Browsing by Author "Stroppa, Fabio"

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Cooperative Mission Planning for Multiple Aerial and Ground Vehicles Based on Evolutionary Computation
(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Eker, A. Harun; Oznigolyan, Masis; Karaagacli, Kemal Faruk; Gokalp, Dogukan; Bickici, Yigit; Stroppa, Fabio
The limited flight endurance of unmanned aerial vehicles (UAVs) necessitates multiple battery replacements to complete long-duration missions. In this paper, we address the cooperative mission planning of multiple UAVs and unmanned ground vehicles (UGVs), where UAVs are tasked with visiting a predetermined set of waypoints, and UGVs act as mobile battery replenishment platforms. The objective is to determine a cooperative mission plan that minimizes the overall mission completion time while respecting UAV flight time constraints. To this end, the mission is decomposed into a set of flight-and-recharge segments, and a genetic algorithm is applied to solve the resulting optimization problem. The proposed approach is evaluated using real-world datasets from three different operational areas. Extensive experiments are conducted to analyze parameter settings and validate the robustness of the method. Simulation results show that the algorithm adapts to variations in mission layout and can efficiently plan large-scale missions with thousands of waypoints, involving multiple UAVs and UGVs. A comparative study demonstrates that the proposed method achieves mission times very close to optimal solutions with a single-robot pair and remains competitive with a theoretical lower bound in multi-robot scenarios.
Citation - WoS: 12
Citation - Scopus: 12
Design optimizer for planar soft-growing robot manipulators
(Pergamon-elsevier Science Ltd, 2024) Stroppa, Fabio
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).
Enhanced Design of a Hand Exoskeleton: Balancing Force Transmission and Actuation with Evolutionary Algorithms
(IEEE-INST Electrical Electronics Engineers INC, 2025) Akbas, Baris; Yuksel, Huseyin Taner; Soylemez, Aleyna; Sarac, Mine; Stroppa, Fabio
Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study addresses significant challenges of ensuring safety and maximizing performance in the design process for an underactuated hand exoskeleton intended for physical rehabilitation. We first implemented a single objective optimization problem by maximizing force transmission from the actuator to the finger joints, then expanded into multi-objective optimization by also minimizing the variance of torques rendered on the finger joints and the actuator displacement needed. The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Our simulation results and statistical analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process - at the cost of complicating the optimization algorithm and computational burden. These findings underline the critical importance of performing proper optimization techniques while designing exoskeletons, as well as providing a significant improvement to this specific robotic design that could provide more effective rehabilitation therapies and augmented human-robot interactions.
Citation - WoS: 1
Citation - Scopus: 1
Gamifying Haptics User Studies: Comparison of Response Times From Smartphone Interfaces
(IEEE Computer Soc, 2025) Kudsi, Bushra; Xu, Doris; Sen, Umit; Yoshida, Kyle T.; Stroppa, Fabio; Nunez, Cara M.; Sarac, Mine
Haptics user studies are often restricted to a set, physical location and use methods that do not captivate the user. Applying game design elements can create an entertaining environment and increase user engagement. Using ubiquitous tools, like smartphones, to conduct haptics user studies could allow researchers to access larger participant groups while a gamified approach could facilitate the data collection by making the experiment more enjoyable. To explore this concept, this work presents a gamified version of an existing psychophysical experiment that investigates response time to multisensory cues using a smartphone based on "Whac-A-Mole". We conducted a user study to compare our gamified interface with an existing psychophysical interface with thirteen participants exploring the response time from eighteen combinations of auditory, haptic, and visual stimuli at different levels of intensities and participant preferences for both interfaces. The results demonstrate that the gamified interface successfully captured similar trends in response times and significantly elevated participant enjoyment ($p < 0.003$), but did not result in equivalent response times to the original interface. This work shows the benefits and drawbacks of following a gamification approach when designing haptics user studies and discusses factors and trade-offs to consider when gamifying studies.
Citation - WoS: 3
Citation - Scopus: 4
The Impact of Evolutionary Computation on Robotic Design: a Case Study With an Underactuated Hand Exoskeleton
(Ieee, 2024) Akbas, Baris; Yuksel, Huseyin Taner; Soylemez, Aleyna; Zyada, Mazhar Eid; Sarac, Mine; Stroppa, Fabio
Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design.
Citation - WoS: 1
Citation - Scopus: 1
Large Monitors Reduce Tracking Error in Robot-Assisted Visual-Motor Tasks
(IEEE, 2022) Stroppa, Fabio; 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 - Scopus: 1
Miniaturized Soft Growing Robots for Minimally Invasive Surgeries: Challenges and Opportunities
(Iop Publishing Ltd, 2025) Oyejide, Ayodele; Stroppa, Fabio; Sarac, Mine
Advancements in assistive robots have significantly transformed healthcare procedures in recent years. Clinical continuum robots have enhanced minimally invasive surgeries, offering benefits to patients such as reduced blood loss and a short recovery time. However, controlling these devices is difficult due to their limited accuracy in three-dimensional deflections and challenging localization, particularly in confined spaces like human internal organs. Consequently, there has been growing research interest in employing miniaturized soft growing robots, a promising alternative that provides enhanced flexibility and maneuverability. In this work, we extensively investigated issues concerning their designs and interactions with humans in clinical contexts. We took insights from the open challenges of the generic soft growing robots to examine implications for miniaturization, actuation, and biocompatibility. We proposed technological concepts and provided detailed discussions on leveraging existing technologies, such as smart sensors, haptic feedback, and artificial intelligence, to ensure the safe and efficient deployment of the robots. Finally, we offer an array of opinions from a biomedical engineering perspective that contributes to advancing research in this domain for future research to transition from conceptualization to practical clinical application of miniature soft growing robots.
Citation - WoS: 1
Citation - Scopus: 1
A Motion Planner for Growing Reconfigurable Inflated Beam Manipulators in Static Environments
(Ieee-inst Electrical Electronics Engineers inc, 2025) Altagiuri, Rawad E. H.; Zaghloul, Omar H. A.; Do, Brian H.; Stroppa, Fabio
Soft growing robots have the potential to be useful for complex manipulation tasks and navigation for inspection or search and rescue. They are designed with plant-like properties, allowing them to evert and steer multiple links and explore cluttered environments. However, this variety of operations results in multiple paths, which is one of the biggest challenges faced by classic pathfinders. In this letter, we propose a motion planner based on A$<^>*$ search specifically designed for soft growing manipulators operating on predetermined static tasks. Furthermore, we implemented a stochastic data structure to reduce the algorithm's complexity as it explores alternative paths. This allows the planner to retrieve optimal solutions over different tasks. We ran demonstrations on a set of three tasks, observing that this stochastic process does not compromise path optimality.
Mühendislik Problemlerinin Optimizasyonu için Evrimsel Algoritmalar
(2025) Aştar, Ahmet; Stroppa, Fabio
Üç boyutlu yumuşak büyüyen robotik manipülatörlerin tasarımını gerçekleştirmeyi amaçlayan çok amaçlı sürekli evrimsel bir çerçeve öneriyoruz. Ulaşılabilirlik, malzeme kullanımı, eğrilik düzgünlüğü ve çarpışmasız alanı tek bir optimizasyon problemine dahil ederek, ağırlık ayarlamasını ve karmaşık Pareto ön yüzü hesaplamalarını ortadan kaldırmak için Sıralama Bölme (Rank Partitioning) ve hayatta kalma stratejilerinden faydalanıyoruz. Dört algoritmayı, Genetik Algoritma (GA), Parçacık Sürüsü Optimizasyonu (PSO), Diferansiyel Evrim (DE) ve Büyük Patlama–Büyük Çöküş (BB–BC), aynı koşullar altında, farklı hedefler ve engeller ile karşılaştırdık. Sonuçlar, bu mühendislik problemi için en iyi algoritmaların sırasıyla GA ve PSO olduğunu, ardından DE'nin geldiğini göstermektedir; ayrıca BB–BC'nin ortalama olarak PSO ve DE'ye kıyasla daha tutarlı olduğunu iddia ediyoruz. Yaklaşımımız, sürekli robot tasarımı için ölçeklenebilir bir çözümdür ve karmaşık, yapısal olmayan alanlar için en uygun algoritmanın seçilmesine yardımcı olur.
Multiple Global Peaks Big Bang-Big Crunch Algorithm for Multimodal Optimization
(Springer Heidelberg, 2025) Stroppa, Fabio; Astar, Ahmet
The main challenge of multimodal optimization problems is identifying multiple peaks with high accuracy in multidimensional search spaces with irregular landscapes. This work proposes the Multiple Global Peaks Big Bang-Big Crunch (MGP-BBBC) algorithm, which addresses the challenge of multimodal optimization problems by introducing a specialized mechanism for each operator. The algorithm expands the Big Bang-Big Crunch algorithm, a state-of-the-art metaheuristic inspired by the universe's evolution. Specifically, MGP-BBBC groups the best individuals of the population into cluster-based centers of mass and then expands them with a progressively lower disturbance to guarantee convergence. During this process, it (i) applies a distance-based filtering to remove unnecessary elites such that the ones on smaller peaks are not lost, (ii) promotes isolated individuals based on their niche count after clustering, and (iii) balances exploration and exploitation during offspring generation to target specific accuracy levels. Experimental results on twenty multimodal benchmark test functions show that MGP-BBBC generally performs better or competitively with respect to other state-of-the-art multimodal optimizers.
Citation - WoS: 10
Citation - Scopus: 12
Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey
(Mdpi, 2023) Stroppa, Fabio; 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 - WoS: 4
Citation - Scopus: 4
Optimizing soft robot design and tracking with and without evolutionary computation: an intensive survey
(Cambridge Univ Press, 2024) Stroppa, Fabio; Majeed, Fatimah Jabbar; Batiya, Jana; Baran, Eray; Sarac, Mine
Soft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords "soft robot," "design," and "optimization." We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.
Citation - WoS: 9
Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator
(Springer, 2023) Stroppa, Fabio; Selvaggio, Mario; Agharese, Nathaniel; Luo, Ming; Blumenschein, Laura H.; Hawkes, Elliot W.; Okamura, Allison 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.
Citation - WoS: 12
Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints
(Ieee, 2022) Exarchos, Ioannis; Wang, Karen; Do, Brian H.; Stroppa, Fabio; Coad, Margaret M.; Okamura, Allison M.; Liu, C. Karen
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.