Fabıo Stroppa

Stroppa, Fabıo

Name Variants

Fabio, Stroppa
S., Fabıo
Stroppa,F.
Stroppa,Fabio
STROPPA, FABIO
Stroppa, F.
STROPPA, Fabıo
S.,Fabio
F. Stroppa
Stroppa, Fabio
Fabıo Stroppa
Stroppa, Fabıo
S., Fabio
Stroppa F.
Stroppa, FABIO
Fabıo STROPPA
FABIO STROPPA

Job Title

Dr. Öğr. Üyesi

Email Address

fabio.stroppa@khas.edu.tr

ORCID ID

0000-0003-2644-2029

Scopus Author ID

54891556200

Full item page

Scholarly Output

6

Articles

2

Citation Count

11

Supervised Theses

0 Scholarly Output Search Results

Now showing 1 - 6 of 6

Citation Count: 7
Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints
(Institute of Electrical and Electronics Engineers Inc., 2022) Stroppa, Fabıo; Wang, K.; Do, B.H.; Stroppa, F.; Coad, M.M.; Okamura, A.M.; Liu, C.K.
Soft robot serial chain manipulators with the capability for growth, stiffness control, and discrete joints have the potential to approach the dexterity of traditional robot arms, while improving safety, lowering cost, and providing an increased workspace, with potential application in home environments. This paper presents an approach for design optimization of such robots to reach specified targets while minimizing the number of discrete joints and thus construction and actuation costs. We define a maximum number of allowable joints, as well as hardware constraints imposed by the materials and actuation available for soft growing robots, and we formulate and solve an optimization problem to output a planar robot design, i.e., the total number of potential joints and their locations along the robot body, which reaches all the desired targets, avoids known obstacles, and maximizes the workspace. We demonstrate a process to rapidly construct the resulting soft growing robot design. Finally, we use our algorithm to evaluate the ability of this design to reach new targets and demonstrate the algorithm's utility as a design tool to explore robot capabilities given various constraints and objectives. © 2022 IEEE.
Citation Count: 1
Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey
(Mdpi, 2023) Stroppa, Fabıo; Soylemez, Aleyna; Yuksel, Huseyin Taner; Akbas, Baris; Sarac, Mine
Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human-machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords exoskeleton, design, and optimization. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research.
Citation Count: 0
Optimizing Real-Time Decision-Making in Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2023) Stroppa, Fabıo; Stroppa,F.; Badia,L.
The rapid integration of digital technologies into physical systems has given rise to cyber-physical systems, where the interaction between the computational and physical components plays a crucial role. This study explores optimal decision-making in event detection and transmission scheduling within cyber-physical systems, emphasizing the crucial aspect of efficient decision-making. We consider the problem of monitoring and reporting about a single event taking place within a finite time window achieving a reward related to the timeliness of the status update. Thus, the objective corresponds to minimizing the age of information between the instant of the event x and the status update time t, with a further penalty for a missed event. The monitoring apparatus decides when to perform the status update without knowing the value of x, but only knowing its statistical distribution. We assume a triangular probability density function for the instant of the event taking place, with a variable average. We provide an analytical derivation of the optimal choice of the status update, highlighting interesting trends, such as the saturation in the value of t as x grows close to the limit of the observation window. This proposed problem and its analytical formalization may serve as a further foundation for the general analysis of optimal monitoring of cyber-physical systems. © 2023 IEEE.
Citation Count: 2
Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator
(Institute for Ionics, 2023) Stroppa, Fabıo; Selvaggio, M.; Agharese, N.; Luo, M.; Blumenschein, L.H.; Hawkes, E.W.; Okamura, A.M.
Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot’s autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Citation Count: 0
Large monitors reduce tracking error in robot-assisted visual-motor tasks
(IEEE, 2022) Stroppa, Fabıo; Amirabdollahian, Farshid; Frisoli, Antonio
Robot-assisted rehabilitation often makes use of virtual environments to present the therapy tasks. Virtual reality has the ability of providing valuable visual feedback and enjoyable interaction to the patients; therefore, the way they are displayed to users becomes crucial. Is the monitor size an important feature that influences how the task is perceived and thus affects patients' performance? This study on healthy participants investigates the influence of displays in perceiving haptic effects. The participants performed an experiment using an end-effector robot, where they followed a moving target around a trajectory while disturbed by a simulated perturbation and assisted by an adaptive algorithm. The experiment was presented on two different monitors to assess whether a different size affects their performance. Statistically significant results show that the performance achieved with the large monitor features lower error compared to the small monitor, implying that large monitors might be a better solution for rehabilitation with virtual tasks and assistive robots.
Citation Count: 1
Design optimizer for planar soft-growing robot manipulators
(Pergamon-elsevier Science Ltd, 2024) Stroppa, Fabıo
Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovations in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delicate/dangerous environments, manipulation of items, or assistance in domestic environments. This work presents a novel approach for design optimization of soft-growing robots, which will be used prior to manufacturing to suggest to engineers - or robot designer enthusiasts - the optimal size of the robot to be built for solving a specific task. The design process is modeled as a multi-objective optimization problem, optimizing the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. The method exploits the advantages of population-based optimization algorithms, in particular evolutionary algorithms, to transform the problem from multi-objective into single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. The proposed method was tested on different tasks to assess its optimality, which showed significant performance in solving the problem: the retrieved designs are short, smooth, and precise at reaching targets. Finally, comparative experiments show that the proposed method works better than the one existing in the literature in terms of precision (14% higher), resource consumption (2% shorter configurations with 4% fewer links), actuation (85% less wavy and undulated configurations), and run time (13% faster).