Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal Control via Spectral Method Model

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations (PDEs) combined with boundary conditions described by ordinary differential equations (ODEs). The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a subspace spanned by orthogonal basis functions. Based on the reduced order model, nonlinear model predictive control (NMPC) is implemented online to realize shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed reduced modeling and optimal control paradigms are numerically verified against an accurate high-dimensional FDM-based model in different scenarios and the controller's superior performance is shown compared to an optimally tuned PID controller.

On the Existence of Static Equilibria of a Cable-Suspended Load with Non-stopping Flying Carriers

Aerial cooperative robotic manipulation of cable-suspended objects has been largely studied as it allows handling large and heavy objects, and cables offer multiple advantages, such as their low weight and cost efficiency. Multirotors have been typically considered, which, however, can be unsuitable for long-lasting manipulation tasks due to their scarce endurance. Hence, this work investigates whether non-stop flights are possible for maintaining constant the pose of cable-suspended objects. First, we show that one or two flying carriers alone cannot perform non-stop flights while maintaining a constant pose of the suspended object. Instead, we demonstrate that \emph{three} flying carriers can achieve this task provided that the orientation of the load at the equilibrium is such that the components of the cable forces that balance the external force (typically gravity) do not belong to the plane of the cable anchoring points on the load. Numerical tests are presented in support of the analytical results.

AERIAL-CORE: AI-Powered Aerial Robots for Inspection and Maintenance of Electrical Power Infrastructures

Large-scale infrastructures are prone to deterioration due to age, environmental influences, and heavy usage. Ensuring their safety through regular inspections and maintenance is crucial to prevent incidents that can significantly affect public safety and the environment. This is especially pertinent in the context of electrical power networks, which, while essential for energy provision, can also be sources of forest fires. Intelligent drones have the potential to revolutionize inspection and maintenance, eliminating the risks for human operators, increasing productivity, reducing inspection time, and improving data collection quality. However, most of the current methods and technologies in aerial robotics have been trialed primarily in indoor testbeds or outdoor settings under strictly controlled conditions, always within the line of sight of human operators. Additionally, these methods and technologies have typically been evaluated in isolation, lacking comprehensive integration. This paper introduces the first autonomous system that combines various innovative aerial robots. This system is designed for extended-range inspections beyond the visual line of sight, features aerial manipulators for maintenance tasks, and includes support mechanisms for human operators working at elevated heights. The paper further discusses the successful validation of this system on numerous electrical power lines, with aerial robots executing flights over 10 kilometers away from their ground control stations.

A Signal Temporal Logic Planner for Ergonomic Human-Robot Collaboration

This paper proposes a method for designing human-robot collaboration tasks and generating corresponding trajectories. The method uses high-level specifications, expressed as a Signal Temporal Logic (STL) formula, to automatically synthesize task assignments and trajectories. To illustrate the approach, we focus on a specific task: a multi-rotor aerial vehicle performing object handovers in a power line setting. The motion planner considers limitations, such as payload capacity and recharging constraints, while ensuring that the trajectories are feasible. Additionally, the method enables users to specify robot behaviors that take into account human comfort (e.g., ergonomics, preferences) while using high-level goals and constraints. The approach is validated through numerical analyzes in MATLAB and realistic Gazebo simulations using a mock-up scenario.

Ergonomic Collaboration between Humans and Robots: An Energy-Aware Signal Temporal Logic Perspective

This paper presents a method for designing energy-aware collaboration tasks between humans and robots, and generating corresponding trajectories to carry out those tasks. The method involves using high-level specifications expressed as Signal Temporal Logic (STL) specifications to automatically synthesize task assignments and trajectories. The focus is on a specific task where a Multi-Rotor Aerial Vehicle (MRAV) performs object handovers in a power line setting. The motion planner takes into account constraints such as payload capacity and refilling, while ensuring that the generated trajectories are feasible. The approach also allows users to specify robot behaviors that prioritize human comfort, including ergonomics and user preferences. The method is validated through numerical analyses in MATLAB and realistic Gazebo simulations in a mock-up scenario.

Modelling, Analysis and Control of OmniMorph:an Omnidirectional Morphing Multi-rotor UAV

We present the design, modelling, and control of a novel morphing multi-rotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the platform to switch between different configurations to achieve the required task. The uni-directional thrust (UDT) configuration can be used for energy-efficient navigation, while fully-actuated (FA) and omnidirectional (OD) configurations can be used for full pose tracking and make the platform assume any orientation while compensating the gravity. The platform is equipped with eight bi-directional propellers that are actively tilted in a synchronized fashion using only one additional degree of actuation.

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

Nonlinear MPC for full-pose manipulation of a cable-suspended load using multiple UAVs

Using multiple UAVs to manipulate the full posture of an object is a promising capability in many industrial applications, such as autonomous building construction and heavy-load transportation. Among various methods, manipulation via cables excels in mechanical simplicity and ease of use, but is challenging from a control perspective. Existing centralized control methods either neglect the dynamic coupling between UAVs and the load or resort to a cascade structure, which limits the operational speed and cannot guarantee safety. In this work, we propose a centralized control method that uses nonlinear model predictive control. This control method takes into account the full nonlinear model of the load-UAV system, as well as the constraints of UAV thrust, collision avoidance, and ensuring all cables are taut. By taking into account the above factors, the proposed control algorithm can fully exploit the performance of UAVs and facilitate the speed of operation. We demonstrate our algorithm through 6-DoF simulations to achieve fast and safe manipulation of the pose of a rigid-body payload using multiple UAVs.

A Dynamic Programming Framework for Optimal Planning of Redundant Robots Along Prescribed Paths With Kineto-Dynamic Constraints

Off-line optimal planning of trajectories for redundant robots along prescribed task space paths is usually broken down into two consecutive processes: first, the task space path is inverted to obtain a joint-space path, then, the latter is parametrized with a time law. If the two processes are separated, they cannot optimize the same objective function, ultimately providing sub-optimal results. In this paper, a unified approach is presented where dynamic programming is the underlying optimization technique. Its flexibility allows accommodating arbitrary constraints and objective functions, thus providing a generic framework for optimal planning of real systems. To demonstrate its applicability to a real world scenario, the framework is instantiated for time-optimality. Compared to numerical solvers, the proposed methodology provides visibility of the underlying resolution process, allowing for further analyses beyond the computation of the optimal trajectory. The effectiveness of the framework is demonstrated on a real 7-degrees-of-freedom serial chain. The issues associated with the execution of optimal trajectories on a real controller are also discussed and addressed. The experiments show that the proposed framework is able to effectively exploit kinematic redundancy to optimize the performance index defined at planning level and generate feasible trajectories that can be executed on real hardware with satisfactory results.

Coordinated Multi-Robot Trajectory Tracking over Sampled Communication

In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking {in a coordinated way} when the sampling time of communications is non-negligible, disrupting the theoretical convergence guarantees of standard control designs. Given a feasible desired trajectory in the configuration space, the proposed controller receives measurements from the system at sampled time instants and computes velocity references for the robots, which are tracked by a low-level controller. We propose a jointly designed feedback plus feedforward controller with provable stability and error convergence guarantees, and further show that the obtained controller is amenable of decentralized implementation. We test the proposed control strategy via numerical simulations in the scenario of cooperative aerial manipulation of a cable-suspended load using a realistic simulator (Fly-Crane). Finally, we compare our proposed decentralized controller with centralized approaches that adapt the feedback gain online through smart heuristics, and show that it achieves comparable performance.