Cloud robotics has emerged as a promising technology for robotics applications due to its advantages of offloading computationally intensive tasks, facilitating data sharing, and enhancing robot coordination. However, integrating cloud computing with robotics remains a complex challenge due to network latency, security concerns, and the need for efficient resource management. In this work, we present a scalable and intuitive framework for testing cloud and edge robotic systems. The framework consists of two main components enabled by containerized technology: (a) a containerized cloud cluster and (b) the containerized robot simulation environment. The system incorporates two endpoints of a User Datagram Protocol (UDP) tunnel, enabling bidirectional communication between the cloud cluster container and the robot simulation environment, while simulating realistic network conditions. To achieve this, we consider the use case of cloud-assisted remote control for aerial robots, while utilizing Linux-based traffic control to introduce artificial delay and jitter, replicating variable network conditions encountered in practical cloud-robot deployments,

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.

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which an adaptive controller tracks to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs' motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV's nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets.

In this article, we present a control barrier function (CBF)-based control strategy for safe and precise landing of an unmanned aerial vehicle (UAV) on a moving target. The CBF is time-varying, as it depends on the velocity of the landing platform and captures three crucial safety constraints: (a) collision avoidance with the landing platform, (b) precise vertical descent on a narrow landing platform, and (c) ground clearance throughout the landing maneuver. The proposed CBF’s parameters can be adjusted to set the desired width and height of the descending cone. A quadratic programbased CBF safety filter is designed, which takes a nominal position tracking control input and yields a minimally invasive control input that enforces the safety constraints throughout the landing maneuver. The controller’s feasibility is analyzed and its performance is validated through multiple experiments using a quadrotor UAV and an unmanned ground vehicle.

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.