Geometric Fault-Tolerant Control of Quadrotors in Case of Rotor Failures: An Attitude Based Comparative Study

The ability of aerial robots to operate in the presence of failures is crucial in various applications that demand continuous operations, such as surveillance, monitoring, and inspection. In this paper, we propose a fault-tolerant control strategy for quadrotors that can adapt to single and dual complete rotor failures. Our approach augments a classic geometric tracking controller on $SO(3)\times\mathbb{R}^3$ to accommodate the effects of rotor failures. We provide an in-depth analysis of several attitude error metrics to identify the most appropriate design choice for fault-tolerant control strategies. To assess the effectiveness of these metrics, we evaluate trajectory tracking accuracies. Simulation results demonstrate the performance of the proposed approach.Comment: Accepted for publication in IROS 202

Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking

Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight regimes and operating conditions, including those producing highly nonlinear effects such as aerodynamic forces and torques, rotor interactions, or possible system configuration modifications. Classical approaches rely on handcrafted models and struggle to generalize and scale to capture these effects. In this paper, we present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions. In addition, physics constraints are embedded in the training process to facilitate the network's generalization capabilities to data outside the training distribution. Finally, we design a model predictive control approach that incorporates the learned dynamics for accurate closed-loop trajectory tracking fully exploiting the learned model predictions in a receding horizon fashion. Experimental results demonstrate that our approach accurately extracts the structure of the quadrotor's dynamics from data, capturing effects that would remain hidden to classical approaches. To the best of our knowledge, this is the first time physics-inspired deep learning is successfully applied to temporal convolutional networks and to the system identification task, while concurrently enabling predictive control.Comment: Video: https://youtu.be/dsOtKfuRjE

Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs

Human-robot interaction will play an essential role in various industries and daily tasks, enabling robots to effectively collaborate with humans and reduce their physical workload. Most of the existing approaches for physical human-robot interaction focus on collaboration between a human and a single ground robot. In recent years, very little progress has been made in this research area when considering aerial robots, which offer increased versatility and mobility compared to their grounded counterparts. This paper proposes a novel approach for safe human-robot collaborative transportation and manipulation of a cable-suspended payload with multiple aerial robots. We leverage the proposed method to enable smooth and intuitive interaction between the transported objects and a human worker while considering safety constraints during operations by exploiting the redundancy of the internal transportation system. The key elements of our system are (a) a distributed payload external wrench estimator that does not rely on any force sensor; (b) a 6D admittance controller for human-aerial-robot collaborative transportation and manipulation; (c) a safety-aware controller that exploits the internal system redundancy to guarantee the execution of additional tasks devoted to preserving the human or robot safety without affecting the payload trajectory tracking or quality of interaction. We validate the approach through extensive simulation and real-world experiments. These include as well the robot team assisting the human in transporting and manipulating a load or the human helping the robot team navigate the environment. To the best of our knowledge, this work is the first to create an interactive and safety-aware approach for quadrotor teams that physically collaborate with a human operator during transportation and manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape

Fast Object Tracking on a Many-Core Neural Network Chip

Fast object tracking on embedded devices is of great importance for applications such as autonomous driving, unmanned aerial vehicle, and intelligent monitoring. Whereas, most of previous general solutions failed to reach this goal due to the facts that (i) high computational complexity and heterogeneous operation steps in the tracking models and (ii) parallelism-limited and bloated hardware platforms (e.g., CPU/GPU). Although previously proposed devices leverage neural dynamics and near-data processing for efficient tracking, their flexibility is limited due to the tight integration with vision sensor and the effectiveness on various video datasets is yet to be fully demonstrated. On the other side, recently the many-core architecture with massive parallelism and optimized memory locality is being widely applied to improve the performance for flexibly executing neural networks. This motivates us to adapt and map an object tracking model based on attractor neural networks with continuous and smooth attractor dynamics onto neural network chips for fast tracking. In order to make the model hardware friendly, we add local-connection restriction. We analyze the tracking accuracy and observe that the model achieves comparable results on typical video datasets. Then, we design a many-core neural network architecture with several computation and transformation operations to support the model. Moreover, by discretizing the continuous dynamics to the corresponding discrete counterpart, designing a slicing scheme for efficient topology mapping, and introducing a constant-restricted scaling chain rule for data quantization, we build a complete mapping framework to implement the tracking model on the many-core architecture. We fabricate a many-core neural network chip to evaluate the real execution performance. Results show that a single chip is able to accommodate the whole tracking model, and a fast tracking speed of nearly 800 FPS (frames per second) can be achieved. This work enables high-speed object tracking on embedded devices which normally have limited resources and energy

Learning Model Predictive Control for Quadrotors

Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding--horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)xR^3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.Comment: Guanrui Li and Alex Tunchez contributed equally to this paper. This paper has been accepted to the 2022 IEEE International Conference on Robotics and Automation. Please cite this paper with the standard IEEE Conference format. Link to the Video: https://youtu.be/-5cIsIM5G7

RotorTM: A Flexible Simulator for Aerial Transportation and Manipulation

Low-cost autonomous Micro Aerial Vehicles (MAVs) have the potential to help humans by simplifying and speeding up complex tasks that require their interaction with the environment such as construction, package delivery, and search and rescue. These systems, composed of single or multiple vehicles, can be endowed with passive connection mechanisms such as rigid links or cables to perform transportation and manipulation tasks. However, they are inherently complex since they are often underactuated, and evolve on nonlinear manifold configuration spaces. In addition, the complexity of systems with cable-suspended load is further increased by the hybrid dynamics depending on the cables' varying tension conditions. In this paper, we present the first aerial transportation and manipulation simulator incorporating different payloads and passive connection mechanisms with full system dynamics as well as planning and control algorithms. Furthermore, it includes a novel model accounting for the transient hybrid dynamics for aerial systems with cable-suspended load to closely mimic real-world systems. The availability of a flexible and intuitive interface further contributes to its usability and versatility. Comparisons between simulations and real-world experiments with different vehicles' configurations show the fidelity of the simulator results with respect to real-world settings and its benefit for rapid prototyping and transitioning of aerial transportation and manipulation systems to real-world deployment

Association of Visceral Fat Area and Hyperuricemia in Non-Obese US Adults: A Cross-Sectional Study

This study aimed to investigate the relationship between visceral fat area (VFA) and hyperuricemia (HUA) among non-obese adults. We extracted data from 6224 US adults aged 20–59 years from the National Health and Nutrition Examination Survey (NHANES) from 2011–2018. The VFA was divided into four quartiles (Q1–Q4). We used multivariable logistic regression models to control for known confounders. A generalized additive model (GAM) and restricted cubic spines were used to examine the association between VFA and HUA stratified by sex, and a two-piecewise linear regression model was used to calculate the threshold effect among males. The results revealed that the prevalence of HUA was 11.8% (men 15.8%, women 7.2%). In the fully adjusted model, there was a positive association between VFA and HUA [as a quartile variable, Q4 vs. Q1, odds ratio (OR): 3.77 and 95% confidence interval (CI): (2.47~5.75), p < 0.001, p for trend < 0.001; as a continuous variable, per 10 cm2 increment, OR (95%CI):1.10(1.07,1.14), p < 0.001]. Besides, this positive association remained significantly stratified by sex. Interestingly, we observed a nonlinear dose-response relationship between VFA and HUA in males (inflection point: 107.46 cm2). In conclusion, our study confirmed a significant positive relationship between VFA and HUA among non-obese adults and remained statistically significant when stratified by sex