Panagiotis Polygerinos

The assessment of tissue properties via direct palpation is essential for localizing abnormalities during surgery. However, Minimally Invasive Surgery (MIS) eliminates this tactile feedback, creating a critical sensory gap. To address this, the EU Horizon project PALPABLE is developing novel fiber-optic sensing modalities for stiffness assessment. Successful integration of this technology requires an articulation interface that balances high maneuverability with the structural stability necessary for deflection-free tissue palpation. We present a single-degree-of-freedom (DOF) soft silicone actuator integrated with a modular, bioinspired passive stiffening element. Mechanical loading tests, motion tracking, and Finite Element Method (FEM)-guided design iterations validate the stiffening strategy and actuator performance. The design achieves a substantial increase in bending stiffness, improving the stiffness–pressure response by 8-fold relative to the unstiffened actuator under critical loading (normal force at a 90° bend). Furthermore, we developed a compact, custom pneumatic syringe pump with closed-loop pressure control to ensure precise, safe operation in surgical environments. By combining embedded fiber-optic sensing with machine-learning models, the system achieves free-space full-shape reconstruction with approximately 1% error. This work delivers a compact, load-capable, and sensorized soft actuator tailored for PALPABLE’s probe manipulation requirements, enabling safe, controllable, and data-rich tissue palpation in confined anatomical spaces.

Soft inflatable fabric actuators are lightweight and compliant, but are typically limited to unidirectional force/torque generation and large inflation volumes. This work presents PNEUMA: a soft inflatable fabric bidirectional actuation module capable of generating high torque in both bending directions within a single integrated architecture. The actuator delivers peak torques of 50 Nm at 0.3 MPa, with an inflation volume of ≈100 mL and a mass of 120 g, achieving a high torque‐to‐weight ratio across a motion range of 150°. Experimental characterization confirms consistent static and dynamic performance, as well as robustness under repeated pressurization up to 0.4 MPa. Integration into a knee exosuit demonstrates functional effectiveness. During the sit‐to‐stand task, peak activation of the extensor muscle decreases by 24% without significant kinematic changes. During step descent, flexor muscle activation decreases by up to 33%, indicating substantial biological torque assistance. During an isometric squat task, extensor activation decreased by 35%, accompanied by a reduction in metabolic cost of up to 23% by the end of the experimental set. The proposed architecture overcomes key limitations of conventional soft inflatable fabric actuators and provides an efficient building block for portable wearable and soft robotic systems that require bidirectional, high‐torque control.

Fabric‐based soft pneumatic actuators (SPAs) offer exceptional power‐to‐weight ratios and inherent compliance, making them very attractive in the soft robotics community and a critical component in the development of next‐generation intelligent artificial systems. Existing SPA research predominantly focuses on elastomeric actuators, and the resulting design principles and performance insights are not directly transferable to fabric‐based actuator architectures. This study proposes a systematic optimization framework for multi‐chamber elongating and bending fabric‐based SPAs, based on validated finite element models and transferable objective functions and constraints. A preliminary analysis reduces the design‐space dimensionality and identifies exploratory solutions, while a subsequent refined optimization employs statistically validated surrogate models to minimize pneumatic energy consumption under force–displacement or torque–rotation constraints. The resulting optimal designs are validated through both numerical simulations and experimental testing. The proposed framework offers a scalable and reproducible methodology that enables researchers to balance optimization fidelity and computational cost, supporting both rapid design exploration and high‐accuracy actuator optimization.

This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To mitigate this risk further, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles.

Soft inflatable fabric actuators are gaining traction in soft robotics due to their lightweight, compliant structures and capacity for generating diverse motions. However, the increasing diversity and structural complexity of their designs present significant challenges for scalable modeling and predictive performance analysis. Here, we present a unified taxonomy of soft inflatable fabric actuators, built around a stiffening actuator as a fundamental unit cell. We introduce a spring-based modeling framework that captures the mechanical behavior of complex actuator types- including elongating, contracting, and bending— through modular combinations of individual units in series or parallel. High-fidelity finite element simulations, validated experimentally, show that the mechanical response of complex multi-chamber actuators can be accurately inferred from the behavior of a single unit. Two case studies demonstrate the framework’s practical utility for task-specific actuator design, eliminating the need for iterative prototyping or computationally expensive modeling. This scalable and generalizable approach enables efficient soft actuator design for a wide range of applications, including but not limited to wearable systems, robotic manipulation, biomedical devices, and adaptive or morphing structures.

Minimally Invasive Surgery is often limited by the lack of tactile feedback. Indeed, surgeons have traditionally relied heavily on tactile feedback to estimate tissue stiffness - a critical factor in both diagnostics and treatment. With this in mind we present in this paper a flexible sensor foil, based on polymer optical waveguide. This sensor has been applied for real-time contact force measurement, material stiffness differentiation and surface texture reconstruction. Interrogated by a commercially available optoelectronic device, the sensor foil offers precise and reproducible feedback of contact forces up to 5 N, with a minimal detectable limit of 0.1 N. It also demonstrates distinct optical attenuation responses when indenting silicone samples of varying stiffnesses under controlled displacement. When integrated onto a 3D-printed module resembling an endoscopic camera and manipulated by a robotic arm, the sensor successfully generated spatial stiffness mapsof a phantom. Moreover, by sliding over structures with varying surface textures, the sensor foil was able to reconstruct surface profiles based on the light attenuation responses. The results demonstrate that the presented sensor foil possesses great potential for surgical applications by providing additional haptic information to surgeons.

Medical robotics holds transformative potential for healthcare. Robots excel in tasks requiring precision, including surgery and minimally invasive interventions, and they can enhance diagnostics through improved automated imaging techniques. Despite the application potentials, the adoption of robotics still faces obstacles, such as high costs, technological limitations, regulatory issues, and concerns about patient safety and data security. This roadmap, authored by an international team of experts, critically assesses the state of medical robotics, highlighting existing challenges and emphasizing the need for novel research contributions to improve patient care and clinical outcomes. It explores advancements in machine learning, highlighting the importance of trustworthiness and interpretability in robotics, the development of soft robotics for surgical and rehabilitation applications, and the role of image-guided robotic systems in diagnostics and therapy. Mini, micro, and nano robotics for surgical interventions, as well as rehabilitation and assistive robots, are also discussed. Furthermore, the roadmap addresses service robots in healthcare, covering navigation, logistics, and telemedicine. For each of the topics addressed, current challenges and future directions to improve patient care through medical robotics are suggested.