Data-driven predictive pursuit-evasion engagement guidance and fast posture reconstruction of soft continuum arm

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Akcal, Ugur

Contributors dc:contributor

• Chowdhary, Girish

• Artificial Neural Networks
• Data-driven Predictive Guidance
• Posture Reconstruction
• Soft Continuum Arm

Statement dc:rights

• Copyright 2025 Ugur Akcal

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129212

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129212