An autonomous chunk-based aerial additive manufacturing framework is presented, supported with experimental demonstration advancing aerial 3D printing. An optimization-based decomposition algorithm transforms structures into sub-components, or chunks, treated as individual tasks coordinated via a dependency graph, ensuring sequential assignment to UAVs considering inter-dependencies and printability constraints for seamless execution. A specially designed hexacopter equipped with a pressurized canister for lightweight expandable foam extrusion is utilized to deposit the material in a controlled manner. To further enhance precise execution of the printing, an offset-free Model Predictive Control mechanism is considered to compensate reactively for disturbances and ground effect during execution. Additionally, an interlocking mechanism is introduced in the chunking process to enhance structural cohesion and improve layer adhesion. Extensive experiments demonstrate the framework’s effectiveness in constructing precise structures of various shapes, while seamlessly adapting to practical challenges, proving its potential for a transformative leap in aerial robotic capability for autonomous construction.

This paper presents a novel reactive coordination and planning framework for collaborative aerial 3D printing with Unmanned Aerial Vehicles (UAVs) while ensuring their safe and efficient simultaneous operation. The proposed framework incorporates a hierarchical dynamic scheduling embedded with a conflict resolution mechanism, enabling it to account for online adaptability to operational uncertainties and unforeseen events during execution. The novelty of the approach lies in its two-tiered hierarchical structure that tightly integrates dynamic assignment with an online conflict resolution mechanism, providing a flexible, adaptive and conflict-free solution for aerial construction tasks. This hierarchical framework introduces the first layer, which is responsible for dynamically assigning the tasks to the available fleet of UAVs. The task assignment considers precedence constraints to ensure structural integrity during construction while also prioritizing safe operation by minimizing the probability of conflicts and highly dependent tasks. In the second layer, conflicts arising from assigned paths are dynamically decomposed into smaller independent sub-graphs and resolved locally to reduce the computational complexities. Towards this, an online locally optimal spatiotemporal conflict resolution scheme is introduced for multi-agent systems to address the local conflicts efficiently. This mechanism dynamically adjusts the UAVs’ speeds with minimal deviation from an optimal reference to mitigate conflicts and ensure printing performance. Additionally, building on this local conflict resolution strategy, the framework enforces reactiveness by iteratively relaxing the problem when conflicts cannot be resolved immediately. This is executed via dynamic reduction and rearrangement of the concurrent tasks’ space to resolve the conflict between them. Moreover, insights gained from failed resolution attempts are dynamically integrated into the global dependency graph, preventing redundant computations in subsequent steps and enhancing overall efficiency and versatility. The framework is distinguished by its use of reactive task-space reconfiguration, informed by infeasible conflict resolutions, and the assignment of guaranteed conflict-free paths, unlike existing sequential or non-guaranteed approaches. The efficacy of the proposed framework is demonstrated through two case studies, constructing both a rectangular and a dome mesh with a collaborative team of UAVs, in a high-fidelity ROS-Gazebo simulation. A video of the mission can be found here https://youtu.be/Ow_qDPWmgDw.

Advancements in satellite technology have enabled the deployment of large constellations in low Earth orbit (LEO), presenting significant challenges in orbital management. Operating hundreds or even thousands of satellites imposes considerable computational and communication demands on ground control systems and operators. A primary operational challenge is maintaining satellites on their nominal trajectories in the presence of continuous perturbations, such as atmospheric drag and third-body gravitational forces. This article presents a reinforcement learning (RL)-based approach for decentralized satellite station keeping (SK). The proposed method utilizes a neural network policy—trained with the model-free soft actor-critic (SAC) algorithm in a high-fidelity, physics-based simulation environment—to compute corrective thrust vectors based on observed deviations from the satellite’s nominal unperturbed trajectory. The resulting policy is computationally efficient and suitable for deployment onboard resource-constrained, space-grade systems. The performance of the RL-based controller is evaluated through Monte Carlo simulations and compared with that of a conventional linear model predictive controller (MPC), which is widely adopted due to its relatively low computational requirements. The comparison focuses on computational complexity, control performance, and robustness. Results demonstrate that the RL-based controller can achieve improved maneuver efficiency by directly learning the nonlinear relative dynamics, without incurring the computational cost typically associated with classical nonlinear optimization-based control methods. These findings underscore the potential of RL techniques for scalable and autonomous management of satellite constellations.

Advancements in satellite technology have led to the development of large constellations in Low Earth Orbit, which presents new challenges in orbital management. Controlling and managing these large numbers of satellites efficiently becomes un-scalable due to the high computational and telemetry demands. This article addresses the problem of station-keeping, where each satellite in a constellation independently calculates correction maneuvers to compensate deviations from the nominal orbit caused by orbital perturbations. Using Reinforcement Learning, a decentralized satellite station-keeping policy is trained in a high-fidelity simulation environment, to output a low-thrust finite-pulse maneuver plan. The trained neural network policy requires minimal computational resources and can be readily deployed onboard resource-constrained space-grade computers. The proposed framework employs the model-free Soft Actor-Critic algorithm, which observes the relative orbital elements between the satellite’s current and ideal/desired trajectory and outputs a maneuver plan consisting of thrust direction, start time, and duration. Two policies are trained to account for both in-plane and out-of-plane tracking. To this end, a realistic and fuel-efficient mission scenario is designed, keeping orbit-plane errors within specified bounds. Furthermore, the performance of the proposed framework is compared with an optimal-control-based station-keeping approach. The efficacy and robustness of the proposed framework is demonstrated through a series of Monte-Carlo simulations and benchmarked against the traditional optimization-based approach, on a wide array of initial conditions.

In this article, we design an adaptive controller for the position and heading control for a payload-carrying spacecraft to perform docking with a target docking station. We address the problem by identifying the state constraints required to safely dock the spacecraft and imposing these constraints on an adaptive tracking controller. To make the controller adapt to different types of payloads, the adaptive controller is designed without any explicit a priori knowledge of the system dynamics or bound for the uncertainties. Furthermore, to accommodate a wide range of initial conditions, the constraints are chosen to be time-varying. Thus, unlike conventional controllers, the proposed controller enforces the safety of the spacecraft during docking by imposing state constraints while adapting to unknown drastic dynamic variations. The controller is validated in simulation for docking a 6 DoF spacecraft in the orbital space. Additionally, for technology readiness, we have performed the hardware validation of the controller using a payload-carrying planar floating robot and a prototype docking station. Compared to the state-of-the-art controllers, the proposed controller guarantees predefined time-varying state constraints while significantly improving the performance.

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.