YorkSpace

YorkSpace is York University's Institutional Repository. It supports York University's Senate Policy on Open Access by providing York community members with a place to preserve their research online in an institutional context.

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Recent Submissions

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    Hardware-in-the-loop emulation for 6-DOF free-floating spacecraft using active gravity compensation with a collaborative robot
    (Elsevier, 2026-05-28) Jahanshahi, Hadi
    This technical note presents a hardware-in-the-loop testbed that uses a UR10e collaborative robot to emulate 6-DOF free-floating spacecraft motion through active gravity compensation. Experimental validations across all three Cartesian axes demonstrated high translational fidelity with a mean speed correlation of 0.9992 between the measured robot trajectory and an independently initialized rigid-body simulation, low residual forces below the controller deadband, and minor rotational errors of 2° to 6° attributed to kinematic coupling through the robot joint structure. The platform provides a cost-effective testbed for ground-based emulation of free-floating spacecraft dynamics.
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    A comprehensive review of tactile sensing technologies in space robotics
    (Elsevier, 2025-01-23) Jahanshahi, Hadi; Zhu, Zheng Hong
    This review explores the current state and future prospects of tactile sensing technologies in space robotics, addressing the unique challenges posed by harsh space environments such as extreme temperatures, radiation, microgravity, and vacuum conditions, which necessitate specialized sensor designs. We provide a detailed analysis of four primary types of tactile sensors: resistive, capacitive, piezoelectric, and optical, evaluating their operating principles, advantages, limitations, and specific applications in space exploration. Recent advancements in materials science, including the development of radiation-hardened components and flexible sensor materials, are discussed alongside innovations in sensor design and integration techniques that enhance performance and durability under space conditions. Through case studies of various space robotic systems, such as Mars rovers, robotic arms like Canadarm, humanoid robots like Robonaut, and specialized robots like Astrobee and LEMUR 3, this review highlights the crucial role of tactile sensing in enabling precise manipulation, environmental interaction, and autonomous operations in space. Moreover, it synthesizes current research and applications to underscore the transformative impact of tactile sensing technologies on space robotics and highlights their pivotal role in expanding human presence and scientific understanding in space, offering strategic insights and recommendations to guide future research and development in this critical field.
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    Comprehensive review of swarm intelligence for space robotics
    (Elsevier, 2026-06-26) Zhang, Zixuan; Zhu, Zhang Hong
    Swarm intelligence has emerged as a transformative paradigm for autonomous space robotics, enabling scalable, robust, and adaptive behaviors through decentralized coordination of multiple agents. Inspired by collective phenomena in nature, swarm intelligence provides solutions to the challenges of extreme space environments, where resilience, autonomy, and fault tolerance are crucial. This review explores recent advances in the modeling, control, and validation of swarm-based space robotic systems. Mathematical frameworks ranging from single- and double-integrator dynamics to orbital swarm dynamics are examined, alongside formation control strategies such as consensus-based, leader–follower, virtual structure, and behavior-based approaches. The review covers swarm controllability, scalability, and performance metrics, highlighting trade-offs between efficiency, robustness, and computational complexity. Emerging optimization paradigms, including bio-inspired algorithms, hybrid global-local strategies, and multi-objective optimization, are surveyed for their applicability to mission-critical tasks such as debris removal, and distributed satellite constellations. The review also investigates numerical simulation platforms and experimental testbeds associated with swarm intelligence, highlighting their role in bridging the gap between theory and deployment. Case studies of current and proposed space missions illustrate the transition of swarm intelligence from conceptual design to operational reality, while trends in reinforcement learning, blockchain integration, and large language model-guided swarms signal future research directions. By consolidating theoretical foundations, experimental progress, and mission applications, this paper outlines the opportunities and challenges of harnessing swarm intelligence for future space exploration and infrastructure.
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    Review of machine learning in robotic grasping control in space application
    (Elsevier, 2024-04-15) Jahanshahi, Hadi; Zhu, Zheng Hong
    This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges and advancements for space applications. The incorporation of artificial intelligence, particularly through deep learning, reinforcement learning, transfer learning, convolutional neural networks and recurrent neural networks, has significantly revolutionized robotic grasping. These advancements facilitate autonomous, efficient, and sophisticated manipulation in the challenging environment of outer space, transitioning from traditional mechanical grippers to sophisticated systems powered by advanced algorithms. This transition highlights the critical integration of sensory perception, grasp planning, and execution mechanisms, enhancing robots' capabilities to perceive, interact with, and manipulate objects with unprecedented precision and adaptability. The article meticulously outlines significant advancements achieved through the deployment of convolutional neural networks for visual information processing, RNNs for sequential decision-making, RL for autonomous strategy refinement, and transfer learning for leveraging pre-learned knowledge in novel tasks. These technologies address the unique challenges of space environments, such as varied textures, occlusions, microgravity conditions, and the sim-to-real gap, by enhancing sample efficiency, improving sim-to-real transfer capabilities, and integrating multimodal data for better object localization and pose estimation. Furthermore, the review explores the specific challenges faced in space robotic grasping, including handling varied textures and occlusions, adapting to unpredictable conditions, achieving real-time processing, and ensuring safety and reliability. It proposes future research directions focused on overcoming these hurdles, such as enhanced generalization through multimodal learning, robust sim-to-real transfer techniques, and the development of collaborative robotics and swarm intelligence. Critical to the development of ML models for robotic grasping are the roles of specialized datasets and simulation environments. Datasets like the Cornell Grasping Dataset and the Yale-CMU-Berkeley Object, along with simulation platforms such as Gazebo and PyBullet, provide essential resources for training, testing, and refining ML models. These tools enable researchers to simulate complex robotic systems and interactions within realistic environments, fostering rapid iterations on design and control strategies. In summary, this article offers in-depth insights into the progress, current challenges, and future prospects of machine learning techniques in robotic grasping for space exploration. It showcases significant strides made in the field and charts a path forward, emphasizing the need for innovative solutions to navigate the complexities of robotic manipulation in outer space. Through the strategic integration of advanced ML techniques, the development of adaptable and efficient robotic systems for space applications continues to advance, promising to unlock new possibilities in space exploration and beyond.
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    Uncertainty propagation networks for neural ordinary differential equations
    (Elsevier, 2026-02-23) Jahanshahi, Hadi; Zhu, Zheng Hong
    This paper introduces Uncertainty Propagation Network (UPN), a novel family of neural differential equations that naturally incorporate uncertainty quantification into continuous-time modeling. Unlike existing neural ordinary differential equations (neural ODEs) that predict only state trajectories, UPN simultaneously models both state evolution and its associated uncertainty by parameterizing coupled differential equations for mean and covariance dynamics. The architecture is grounded in Gaussian moment closure approximation, which enables efficient analytical uncertainty propagation through nonlinear dynamics without requiring stochastic sampling or ensemble methods. UPN supports two operational modes: pure prediction from initial conditions, and adaptive filtering with sparse measurement updates when observations become available during the prediction horizon. The continuous-depth formulation provides principled uncertainty quantification in a single forward pass, handles irregularly-sampled observations naturally, and adapts evaluation strategy to each input’s complexity. Experimental results demonstrate UPN’s effectiveness across multiple domains: (1) four canonical non-chaotic dynamical systems achieve near-perfect 96.7 % confidence interval coverage with single-point Markovian initialization; (2) chaotic Lorenz attractor modeling maintains 94.5 % calibration while correctly capturing exponential uncertainty growth in a fully Markovian framework; (3) real-world CubeSat trajectory prediction achieves 89.6 % error reduction through integrated measurement updates; and (4) time-series forecasting on the ETTh1 benchmark dataset demonstrates 14 % improved accuracy and 6.6 × faster inference compared to Neural Stochastic Differential Equations (Neural SDEs). These gains stem from UPN’s analytical distribution evolution, which provides superior computational efficiency and calibration compared to sampling-based approaches.