms. This system model must be generalizable, so it is not application-dependent, and it must exploit the benefits of FPGAs over software solutions. Previous work mainly focused on individual accelerators rather than all components involved in a system and their interactions. The proposed approach exploits the advantages of model-driven engineering and model-based code generation to produce all components, i.e., message converters acting as middleware interfaces and wrappers to integrate algorithms. Data type and data flow analysis are performed to derive the necessary information to generate the components and their connections. Solutions to several identified challenges for generating entire systems from such models are evaluated using four different use cases.

There have been partial contributions in the state-of-the-art about FPGAs being part of robotics systems. However, a study of FPGAs as a whole for robotics systems is missing in the literature. This means that defining all the components required for an FPGA-based system for robotics applications as a whole, their integration into existing solutions, and the generation of said components has not been done. The traditional robotics workflow involves many disciplines (e.g., mechatronics, control, software) where experts deal with the integration of all individual parts. We propose a model-based component-oriented workflow, focusing on easing the integration of all the parts to deploy FPGA-based robotics systems automatically. Our systematic approach reduces ten times the effort needed to deploy a system than doing it manually. Furthermore, it converts an arduous and error-prone process of doing it manually into a simple system description.

Communicating science to the public is increasingly important. Demonstrators are a valuable and established tool for communication in technology research and development. However, their role in communicating current science and technology to the public has not received much attention neither in research nor practice. This paper reflects on the design and usage of the demonstrator “Rock, Paper, Scissors”, which we developed to communicate current advances in Human-Robot Interaction to public audiences. We discuss two years of “Rock, Paper, Scissors” in action and its evolution within this period. We conclude with an outlook to future work regarding technology development and evaluation of science communication.

Modern robotic platforms are increasingly complex due to incorporating various heterogeneous sensors and several actuators generating data at large frequencies. They are mostly based on embedded computers but relying on software solutions not entirely suited for parallel processing. FPGAs are an ideal candidate to solve this issue, enhancing those systems’ computing capabilities while still being programmable. We follow a holistic approach and study which components are needed for FPGA-based robotic applications. We propose a model-based component-oriented workflow to realize such applications. Its only input is a System’s Specification to generate components to manage N accelerators, their behavior and interfaces to several middlewares. Only simple modifications to specifications rather than complex changes to implementations are needed to generate all these tailored components for any kind of robotic applications.

This paper takes place within the field of sound source localization by combining the signals sensed by a binaural head with its motor commands. Such so-called "active" schemes are known to overcome limitations occurring in the static context, such as front-back ambiguities or distance non-observability. On the basis of a stochastic filter, which approximates the posterior probability density function of the sensor-to-source situation, a feedback controller of the sensor motion is proposed so as to reduce the associated uncertainty. An information-theoretic analysis of the effect of the sensor motion on the localization uncertainty is first conducted. Then, a gradient ascent scheme is used to drive the head towards the area of minimum uncertainty (maximum information). An evaluation on simulated scenarios, as well as on data coming from real experiments, is included.