2026-09-24T02:49:00Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129212 2025-10-20 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms The student, Ugur Akcal, accepted the attached license on 2025-04-16 at 20:30. The student, Ugur Akcal, submitted this Thesis for approval on 2025-04-16 at 20:51. This Thesis was approved for publication on 2025-04-17 at 09:55. DSpace SAF Submission Ingestion Package generated from Vireo submission #21809 on 2025-10-19 at 18:09:29 Data-driven predictive pursuit-evasion engagement guidance and fast posture reconstruction of soft continuum arm Akcal, Ugur 2025-04-17 Chowdhary, Girish Artificial Neural Networks Data-driven Predictive Guidance Posture Reconstruction Soft Continuum Arm eng This thesis explores artificial neural network-based methodologies designed to improve performance in two critical areas of robotics: high-precision pursuit-evasion guidance and soft continuum arm posture reconstruction. First, a predictive guidance scheme is developed to enable rapid interception of agile and evasive targets with limited knowledge of evader dynamics. A recurrent neural network is trained on representative evader maneuvers to efficiently predict future acceleration commands. These predictions are incorporated into a finite-horizon optimal control problem, generating near-optimal guidance commands that significantly outperform traditional reactive laws such as proportional navigation in dynamic engagement scenarios, particularly in terms of average miss distance. Second, the thesis introduces a framework in which the Vicon motion capture system is leveraged to acquire high-fidelity ground-truth posture data in order to train an artificial neural network for fast and smooth posture reconstruction of soft continuum arms. Given the infinite-dimensional nature of soft-arm deformation, strain fields are represented using a low-dimensional set of principal components. A feed-forward neural network is trained in an unsupervised manner with a physics-informed loss to instantly infer the coefficients for the principal components from sparse marker measurements. This approach allows for real-time posture reconstruction, achieving computation speeds five orders of magnitude faster than classical iterative or optimization-based techniques while preserving accuracy and smoothness. Together, these contributions underscore the potential of neural networks to unify control, estimation, and efficient computation in robotics. By bridging pursuit-evasion engagements and continuum robot shape reconstruction, the thesis highlights the versatility and performance gains afforded by data-driven models, ultimately paving the way for advanced, high performance robotic autonomy in both aerial and soft arm applications. 2025-05 Thesis https://hdl.handle.net/2142/129212 Copyright 2025 Ugur Akcal Siebel School Comp & Data Sci Computer Science University of Illinois Urbana-Champaign M.S. Thesis