In this article, we present and experimentally validate a safe docking control strategy designed for an experimental planar floating platform, called the Slider. Three degrees-of-freedom (DOF) platforms like the Slider are used extensively in space industry and academia to emulate micro-gravity conditions on Earth, for validating in-plane Guidance, Navigation and Control (GNC) algorithms. The Slider uses an air cushion (induced by air bearings) to levitate on a smooth flat table, thus emulating the in-plane zero-gravity motion of a spacecraft in orbit. The proposed docking control strategy is applicable in the in-plane approach and docking phases of space docking missions, and is based on the Control Barrier Functions (CBF) approach, where a safe set (a Cardioid), capturing the clearance and direction-of-approach constraints, is rendered positively forward invariant. To enable precise and safe docking in the presence of unmodeled dynamics, disturbances induced by the tether and drifts induced by the non-flat floating surface, we present a switching strategy among the zero and positive level sets of a Cardioid function. In the approach phase, the positive contour of the Cardioid function smoothly steers the Slider platform into the neighborhood of a deadlock point, which is designed to be at a safe distance from the docking port. In the neighborhood of the deadlock point, Slider corrects its proximity and heading until its configuration is well-suited to enter the docking phase. The docking maneuver is initiated by the CBF switching mechanism (positive to zero contour), which expands the safe zone to include the final docking configuration. We present an analysis of the Quadratic program defining the CBF filter, and identify two deadlock points (an asymptotically stable point in the vicinity of the docking port and an unstable point diametrically opposite on the CBF boundary). Both the approach and docking phases are validated through experimentation on the Slider platform, in the presence of tether-induced disturbances and drifts induced by the non-ideal floating surface. In the docking phase, the CBF switching condition effectively handles experimental non-idealities and recovers the slider platform from unsafe configurations. The proposed docking strategy caters to the in-plane (3DOF) approach and docking phases of real space docking missions and is scalable to three-dimensional 6DOF operations, in conjunction with controllers that stabilize the attitude and the out-of-plane degree-of-freedom. Link to the video of experimental demonstration: https://youtu.be/eBiWvnKtG7U?si=QFPD-vm11wydyZSd.

Aerial 3D printing is a pioneering technology yet in its conceptual stage that combines frontiers of 3D printing and Unmanned aerial vehicles (UAVs) aiming to construct large-scale structures in remote and hard-to-reach locations autonomously. The envisioned technology will enable a paradigm shift in the construction and manufacturing industries by utilizing UAVs as precision flying construction workers. However, the limited payload-carrying capacity of the UAVs, along with the intricate dexterity required for manipulation and planning, imposes a formidable barrier to overcome. Aiming to surpass these issues, a novel aerial decomposition-based and scheduling 3D printing framework is presented in this article, which considers a near-optimal decomposition of the original 3D shape of the model into smaller, more manageable sub-parts called chunks. This is achieved by searching for planar cuts based on a heuristic function incorporating necessary constraints associated with the interconnectivity between subparts, while avoiding any possibility of collision between the UAV’s extruder and generated chunks. Additionally, an autonomous task allocation framework is presented, which determines a priority-based sequence to assign each printable chunk to a UAV for manufacturing. The efficacy of the proposed framework is demonstrated using the physics-based Gazebo simulation engine, where various primitive CAD-based aerial 3D constructions are established, accounting for the nonlinear UAVs dynamics, associated motion planning and reactive navigation through Model predictive control.

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator’s precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO).

The article introduces a novel approach to foster collaborative aerial 3D printing by leveraging the dexterous flexibility of multiple UAVs working in tandem towards autonomous construction. This cooperative operation effectively overcomes payload limitations through synchronized deployment. Nevertheless, the transformation of UAVs into aerial construction agents poses a pivotal challenge, demanding efficient coordination of task planning and effective execution among multiple collaboratively working UAVs. In pursuit of addressing this challenge, the proposed innovative framework introduces a novel chunk-decomposition strategy supported by a reactive task assignment mechanism, dynamically allocating additive manufacturing tasks based on a dependency graph derived from decomposed chunks. Furthermore, the framework promotes parallelization by minimizing interdependencies thereby reducing the overall makespan. It also incorporates conflict resolution among UAVs during the assignment process by employing probabilistic fitness scores and penalizing the probability of conflicts. Conflicts that emerge during printing execution are addressed in a decentralized manner through trajectory sharing among UAVs. This entails dynamically determining one UAV suspending its movement until conflicts are resolved. The proposed framework's effectiveness is evaluated through a GAZEBO-based simulation setup, showcasing its potential in deploying multiple UAVs for the simultaneous printing of large-scale 3D structures. - Video overview: https://youtu.be/bLnzcLrD1NA

This article introduces a novel collaborative optimal motion planning framework for parallel aerial 3D printing. The proposed novel framework is efficiently capable of handling conflicts between the utilized Unmanned Aerial Vehicles (UAVs), as they follow predefined paths, allowing for a seamless enhancement of aerial 3D printing capabilities by employing multiple UAVs to collaborate in a parallel printing process. The established approach ingeniously formulates UAVs’ motion planning as a multi-constraint optimization problem, ensuring minimal adjustments to their velocities within specified limits. This guarantees smooth and uninterrupted printing while preventing collisions and adhering to the requirements of aerial printing. To substantiate the effectiveness of our proposed motion planning algorithm, an extensive array of simulation studies have been undertaken, encompassing scenarios where multiple UAVs engage in the fabrication of diverse construction shapes. The overall novel concept is being extensively validated in simulations, while the obtained results promise for enhancing the viability and advancing the landscape of aerial additive manufacturing.

This article introduces an experimental emulation of a novel chunk-based flexible multi-DoF aerial 3D printing framework. The experimental demonstration of the overall autonomy focuses on precise motion planning and task allocation for a UAV, traversing through a series of planned space-filling paths involved in the aerial 3D printing process without physically depositing the overlaying material. The flexible multi-DoF aerial 3D printing is a newly developed framework and has the potential to strategically distribute the envisioned 3D model to be printed into small, manageable chunks suitable for distributed 3D printing. Moreover, by harnessing the dexterous flexibility due to the 6 DoF motion of UAV, the framework enables the provision of integrating the overall autonomy stack, potentially opening up an entirely new frontier in additive manufacturing. However, it’s essential to note that the feasibility of this pioneering concept is still in its very early stage of development, which yet needs to be experimentally verified. Towards this direction, experimental emulation serves as the crucial stepping stone, providing a pseudo mockup scenario by virtual material deposition, helping to identify technological gaps from simulation to reality. Experimental emulation results, supported by critical analysis and discussion, lay the foundation for addressing the technological and research challenges to significantly push the boundaries of the state-of-the-art 3D printing mechanism. - Full mission video available at https://youtu.be/gfZuYCA8jAw

The recent advances in space technology are focusing on fully autonomous, real-time, long-term orbit management and mission planning for large-scale satellite constellations in Low-Earth Orbit (LEO). Thus, a pioneering approach for autonomous orbital station-keeping has been introduced using a model-free Deep Policy Gradient-based Reinforcement Learning (DPGRL) strategy explicitly tailored for LEO. Addressing the critical need for more efficient and self-regulating orbit management in LEO satellite constellations, this work explores the potential synergy between Deep Reinforcement Learning (DRL) and Neuro-Evolution of Augmenting Topology (NEAT) to optimize station-keeping strategies with the primary goal to empower satellite to autonomously maintain their orbit in the presence of external perturbations within an allowable tolerance margin, thereby significantly reducing operational costs while maintaining precise and consistent station-keeping throughout their life cycle. The study specifically tailors DPGRL algorithms for LEO satellites, considering low-thrust constraints for maneuvers and integrating dense reward schemes and domain-based reward shaping techniques. By showcasing the adaptability and scalability of the combined NEAT and DRL framework in diverse operational scenarios, this approach holds immense promise for revolutionizing autonomous orbit management, paving the way for more efficient and adaptable satellite operations while incorporating the physical constraints of satellite, such as thruster limitations.

In this article, a novel adaptive trajectory tracking controller is designed for a payload-carrying spacecraft under full state constraints. The proposed controller can tackle state-dependent uncertainties without a priori knowledge of their structures and upper bounds. The controller ensures time-varying constraints on all states and their time derivatives. The closed-loop stability of the proposed scheme is verified analytically via the Lyapunov method, and real-life experiments using a robotic testbed validated the effectiveness of the proposed adaptive controller over the state-of-the-art.