Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey
Abstract
:1. Introduction
Search Strategy and Eligibility Criteria
2. Background
2.1. Background on Exoskeletons
- Assistance: Assistive exoskeletons augment users’ physical abilities to help them perform real-time activities that might be challenging to be completed alone. These devices can be used by (i) people with disabilities in their daily lives or (ii) healthy workers while performing physically demanding tasks in a workspace (see Figure 2a). Regardless of the target users, these devices must be capable of adapting their operation to perform different tasks or to interact with different objects. They must be portable, lightweight, and easy to wear while applying high interaction forces. They must achieve the range of motion of the anatomical joints without harming users when functionality limits are reached. Finally, these devices should feel highly transparent to follow the physical guidance of users. Observing/tracking movement performance is neither mandatory nor favorable.
- Physical Rehabilitation: Rehabilitative exoskeletons are used in clinical settings to treat patients suffering from physical or neurological disabilities (see Figure 2b). Due to users’ limited functional capabilities, rehabilitative exoskeletons must be easy to wear without a predefined initial orientation (i.e., the device adapts to the patient’s position rather than the opposite) and provide high output forces with respect to the actuator size adopted for the exoskeleton. The range of motion of anatomical joints must be achieved without harming patients when functionality limits are reached. Patients should be able to perform different actions with no prior control or mechanical design change thanks to the devices’ instant adaptability. Rehabilitation exoskeletons allow patients to actively participate in therapy exercises and monitor their progress in muscular activity. Unlike assistive devices, they are often grounded and do not need to be portable.
- Haptic Rendering: Haptic exoskeletons render an artificial sense of touch in response to virtual interactions or remotely operated real interactions (see Figure 2c). They must be wearable to track users’ joint movements to control the interactions performed by virtual avatars or remote robots. Similarly to assistive devices, portability and instant adaptability are crucial. Haptic exoskeletons must feel highly transparent to follow users’ physical guidance, especially when there is no interaction at the virtual/remote site. Since the target user profile is assumed to be healthy, the wearability or the amount of output forces is not as crucial as for other applications but is preferred.
- Workspace: The workspace is the range of motion the user is allowed while wearing an exoskeleton. Exoskeletons must respect the natural movements of users’ limbs to ensure safety, and their mechanical limits must not exert force on human joints once they reach their natural limits. An exoskeleton must be comfortable, as users wear the device during operation. The kinematic and ergonomic design must be ensured not to cause any pain or fatigue.
- –
- While designing an exoskeleton, the mechanical joints must be aligned with the anatomical joints with minimal mechanical changes and cover the overall range of motion for the anatomical joints they are aligned with. This allows the exoskeleton to be inherently safe, ergonomic, and comfortable. To achieve this outcome, an optimization algorithm must retrieve the best link lengths and the actuated motion to either (i) maximize the operational workspace for each assisted joint or (ii) maximize a different design requirement and simultaneously ensure that the natural workspace is covered via constraints.
- Force Transmission: The human body has highly complex kinematics. For example, the human wrist can be modeled with three DoFs [31] (flexion, pronation, and radial deviation), the human finger with four DoFs [32] (one for the distal interphalangeal joint, one for the proximal interphalangeal joint, and two for the metacarpophalangeal joint), etc. The anatomic joints’ complexity (and their proximity to each other) has led designers to decouple the actuators from the joints and transmit the actuator forces through linkage-based mechanical devices—whether they are made of rigid or soft materials. In addition, linkage-based transmission allows designers to augment the transmitted forces through effective kinematic chains and lower the actuator size. The efficacy of its force transmission should be evaluated based on (i) the size of the wearable actuator components, (ii) the amount of force/torque rendered on the user’s joints safely and comfortably, and (iii) the ratio between the actuated and output forces for each independent joint.
- –
- While designing an exoskeleton, an optimization algorithm should retrieve the best link lengths to maximize the force transmission for each assisted joint or for the overall targeted task (e.g., grasping a one-liter water bottle or lifting a five-kilogram storage box). Using such optimization techniques could also yield the same output forces with smaller actuators, improving the portability/wearability of the system as well.
- Adjustability/Calibration: Unlike prosthesis devices, exoskeletons are not custom-made for each potential user with different limb sizes. This lack of customization might cause misalignment, harm users, or work with a limited operational workspace or performance. In addition, especially for rehabilitative applications, wearing an exoskeleton should be equal and pain-free for every user.
- –
- While designing an exoskeleton, an optimization algorithm should ensure the same performance for users of all sizes. There are three ways of achieving this outcome: (i) maximize the allowed range of limb sizes with no focus on other metrics, (ii) maximize the allowed range of limb sizes while optimizing another design requirement simultaneously, or (iii) maximize one of the previously detailed design requirements while ensuring an acceptable range of adjustability to different limb sizes via constraints.
- Size: While some full-arm exoskeletons need to be carried by a base due to their high weight [13], there is a great deal of research on reducing their weight and making them portable [33]. Exoskeletons can have improved portability by minimizing the mechanical components’ size or weight.
- –
- While designing an exoskeleton, an optimization algorithm should ensure the same performance with the smallest set of link lengths as much as possible by (i) minimizing the link lengths while other performance measures are fixed at a reasonable and predefined level via constraints or (ii) maximizing a different design requirement and simultaneously ensuring the acceptable set of link lengths to be covered via constraints.
2.2. Background on Optimization and Evolutionary Computation
2.2.1. Optimization Problems
2.2.2. Optimization Methods
- Objective functions are usually complex (i.e., nonlinear, non-convex, discontinuous, discrete, mixed, and multimodal), and the effectiveness of the exact methods is not guaranteed [37];
- Robust solutions are preferred over global ones (i.e., when the optimal solution lies on a peak of the function, a small change in its value causes instability to the system, whereas a sub–optimal solution lying in a plateau area is less susceptible to change) [38];
- Reliable solutions are preferred over global ones, as uncertainty in the search space might lead to infeasible optimal solutions (i.e., in violation of the constraints) [39].
2.2.3. Evolutionary Computation
3. Mechanical Design Optimization
3.1. Exoskeleton Design with Evolutionary Computation Techniques
3.1.1. Genetic Algorithms (GAs)
Weight-Based Genetic Algorithm (WBGA)
- Yoon et al. [60] retrieved the optimal joint locations that minimize (i) the misalignment of the exoskeleton and (ii) the frame protrusion of their shoulder exoskeleton for assistive use;
- Asker et al. [61] retrieved the optimal link lengths that (i) maximize the force transmission and (ii) minimize the misalignment between a human and their knee exoskeleton for assistive use;
- McDaid [65] retrieved the optimal link lengths that (i) maximize the workspace, (ii) maximize the distance from robot singularities, and (iii) minimize the size of their leg exoskeleton for rehabilitation; and
- Yu et al. [69] retrieved the initial conditions that minimize (i) displacement and (ii) dynamics of the hydraulic cylinder composing the joint of their knee exoskeleton for assistive use.
Elitist Non-Dominated Sorting Genetic Algorithm (NSGA–II)
- Li et al. [51] retrieved the optimal link lengths that (i) maximize the force transmission and (ii) minimize the difference between contact forces and reduce the ejection phenomenon (i.e., fingers push the targeted object away instead of grasping it) while satisfying an accepted range of motion on their hand exoskeleton for assistive use;
- Lee et al. [54] retrieved the optimal rotational angles and joint distribution that maximize (i) the accuracy and dexterity through an index estimating the kinematic performance of specific posture (the global condition index) and (ii) the minimum distance between the links and the centerline (the interference safety margin) of their wrist exoskeleton for rehabilitation and haptic use;
- Hunt et al. [55] retrieved the optimal actuator location, maximizing its stiffness volume both for (i) translation and (ii) rotation of their shoulder exoskeleton for assistive use;
- Deboer et al. [62] retrieved the optimal link lengths, spring stiffness values, angles, and displacements that minimize (i) the peak power and (ii) the total length of the two actuators of their leg exoskeleton for assistive use;
- Paez et al. [63] retrieved the optimal link lengths and joint locations that minimize (i) the moment load at the joint, (ii) the difference between the natural human posture and that observed while wearing the exoskeleton to promote a natural torso motion, and (iii) the torque deviation to match a linear profile while independently maximizing the torque output of their knee exoskeleton for assistive use; and
- Rituraj et al. [68] retrieved the optimal link lengths and angles that minimize (i) the maximum distance between the actuators and (ii) the load on the device of their assistive/rehabilitative knee exoskeleton for rehabilitation and assistive use.
3.1.2. Swarm Intelligence (SI)
- Du Z. et al. [57] independently retrieved (i) the optimal link lengths that minimize the misalignment between human and robot and (ii) the optimal rope position of the cable-driven mechanism that maximizes the torque actuating their arm-support exoskeleton for assistive use;
- Tian et al. [64] retrieved the optimal link lengths that minimize the force transmission of their leg exoskeleton for wheelchair assistive use to support the user from sitting to standing; and
3.1.3. Differential Evolution (DE)
- Zakaryan et al. [58] used a weight-sum-based DE [59] to retrieve the optimal link and joint weights that minimize (i) the total mass of the device, (ii) the maximal magnitudes of cable tensions, and (iii) the maximal difference between magnitudes of agonist–antagonist cable tensions (i.e., to resemble the structure of the natural muscular system of human limbs) of their arm exoskeleton for rehabilitation.
3.2. Designs with Other Optimization Techniques (Non-Evolutionary)
3.2.1. Interior Point Algorithm (IPA)
- Xu et al. [85] retrieved the optimal link lengths that minimize the difference between the workspace covered by the human and their hand exoskeleton for rehabilitation;
- Secciani et al. [87] retrieved the optimal geometrical parameters that allow the kinematics to minimize the error to the desired trajectories of their hand exoskeleton for assistive use;
- Kulkarni et al. [88] retrieved the optimal link lengths that minimize the difference between the workspace covered by the human and their wrist exoskeleton for assistive use over two joints (one joint is optimized, while the other joint is modeled as an inequality constraint);
- Balser et al. [90] retrieved the optimal parameters of the cable-driven actuator (i.e., radius, cable, and pulley) and link lengths that minimize the difference between the torques generated by the human and their shoulder exoskeleton for assistive use;
- Anderson et al. [93] retrieved the optimal “mechanical parameters” (The authors left the description of these mechanical parameters intentionally abstract, and they reported that “the system-specific mechanical design will determine which variables affect the model outputs”) that minimize the reflected inertia of their leg exoskeleton for rehabilitation and assistive use;
- Kim et al. [96] retrieved the optimal position and pressure of pneumatic actuators that minimize the average energy consumption rate of the human joint while running using their leg exoskeleton for assistive use;
- Xiao et al. [95] retrieved the optimal link lengths and structural angle that minimize the torque exerted by their knee exoskeleton for assistive use; and
- Bougrinat et al. [97] retrieved the optimal link length and angles that maximize the artificial lever arm (i.e., the perpendicular distance between the joint and the line of action) of their ankle exoskeleton for assistive use.
3.2.2. Levenberg–Marquardt Algorithm (LMA)
- Amirpour et al. [82] retrieved the optimal link lengths that (i) minimize the difference between worst-case workspace dexterity and isotropy and (ii) minimize the distance between the angle of the forces exerted on finger phalanges and the perpendicular direction on their hand exoskeleton for haptic use; and
- Bianchi et al. [83] retrieved the optimal link lengths that minimize the maximum value of the torque of their hand exoskeleton for rehabilitative use.
3.2.3. Geometric Differentiation (GD)
- Liang et al. [84] retrieved the optimal joint shape (i.e., link lengths and their poses) that minimizes the joint workspace and trajectory of humans and the hand exoskeleton for rehabilitation and assistive use. They particularly focused on designing a flexible joint based on the anatomy of grasshoppers (a model that can also be observed in crustaceans such as crabs and lobsters). They proposed a GD-based optimization algorithm (Congjugate Surface Optimization Algorithm) that evaluates the fingertip’s path during flexion and extension motion.
3.2.4. Goal Attainment Method (GAM)
- Qin et al. [86] retrieved the optimal link lengths that minimize the misalignments between the finger joints (one objective for each joint, for a total of two joints) and their hand exoskeleton for rehabilitation.
3.2.5. Pareto Local Search (PLS)
- Vatsal et al. [89] retrieved the optimal design parameters (joint angles, spring parameters, moments, and forces) that minimize muscle effort rates during realistic dynamic tasks (each muscle is defined as a separate objective function, resulting in an MOOP) while using their shoulder exoskeleton for assistive use.
3.2.6. Nelder–Mead Simplex Method (NMSM)
- Malizia et al. [94] retrieved the optimal properties (spring location, spring resting length, and angle between spring and leg) that minimize the error between the desired torque and the actual torque exerted by their leg exoskeleton for assistive use.
3.2.7. Simulated Annealing (SA)
- Malizia et al. [94] enhanced the results of the primary optimization method (as discussed in Section 3.2.6, the authors primarily used NMSM) by outdistancing the starting parameters of each optimization attempt from the optimal values found in the previous attempt on their leg exoskeleton for assistive use.
4. Discussion
4.1. Discussion on Optimization Methods
4.1.1. Wrong use of the term “MOGA”
4.1.2. Popularity of NSGA–II and Interior Point Algorithm
4.1.3. Simplified Implementation of WBGA
4.1.4. When to Use Different EC Methods
Multi Modality
Multi Objective
Choosing the Right MOEA
Hybrid Techniques: Combining Different Optimization Methods
4.2. Recommendations for Future Engineer Designers
4.2.1. Advantages of EC over Other Methods for Mechanical Design of Exoskeletons
Non-Differentiable and Complex Design Spaces
Global Optimization in Wide or Multi–Objective Search Spaces
Solution Exploration and Diversity
Robustness to Noisy or Incomplete Data
Encouraging Innovative Designs
4.2.2. Importance of Using Mathematical Optimization in Engineering
4.2.3. Considerations for Optimal Control
4.2.4. Considerations for Structural Optimization
4.2.5. Importance of Detailing the Optimization Method
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Marcheschi, S.; Salsedo, F.; Fontana, M.; Bergamasco, M. Body Extender: Whole Body Exoskeleton for Human Power Augmentation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9–13 May 2011; pp. 611–616. [Google Scholar]
- Gijbels, D.; Lamers, I.; Kerkhofs, L.; Alders, G.; Knippenberg, E.; Feys, P. The Armeo Spring as Training Tool to Improve Upper Limb Functionality in Multiple Sclerosis: A Pilot Study. J. Neuroeng. Rehabil. 2011, 8, 5. [Google Scholar] [CrossRef] [Green Version]
- Sarac, M.; Solazzi, M.; Sotgiu, E.; Bergamasco, M.; Frisoli, A. Design and Kinematic Optimization of a Novel Underactuated Robotic Hand Exoskeleton. Meccanica 2017, 52, 749–761. [Google Scholar] [CrossRef]
- Zoss, A.B.; Kazerooni, H.; Chu, A. Biomechanical Design of the Berkeley Lower Extremity Exoskeleton (BLEEX). IEEE/ASME Trans. Mechatron. 2006, 11, 128–138. [Google Scholar] [CrossRef]
- Chen, B.; Zi, B.; Wang, Z.; Qin, L.; Liao, W.H. Knee Exoskeletons for Gait Rehabilitation and Human Performance Augmentation: A State-of-the-art. Mech. Mach. Theory 2019, 134, 499–511. [Google Scholar] [CrossRef]
- Buongiorno, D.; Sotgiu, E.; Leonardis, D.; Marcheschi, S.; Solazzi, M.; Frisoli, A. WRES: A novel 3 DoF WRist ExoSkeleton with Tendon-driven Differential Transmission for Neuro-rehabilitation and Teleoperation. IEEE Robot. Autom. Lett. 2018, 3, 2152–2159. [Google Scholar] [CrossRef]
- Lenzo, B.; Fontana, M.; Marcheschi, S.; Salsedo, F.; Frisoli, A.; Bergamasco, M. Trackhold: A Novel Passive Arm-support Device. J. Mech. Robot. 2016, 8, 021007. [Google Scholar] [CrossRef] [Green Version]
- Casas, R.; Martin, K.; Sandison, M.; Lum, P.S. A Tracking Device for a Wearable High-DOF Passive Hand Exoskeleton. In Proceedings of the IEEE Annual International Conference of the Engineering in Medicine & Biology Society (EMBC), Virtual, 1–5 November 2021; pp. 6643–6646. [Google Scholar]
- Koyama, T.; Yamano, I.; Takemura, K.; Maeno, T. Multi-fingered Exoskeleton Haptic Device using Passive Force Feedback for Dexterous Teleoperation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, 30 September–4 October 2002; Volume 3, pp. 2905–2910. [Google Scholar]
- Zhou, N.; Liu, Y.; Song, Q.; Wu, D. A Compatible Design of a Passive Exoskeleton to Reduce the Body—Exoskeleton Interaction Force. Machines 2022, 10, 371. [Google Scholar] [CrossRef]
- Looze, M.; Bosch, T.; Krause, F.; Stadler, K.; O’Sullivan, L. Exoskeletons for Industrial Application and Their Potential Effects on Physical Work Load. Ergonomics 2015, 59, 671–681. [Google Scholar] [CrossRef] [Green Version]
- Sado, F.; Yap, H.J.; Ghazilla, R.A.R.; Ahmad, N. Design and Control of a Wearable Lower-body Exoskeleton for Squatting and Walking Assistance in Manual Handling Works. Mechatronics 2019, 63, 102272. [Google Scholar] [CrossRef]
- Stroppa, F.; Loconsole, C.; Marcheschi, S.; Frisoli, A. A Robot-assisted Neuro-rehabilitation System for Post-stroke Patients’ Motor Skill Evaluation with ALEx Exoskeleton. In Proceedings of the Proceedings of the International Conference on NeuroRehabilitation (ICNR), Segovia, Spain, 18–21 October 2016; pp. 501–505. [Google Scholar]
- Sarac, M.; Solazzi, M.; Frisoli, A. Design Requirements of Generic Hand Exoskeletons and Survey of Hand Exoskeletons for Rehabilitation, Assistive, or Haptic Use. IEEE Trans. Haptics 2019, 12, 400–413. [Google Scholar] [CrossRef] [Green Version]
- Sioshansi, R.; Conejo, A.J. Optimization in Engineering; Springer International Publishing: Cham, Switzerland, 2017; Volume 120. [Google Scholar]
- Statnikov, R.B.; Matusov, J.B. Multicriteria Optimization and Engineering; Springer Science & Business Media: Dordrecht, The Netherlands, 2012. [Google Scholar]
- Andersson, J. A Survey of Multiobjective Optimization in Engineering Design; Department of Mechanical Engineering, Linköping University: Linköping, Sweden, 2000. [Google Scholar]
- Bonnans, J.F.; Gilbert, J.C.; Lemaréchal, C.; Sagastizábal, C.A. Numerical Optimization: Theoretical and Practical Aspects; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006. [Google Scholar]
- Deb, K. Evolutionary Algorithms for Multi-criterion Optimization in Engineering Design. Evol. Algorithms Eng. Comput. Sci. 1999, 2, 135–161. [Google Scholar]
- Dumitrescu, D.; Lazzerini, B.; Jain, L.C.; Dumitrescu, A. Evolutionary Computation; CRC Press: Boca Raton, FL, USA, 2000. [Google Scholar]
- Du, Z.; Zefeng, Y.; Huang, T.; Bai, O.; Huang, Q.; Han, B. Mechanical Design with Experimental Verification of a Lightweight Exoskeleton Chair. J. Bionic Eng. 2021, 18, 319–332. [Google Scholar] [CrossRef]
- Bartalucci, L.; Cavuoti, C.; Secciani, N.; Gelli, J.; Della Valle, A.; Allotta, B.; Ridolfi, A. 3D-Printing-Oriented Mechanical Redesign of a Hand Exoskeleton System for Rehabilitative Tasks. In Proceedings of the International Conference on Biomedical Imaging, Signal Processing, Xiamen, China, 29–31 October 2021; pp. 51–57. [Google Scholar]
- Liu, C.T.; Yang, K.; Wu, Y.C.; Chang, H.L.; Lee, R.C.L. Designs and Performance Assessments of Permanent-Magnet Motors for Personal Mobility-Assistive Device Applications. In Proceedings of the IEEE International Conference on Electrical Machines (ICEM), Alexandroupoli, Greece, 3–6 September 2018; pp. 2130–2136. [Google Scholar]
- Liang, R.; Xu, G.; Li, M.; He, B.; Khalique, U. Fusing Topology Optimization and Pseudo-Rigid-Body Method For the Development of a Finger Exoskeleton. IEEE Robot. Autom. Lett. 2021, 7, 1721–1728. [Google Scholar] [CrossRef]
- Liang, R.; Xu, G.; He, B.; Li, M.; Teng, Z.; Zhang, S. Developing of A Rigid-Compliant Finger Joint Exoskeleton Using Topology Optimization Method. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021; pp. 10499–10504. [Google Scholar]
- Roveda, L.; Pesenti, M.; Rossi, M.; Rodriguez, M.C.; Pedrocchi, A.; Braghin, F.; Gandolla, M. User-centered back-support exoskeleton: Design and prototyping. Procedia CIRP 2022, 107, 522–527. [Google Scholar] [CrossRef]
- Zhang, G.; Tong, Q.; Zhang, T.; Tao, J.; Qiu, A. Design of a High Torque Density Robot Joint and Analysis of Force Control Method Applied for a Light Exoskeleton. Electronics 2023, 12, 397. [Google Scholar] [CrossRef]
- Lee, D.; Kwak, E.C.; McLain, B.J.; Kang, I.; Young, A.J. Effects of Assistance During Early Stance Phase Using a Robotic Knee Orthosis on Energetics, Muscle Activity, and Joint Mechanics During Incline and Decline Walking. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 914–923. [Google Scholar] [CrossRef] [PubMed]
- Buccelli, S.; Tessari, F.; Fanin, F.; De Guglielmo, L.; Capitta, G.; Piezzo, C.; Bruschi, A.; Van Son, F.; Scarpetta, S.; Succi, A.; et al. A Gravity-Compensated Upper-Limb Exoskeleton for Functional Rehabilitation of the Shoulder Complex. Appl. Sci. 2022, 12, 3364. [Google Scholar] [CrossRef]
- Gu, X.; Zhang, Y.; Sun, W.; Bian, Y.; Zhou, D.; Kristensson, P.O. Dexmo: An Inexpensive and Lightweight Mechanical Exoskeleton for Motion Capture and Force Feedback in VR. In Proceedings of the Conference on Human Factors in Computing Systems (CHI), San Jose, CA, USA, 7–12 May 2016; pp. 1991–1995. [Google Scholar]
- Eschweiler, J.; Praster, M.; Quack, V.; Michalik, R.; Hildebrand, F.; Rath, B.; Migliorini, F. Musculoskeletal Modeling of the Wrist via a Multi-body Simulation. Life 2022, 12, 581. [Google Scholar] [CrossRef]
- Gustus, A.; Stillfried, G.; Visser, J.; Jörntell, H.; van der Smagt, P. Human Hand Modelling: Kinematics, Dynamics, Applications. Biol. Cybern. 2012, 106, 741–755. [Google Scholar] [CrossRef] [Green Version]
- Pérez Vidal, A.F.; Rumbo Morales, J.Y.; Ortiz Torres, G.; Sorcia Vázquez, F.d.J.; Cruz Rojas, A.; Brizuela Mendoza, J.A.; Rodríguez Cerda, J.C. Soft Exoskeletons: Development, Requirements, and Challenges of the Last Decade. Actuators 2021, 10, 166. [Google Scholar] [CrossRef]
- Deb, K. Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evol. Comput. 1999, 7, 205–230. [Google Scholar] [CrossRef] [PubMed]
- Miettinen, K. Nonlinear Multiobjective Optimization; Springer Science & Business Media: New York, NY, USA, 2012; Volume 12. [Google Scholar]
- Deb, K. Optimization for Engineering Design: Algorithms and Examples; PHI Learning Pvt. Ltd.: Delhi, India, 2012. [Google Scholar]
- Norde, H.; Patrone, F.; Tijs, S. Characterizing Properties of Approximate Solutions for Optimization Problems. Math. Soc. Sci. 2000, 40, 297–311. [Google Scholar] [CrossRef]
- Nomaguchi, Y.; Kawakami, K.; Fujita, K.; Kishita, Y.; Hara, K.; Uwasu, M. Robust Design of System of Systems Using Uncertainty Assessment Based on Lattice Point Approach: Case Study of Distributed Generation System Design in a Japanese Dormitory Town. Int. J. Autom. Technol. 2016, 10, 678–689. [Google Scholar] [CrossRef]
- Dizangian, B.; Ghasemi, M. Reliability-based Design Optimization of Complex Functions using Self-Adaptive Particle Swarm Optimization Method. Int. J. Optim. Civ. Eng. 2015, 5, 151–165. [Google Scholar]
- Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Boston, MA, USA, 1989. [Google Scholar]
- Kennedy, J. Swarm Intelligence. In Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies; Springer: New York, NY, USA, 2006. [Google Scholar]
- Erol, O.K.; Eksin, I. A New Optimization Method: Big Bang–Big Crunch. Adv. Eng. Softw. 2006, 37, 106–111. [Google Scholar] [CrossRef]
- Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2001. [Google Scholar]
- Deb, K.; Saha, A. Multimodal Optimization using a Bi-objective Evolutionary Algorithm. Evol. Comput. 2012, 20, 27–62. [Google Scholar] [CrossRef]
- Liu, Y.; Yen, G.G.; Gong, D. A Multimodal Multiobjective Evolutionary Algorithm using Two-archive and Recombination Strategies. IEEE Trans. Evol. Comput. 2018, 23, 660–674. [Google Scholar] [CrossRef]
- Goldberg, D.E. Real-Coded Genetic Algorithms, Virtual Alphabets and Blocking. Complex Syst. 1991, 5, 139–167. [Google Scholar]
- Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Trans. Evol. Comput. 2002, 6, 182–197. [Google Scholar] [CrossRef] [Green Version]
- Hajela, P.; Lin, C.Y. Genetic Search Strategies in Multicriterion Optimal Design. Struct. Optim. 1992, 4, 99–107. [Google Scholar] [CrossRef]
- Kennedy, J.; Eberhart, R. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks (ICNN), Perth, WA, Australia, 27 November–1 December 1995; Volume 4, pp. 1942–1948. [Google Scholar]
- Storn, R.; Price, K. Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Glob. Optim. 1997, 11, 341. [Google Scholar] [CrossRef]
- Li, H.; Cheng, L.; Sun, N.; Cao, R. Design and Control of an Underactuated Finger Exoskeleton for Assisting Activities of Daily Living. IEEE/ASME Trans. Mechatron. 2022, 27, 2699–2709. [Google Scholar] [CrossRef]
- Fonseca, C.M.; Fleming, P.J. Genetic Algorithms for Multiobjective Optimization: Formulation Discussion and Generalization. In Proceedings of the International Conference on Genetic Algorithms, Urbana-Champaign, IL, USA, 1 June 1993; Volume 93, pp. 416–423. [Google Scholar]
- Du, J.; Tian, Y.; Zhang, D.; Wang, H.; Zhang, Y.; Cheng, B.; Niu, J. Mechanism Design and Performance Analysis of a Wearable Hand Rehabilitation Robot. Machines 2022, 10, 1211. [Google Scholar] [CrossRef]
- Lee, J.; Kim, H.; Yang, W. Development of Wrist Interface Based on Fully Actuated Coaxial Spherical Parallel Mechanism for Force Interaction. Sensors 2021, 21, 8073. [Google Scholar] [CrossRef]
- Hunt, J.; Artemiadis, P.; Lee, H. Optimizing Stiffness of a Novel Parallel-Actuated Robotic Shoulder Exoskeleton for a Sesired Task or Workspace. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21–25 May 2018; pp. 6745–6751. [Google Scholar]
- Tschiersky, M.; Hekman, E.E.; Brouwer, D.M.; Herder, J.L.; Suzumori, K. A Compact McKibben Muscle based Bending Actuator for Close-to-body Application in Assistive Wearable Robots. IEEE Robot. Autom. Lett. 2020, 5, 3042–3049. [Google Scholar] [CrossRef] [Green Version]
- Du, Z.; Yan, Z.; Huang, T.; Zhang, Z.; Zhang, Z.; Bai, O.; Huang, Q.; Han, B. Mechanical Design and Preliminary Performance Evaluation of a Passive Arm-support Exoskeleton. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October 2020–24 January 2021; pp. 3371–3376. [Google Scholar]
- Zakaryan, N.; Harutyunyan, M.; Sargsyan, Y. Bio-Inspired Conceptual Mechanical Design and Control of a New Human Upper Limb Exoskeleton. Robotics 2021, 10, 123. [Google Scholar] [CrossRef]
- Rodriguez, E.; Saha, B.N.; Romero-Hdz, J.; Ortega, D. A Multiobjective Differential Evolution Algorithm for Robot Inverse Kinematics. SSRG Int. J. Comput. Sci. Eng. (SSRG-IJCSE) 2016, 3, 61–69. [Google Scholar]
- Yoon, J.; Kim, S.; Moon, J.; Kim, J.; Lee, G. Minimizing Misalignment and Frame Protrusion of Shoulder Exoskeleton via Optimization for Reducing Interaction Force and Minimizing Volume. Machines 2022, 10, 1223. [Google Scholar] [CrossRef]
- Asker, A.; Xie, S.; Dehghani-Sanij, A.A. Multi-objective optimization of Force Transmission Quality and Joint Misalignment of a 5-Bar Knee Exoskeleton. In Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Delft, The Netherlands, 12–16 July 2021; pp. 122–127. [Google Scholar]
- DeBoer, B.; Hosseini, A.; Rossa, C. A Discrete Non-Linear Series Elastic Actuator for Active Ankle-Foot Orthoses. IEEE Robot. Autom. Lett. 2022, 7, 6211–6217. [Google Scholar] [CrossRef]
- Paez-Granados, D.F.; Kadone, H.; Hassan, M.; Chen, Y.; Suzuki, K. Personal Mobility with Synchronous Trunk–Knee Passive Exoskeleton: Optimizing Human–Robot Energy Transfer. IEEE/ASME Trans. Mechatron. 2022, 27, 3613–3623. [Google Scholar] [CrossRef]
- Tian, C.; Song, Z.; Ma, T. Mechanism Design of a Multifunctional Motion Assistant Robot Combined Wheelchair and Exoskeleton. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, 6–9 August 2017; pp. 1521–1525. [Google Scholar]
- McDaid, A.J. Design, Analysis, and Multicriteria Optimization of an Overground Pediatric Robotic Gait Trainer. IEEE/ASME Trans. Mechatron. 2017, 22, 1674–1684. [Google Scholar] [CrossRef]
- Xu, J.; Li, Y.; Xu, L.; Peng, C.; Chen, S.; Liu, J.; Xu, C.; Cheng, G.; Xu, H.; Liu, Y.; et al. A Multi-Mode Rehabilitation Robot with Magnetorheological Actuators Based on Human Motion Intention Estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 2019, 27, 2216–2228. [Google Scholar] [CrossRef]
- Coello, C.A.C.; Lamont, G.B.; Van Veldhuizen, D.A. Evolutionary Algorithms for Solving Multi-Objective Problems; Springer: New York, NY, USA, 2007; Volume 5. [Google Scholar]
- Rituraj, R.; Scheidl, R.; Ladner, P.; Lauber, M.; Plöckinger, A. Prototyping and Experimental Investigation of Digital Hydraulically Driven Knee Exoskeleton. Energies 2022, 15, 8695. [Google Scholar] [CrossRef]
- Yu, S. Reliability-Based Design Optimization for the Knee Joint of the Lower Extremity Exoskeleton. In Proceedings of the IEEE Annual Reliability and Maintainability Symposium (RAMS), Reno, NV, USA, 22–25 January 2018; pp. 1–5. [Google Scholar]
- Yang, X.S. Swarm Intelligence Based Algorithms: A Critical Analysis. Evol. Intell. 2014, 7, 17–28. [Google Scholar] [CrossRef] [Green Version]
- Kumar, A.; Gupta, R. Compare the Results of Tuning of PID Controller by Using PSO and GA Technique for AVR System. Int. J. Adv. Res. Comput. Eng. Technol. 2013, 6, 2130–2138. [Google Scholar]
- Ou, C.; Lin, W. Comparison Between PSO and GA for Parameters Optimization of PID Controller. In Proceedings of the IEEE International Conference on Mechatronics and Automation, Luoyang, China, 25–28 June 2006; pp. 2471–2475. [Google Scholar]
- Levenberg, K. A Method for the Solution of Certain Non-linear Problems in Least Squares. Q. Appl. Math. 1944, 2, 164–168. [Google Scholar] [CrossRef] [Green Version]
- Marquardt, D.W. An Algorithm for Least-squares Estimation of Nonlinear Parameters. J. Soc. Ind. Appl. Math. 1963, 11, 431–441. [Google Scholar] [CrossRef]
- Porteous, I.R. Geometric Differentiation: For the Intelligence of Curves and Surfaces; Cambridge University Press: Cambridge, UK, 2001. [Google Scholar]
- Byrd, R.H.; Hribar, M.E.; Nocedal, J. An Interior Point Algorithm for Large-scale Nonlinear Programming. SIAM J. Optim. 1999, 9, 877–900. [Google Scholar] [CrossRef]
- Wright, M. The Interior-Point Revolution in Optimization: History, Recent Developments, and Lasting Consequences. Bull. Am. Math. Soc. 2005, 42, 39–56. [Google Scholar] [CrossRef] [Green Version]
- Gembicki, F. Vector Optimization for Control with Performance and Parameter Sensitivity Indices. Ph.D. Thesis, Case Western Reserve University, Cleveland, OH, USA, 1974. [Google Scholar]
- Jaszkiewicz, A. Many-objective Pareto Local Search. Eur. J. Oper. Res. 2018, 271, 1001–1013. [Google Scholar] [CrossRef] [Green Version]
- Kirkpatrick, S.; Gelatt, C.D., Jr.; Vecchi, M.P. Optimization by Simulated Annealing. Science 1983, 220, 671–680. [Google Scholar] [CrossRef]
- Lagarias, J.C.; Reeds, J.A.; Wright, M.H.; Wright, P.E. Convergence Properties of the Nelder–Mead Simplex Method in Low Dimensions. SIAM J. Optim. 1998, 9, 112–147. [Google Scholar] [CrossRef] [Green Version]
- Amirpour, E.; Savabi, M.; Saboukhi, A.; Gorji, M.R.; Ghafarirad, H.; Fesharakifard, R.; Rezaei, S.M. Design and Optimization of a Multi-DOF Hand Exoskeleton for Haptic Applications. In Proceedings of the IEEE International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, 20–21 November 2019; pp. 270–275. [Google Scholar]
- Bianchi, M.; Cempini, M.; Conti, R.; Meli, E.; Ridolfi, A.; Vitiello, N.; Allotta, B. Design of a Series Elastic Transmission for hand exoskeletons. Mechatronics 2018, 51, 8–18. [Google Scholar] [CrossRef]
- Liang, R.; Xu, G.; Teng, Z.; Li, M.; Zhang, S.; Zheng, X.; Zhang, K.; He, B. A General Arthropod Joint Model and its Applications in Modeling Human Robotic Joints. IEEE Access 2021, 9, 7814–7822. [Google Scholar] [CrossRef]
- Xu, W.; Liu, Y.; Ben-Tzvi, P. Development of a Novel Low-profile Robotic Exoskeleton Glove for Patients with Brachial Plexus Injuries. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October 2022; pp. 11121–11126. [Google Scholar]
- Qin, C.; Li, P.; Yang, X.; Li, B. A Design of Hand Rehabilitation Exoskeleton Mechanism Adapted to Different Finger Lengths. In Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 6–8 December 2019; pp. 1621–1626. [Google Scholar]
- Secciani, N.; Bianchi, M.; Ridolfi, A.; Vannetti, F.; Volpe, Y.; Governi, L.; Bianchini, M.; Allotta, B. Tailor-Made Hand Exoskeletons at the University of Florence: From Kinematics to Mechatronic Design. Machines 2019, 7, 22. [Google Scholar] [CrossRef] [Green Version]
- Kulkarni, S.R.; Noronha, B.; Campolo, D.; Accoto, D. Modelling and Optimisation of a Mechanism-based Metamaterial for a Wrist Flexion-extension Assistive Device. In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021; pp. 7020–7026. [Google Scholar]
- Vatsal, V.; Purushothaman, B. Biomechanical Design Optimization of Passive Exoskeletons through Surrogate Modeling on Industrial Activity Data. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October 2022; pp. 12752–12757. [Google Scholar]
- Balser, F.; Desai, R.; Ekizoglou, A.; Bai, S. A Novel Passive Shoulder Exoskeleton Designed with Variable Stiffness Mechanism. IEEE Robot. Autom. Lett. 2022, 7, 2748–2754. [Google Scholar] [CrossRef]
- Vazzoler, G.; Bilancia, P.; Berselli, G.; Fontana, M.; Frisoli, A. Preliminary Analysis and Design of a Passive Upper Limb Exoskeleton. In Proceedings of the IEEE International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia, 6–10 December 2021; pp. 569–574. [Google Scholar]
- Vazzoler, G.; Bilancia, P.; Berselli, G.; Fontana, M.; Frisoli, A. Analysis and Preliminary Design of a Passive Upper Limb Exoskeleton. IEEE Trans. Med. Robot. Bionics 2022, 4, 558–569. [Google Scholar] [CrossRef]
- Anderson, A.; Richburg, C.; Czerniecki, J.; Aubin, P. A Model-Based Method for Minimizing Reflected Motor Inertia in Off-board Actuation Systems: Applications in Exoskeleton Design. In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada, 24–28 June 2019; pp. 360–367. [Google Scholar]
- Malizia, B.; Ryali, P.; Patton, J. Passive Exotendon Spring Elements Can Replace Muscle Torque during Gait. In Proceedings of the IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA, 29 November–1 December 2020; pp. 773–778. [Google Scholar]
- Xiao, B.; Shao, Y.; Zhang, W. Design and Optimization of Single-degree-of-freedom Six- bar Mechanisms for Knee Joint of Lower Extremity Exoskeleton Robot. In Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 6–8 December 2019; pp. 2861–2866. [Google Scholar]
- Kim, Y.; Kwon, C.; Moon, H.; Kim, K.; Cho, J.; Kong, K. Optimization of Semi-Active Pneumatic Actuators for an Exoskeleton Robot for Running. In Proceedings of the IEEE International Conference on Ubiquitous Robots (UR), Honolulu, HI, USA, 26–30 June 2018; pp. 119–124. [Google Scholar]
- Bougrinat, Y.; Achiche, S.; Raison, M. Design and development of a lightweight ankle exoskeleton for human walking augmentation. Mechatronics 2019, 64, 102297. [Google Scholar] [CrossRef] [Green Version]
- Kischka, P.; Lorenz, H.W.; Derigs, U.; Domschke, W.; Kleinschmidt, P.; Möhring, R.; Goffin, J.L.; Vial, J.P. Interior Point Methods for Nondifferentiable Optimization. In Proceedings of the Operations Research Proceedings 1997: Selected Papers of the Symposium on Operations Research (SOR), Jena, Germany, 3–5 September 1997; pp. 35–49. [Google Scholar]
- Jaszkiewicz, A.; Lust, T. ND-tree-based Update: A Fast Algorithm for the Dynamic Nondominance Problem. IEEE Trans. Evol. Comput. 2018, 22, 778–791. [Google Scholar] [CrossRef] [Green Version]
- Dantzig, G.B. Origins of the Simplex Method. In A History of Scientific Computing; Association for Computing Machinery: New York, NY, USA, 1990; pp. 141–151. [Google Scholar]
- De Jong, K.A. An Analysis of the Behavior of a Class of Genetic Adaptive Systems; University of Michigan: Ann Arbor, MI, USA, 1975. [Google Scholar]
- Goldberg, D.E.; Richardson, J. Genetic Algorithms with Sharing for Multimodal Function Optimization. In Proceedings of the Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, Cambridge, MA, USA, 28–31 July 1987; Volume 4149. [Google Scholar]
- Zhao, H.; Tang, L.; Li, J.R.; Liu, J. Strengthening Evolution-based Differential Evolution with Prediction Strategy for Multimodal Optimization and Its Application in Multi-robot Task Allocation. Appl. Soft Comput. 2023, 139, 110218. [Google Scholar] [CrossRef]
- Coello, C.A.C. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: A Survey of the State of the Art. Comput. Methods Appl. Mech. Eng. 2002, 191, 1245–1287. [Google Scholar] [CrossRef]
- Zitzler, E.; Laumanns, M.; Thiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-Rep. 2001, 103. [Google Scholar] [CrossRef]
- King, R.A.; Deb, K.; Rughooputh, H. Comparison of NSGA–II and SPEA2 on the Multiobjective Environmental/Economic Dispatch Problem. Univ. Maurit. Res. J. 2010, 16, 485–511. [Google Scholar]
- Calborean, H.; Jahr, R.; Ungerer, T.; Vintan, L. A Comparison of Multi-Objective Algorithms for the Automatic Design Space Exploration of a Superscalar System. In Advances in Intelligent Control Systems and Computer Science; Springer: Berlin/Heidelberg, Germany, 2013; pp. 489–502. [Google Scholar]
- Deb, K.; Jain, H. An Evolutionary Many-Objective Optimization Algorithm Using Reference-point-based Non-dominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Trans. Evol. Comput. 2013, 18, 577–601. [Google Scholar] [CrossRef]
- Kirk, D.E. Optimal Control Theory: An Introduction; Courier Corporation: Chelmsford, MA, USA, 2004. [Google Scholar]
- Jafarpour, M.; Eshghi, S.; Darvizeh, A.; Gorb, S.; Rajabi, H. Functional significance of graded properties of insect cuticle supported by an evolutionary analysis. J. R. Soc. Interface 2020, 17, 20200378. [Google Scholar] [CrossRef]
- Xu, W.; Guo, Y.; Bravo, C.; Ben-Tzvi, P. Design, Control, and Experimental Evaluation of a Novel Robotic Glove System for Patients with Brachial Plexus Injuries. IEEE Trans. Robot. 2022, 39, 1637–1652. [Google Scholar] [CrossRef]
- Hua, Y.; Fan, J.; Liu, G.; Zhang, X.; Lai, M.; Li, M.; Zheng, T.; Zhang, G.; Zhao, J.; Zhu, Y. A Novel Weight-Bearing Lower Limb Exoskeleton Based on Motion Intention Prediction and Locomotion State Identification. IEEE Access 2019, 7, 37620–37638. [Google Scholar] [CrossRef]
- Nguyen, V.Q.; LaPre, A.K.; Price, M.A.; Umberger, B.R.; Sup, F.C., IV. Inclusion of actuator dynamics in simulations of assisted human movement. Int. J. Numer. Methods Biomed. Eng. 2020, 36, e3334. [Google Scholar] [CrossRef]
- Gudmundsson, K.; Jonsdottir, F.; Thorsteinsson, F. A Geometrical Optimization of a Magneto-Rheological Rotary Brake in a Prosthetic Knee. Smart Mater. Struct. 2010, 19, 035023. [Google Scholar] [CrossRef]
- Droste, S.; Jansen, T.; Wegener, I. On the Analysis of the (1+ 1) Evolutionary Algorithm. Theor. Comput. Sci. 2002, 276, 51–81. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Zeng, S.; Guo, J. Time-dependent Reliability-based Design Optimization Utilizing Nonintrusive Polynomial Chaos. J. Appl. Math. 2013, 2013, 513261. [Google Scholar] [CrossRef]
Requirement | Metrics |
---|---|
User safety | Workspace, calibration |
High output forces | Force transmission |
Portability | Force transmission, size |
Wearability | Calibration, size |
Joint tracking | Calibration |
Adaptability to different tasks | Workspace, force transmission |
Abbreviation | Algorithm |
---|---|
GA | Single-Objective Genetic Algorithm [40,46] |
NSGA–II | Elitist Non-Dominated Sorting Genetic Algorithm [47] |
WBGA | Weight-Based Genetic Algorithm [48] |
PSO | Particle Swarm Optimization [49] |
DE | Differential Evolution [50] |
Authors | Limb | Application | Metrics | Optimization Method |
---|---|---|---|---|
Li et al. [51] | Hand | A | FT, W | NSGA–II [52] |
Du J. et al. [53] | Hand | R | FT | GA [40,46] |
Lee et al. [54] | Wrist | R, H | FT, W | NSGA–II [47] |
Hunt et al. [55] | Arm | A | FT | NSGA–II [47] |
Tschiersky et al. [56] | Arm | A | FT | GA [40,46] |
Du Z. et al. [57] | Arm | A | FT, W | PSO [49] |
Zakaryan et al. [58] | Arm | R | FT, S | DE [50,59] |
Yoon et al. [60] | Arm | A | W | WBGA [47] |
Asker et al. [61] | Leg | A | FT, W | WBGA [48] |
Deboer et al. [62] | Leg | A | FT, S | NSGA–II [47] |
Paez et al. [63] | Leg | A | FT, W | NSGA–II [47] |
Tian et al. [64] | Leg | A | S | PSO [49] |
McDaid [65] | Leg | R | W | WBGA [48] |
Xu et al. [66] | Leg | R | FT | PSO [49,67] |
Rituraj et al. [68] | Leg | A, R | S, FT | NSGA–II [47] |
Yu et al. [69] | Leg | A | FT | WBGA [48] |
Abbreviation | Algorithm |
---|---|
LMA | Levenberg–Marquardt Algorithm [73,74] |
GD | Geometric differentiation [75] |
IPA | Interior point algorithm [76,77] |
GAM | Goal attainment method [78] |
PLS | Pareto local search [79] |
SA | Simulated annealing [80] |
NMSM | Nelder–Mead simplex method [81] |
Authors | Limb | Application | Metrics | Optimization Method |
---|---|---|---|---|
Amirpour et al. [82] | Hand | H | W, AC | LMA [73,74] |
Bianchi et al. [83] | Hand | R | FT, S | LMA [73,74] |
Liang et al. [84] | Hand | A, R | W | GD [75] |
Xu et al. [85] | Hand | R | W | IPA [76,77] |
Qin et al. [86] | Hand | R | AC | GAM [78] |
Secciani et al. [87] | Hand | A | AC | IPA [76,77] |
Kulkarni et al. [88] | Wrist | A | W | IPA [76,77] |
Vatsal et al. [89] | Arm | A | FT | PLS [79] |
Balser et al. [90] | Arm | A | FT | IPA [76,77] |
Vazzoler et al. [91,92] | Arm | A | FT | IPA [76,77] |
Anderson et al. [93] | Leg | A, R | FT | IPA [76,77] |
Malizia et al. [94] | Leg | A | W | SA [80], NMSM [81] |
Xiao et al. [95] | Leg | A | FT | IPA [76,77] |
Kim et al. [96] | Leg | A | AC | IPA [76,77] |
Bougrinat et al. [97] | Ankle | A | S | IPA [76,77] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Stroppa, F.; Soylemez, A.; Yuksel, H.T.; Akbas, B.; Sarac, M. Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey. Robotics 2023, 12, 106. https://doi.org/10.3390/robotics12040106
Stroppa F, Soylemez A, Yuksel HT, Akbas B, Sarac M. Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey. Robotics. 2023; 12(4):106. https://doi.org/10.3390/robotics12040106
Chicago/Turabian StyleStroppa, Fabio, Aleyna Soylemez, Huseyin Taner Yuksel, Baris Akbas, and Mine Sarac. 2023. "Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey" Robotics 12, no. 4: 106. https://doi.org/10.3390/robotics12040106