On rapid parallel tuning of controllers of a swarm of MAVs -- distribution strategies of the updated gains

In this paper, we present a reliable, scalable, time deterministic, model-free procedure to tune swarms of Micro Aerial Vehicles (MAVs) using basic sensory data. Two approaches to taking advantage of parallel tuning are presented. First, the tuning with averaging of the results on the basis of performance indices reported from the swarm with identical gains to decrease the negative effect of the noise in the measurements. Second, the tuning with parallel testing of varying set of gains across the swarm to reduce the tuning time. The presented methods were evaluated both in simulation and real-world experiments. The achieved results show the ability of the proposed approach to improve the results of the tuning while decreasing the tuning time, ensuring at the same time a reliable tuning mechanism.

Aerial Robots Persistent Monitoring and Target Detection: Deployment and Assessment in the Field

In this manuscript, we present a distributed algorithm for multi-robot persistent monitoring and target detection. In particular, we propose a novel solution that effectively integrates the Time-inverted Kuramoto model, three-dimensional Lissajous curves, and Model Predictive Control. We focus on the implementation of this algorithm on aerial robots, addressing the practical challenges involved in deploying our approach under real-world conditions. Our method ensures an effective and robust solution that maintains operational efficiency even in the presence of what we define as type I and type II failures. Type I failures refer to short-time disruptions, such as tracking errors and communication delays, while type II failures account for long-time disruptions, including malicious attacks, severe communication failures, and battery depletion. Our approach guarantees persistent monitoring and target detection despite these challenges. Furthermore, we validate our method with extensive field experiments involving up to eleven aerial robots, demonstrating the effectiveness, resilience, and scalability of our solution.

Swarming in the Wild: A Distributed Communication-less Lloyd-based Algorithm dealing with Uncertainties

In this work, we present a distributed algorithm for swarming in complex environments that operates with no communication, no a priori information about the environment, and using only onboard sensing and computation capabilities. We provide sufficient conditions to guarantee that each robot reaches its goal region in a finite time, avoiding collisions with obstacles and other robots without exceeding a desired maximum distance from a predefined set of neighbors (flocking constraint). In addition, we show how the proposed algorithm can deal with tracking errors and onboard sensing errors without violating safety and proximity constraints, still providing the conditions for having convergence towards the goal region. To validate the approach, we provide experiments in the field. We tested our algorithm in GNSS-denied environments i.e., a dense forest, where fully autonomous aerial robots swarmed safely to the desired destinations, by relying only on onboard sensors, i.e., without a communication network. This work marks the initial deployment of a fully distributed system where there is no communication between the robots, nor reliance on any global localization system, which at the same time it ensures safety and convergence towards the goal within such complex environments.

Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets

Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly computationally demanding. That reduces the prediction horizon length, leading to a decrease in solution quality. Besides, the fixed time-step length limits the utilization of the available UAV dynamics in the target neighborhood. In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length. A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the generation of feasible trajectories in the flat output space. Based on the presented evaluation results and experimentally validated deployment, the proposed method increases the solution quality by enabling planning for long flight segments but allowing tightly sampled maneuvering.

CurviTrack: Curvilinear Trajectory Tracking for High-speed Chase of a USV

Heterogeneous robot teams used in marine environments incur time-and-energy penalties when the marine vehicle has to halt the mission to allow the autonomous aerial vehicle to land for recharging. In this paper, we present a solution for this problem using a novel drag-aware model formulation which is coupled with MPC, and therefore, enables tracking and landing during high-speed curvilinear trajectories of an USV without any communication. Compared to the state-of-the-art, our approach yields 40% decrease in prediction errors, and provides a 3-fold increase in certainty of predictions. Consequently, this leads to a 30% improvement in tracking performance and 40% higher success in landing on a moving USV even during aggressive turns that are unfeasible for conventional marine missions. We test our approach in two different real-world scenarios with marine vessels of two different sizes and further solidify our results through statistical analysis in simulation to demonstrate the robustness of our method.

FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy

Robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV open-source simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments.

Towards Agile Swarming in Real World: Onboard Relative Localization with Fast Tracking of Active Blinking Markers

A novel onboard tracking approach enabling vision-based relative localization and communication using Active blinking Marker Tracking (AMT) is introduced in this article. Active blinking markers on multi-robot team members improve the robustness of relative localization for aerial vehicles in tightly coupled swarms during real-world deployments, while also serving as a resilient communication channel. Traditional tracking algorithms struggle to track fast moving blinking markers due to their intermittent appearance in the camera frames. AMT addresses this by using weighted polynomial regression to predict the future appearance of active blinking markers while accounting for uncertainty in the prediction. In outdoor experiments, the AMT approach outperformed state-of-the-art methods in tracking density, accuracy, and complexity. The experimental validation of this novel tracking approach for relative localization involved testing motion patterns motivated by our research on agile multi-robot deployment.

Model predictive control-based trajectory generation for agile landing of unmanned aerial vehicle on a moving boat

This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.

A Generalized Thrust Estimation and Control Approach for Multirotors Micro Aerial Vehicles

This paper addresses the problem of thrust estimation and control for the rotors of small-sized multirotors Uncrewed Aerial Vehicles (UAVs). Accurate control of the thrust generated by each rotor during flight is one of the main challenges for robust control of quadrotors. The most common approach is to approximate the mapping of rotor speed to thrust with a simple quadratic model. This model is known to fail under non-hovering flight conditions, introducing errors into the control pipeline. One of the approaches to modeling the aerodynamics around the propellers is the Blade Element Momentum Theory (BEMT). Here, we propose a novel BEMT-based closed-loop thrust estimator and control to eliminate the laborious calibration step of finding several aerodynamic coefficients. We aim to reuse known values as a baseline and fit the thrust estimate to values closest to the real ones with a simple test bench experiment, resulting in a single scaling value. A feedforward PID thrust control was implemented for each rotor, and the methods were validated by outdoor experiments with two multirotor UAV platforms: 250mm and 500mm. A statistical analysis of the results showed that the thrust estimation and control provided better robustness under aerodynamically varying flight conditions compared to the quadratic model.

Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.