Abstract

Conventional multirotor aerial vehicles actively suppress yaw rotation during hover, expending power to maintain a fixed heading despite the fact that yaw regulation is not required for force balance or altitude control. This paper challenges that paradigm by proposing a spinning quadrotor architecture that intentionally operates at a sustained yaw rate, converting power traditionally spent on yaw regulation into useful aerodynamic effects. A dynamic model of the spinning quadrotor is developed, analysis for low Re range is conducted to choose an airfoil for lifting surfaces. Preliminary hardware tests show a 22% reduction in thrust required. These findings suggest that intentional yaw rotation, rather than being suppressed, can be exploited as a design mechanism for efficient and robust multirotor flight.

Abstract

In this work, we conduct performance analysis of a Fault Tolerant Control (FTC) scheme for hexacopter actuator failure through hardware-based fault detection and dynamic control reallocation. Hall effect sensors are used to monitor motor speeds and compared with the reference signals generated by the flight controller (FC) for anomaly detection. Experimental flight tests demonstrate the efficacy of the proposed FTC in detecting the faults and subsequent flight stabilization. The performance of the proposed FTC (quantified in terms of tracking error in attitude dynamics) is compared with the ideal fault detection (no delay between fault occurrence and control reallocation) case.

Abstract

Brachiation, inspired by ape locomotion, involves swinging from one substrate to another. Existing approaches
typically rely on simple grippers and computationally expensive optimal control to compute feasible states and control
trajectories. In contrast, learning-based methods often lack physical modeling and require extensive training data. We
present a brachiating system using high degree-of-freedom anthropomorphic hands to generate swing trajectories and
perform stable grasps in a physics-based simulation environment (MuJoCo). An optimal open-loop trajectory is first
generated via trajectory optimization based on a desired grasp location. A tracking controller follows this reference, while a
grasping controller activates upon proximity to ensure secure contact. To reduce computational cost, we train a Generative
Adversarial Imitation Learning (GAIL) policy using expert trajectories from the optimization framework. The GAIL-based
controller generalizes to perturbed conditions and eliminates the need for repeated re-optimization, significantly lowering
computation time. It also adapts to varying initial configurations, removing the requirement to rerun optimization for each
case. We compare the learned model with a traditional optimal controller and demonstrate marked improvements in both
computational efficiency and versatility.

Abstract

This article presents a metacognitive decision-making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting and confirming stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of "which-sensor-to-use," "when-to-switch-sensor," and "how-to-search." Based on the information gathered by sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching, where the lower levels of search aim to localize a target (detection) and the highest level of a search exploit a target (confirmation). The performance of the MDM framework with two sensors having a low accuracy for detection and increased accuracy to confirm targets is evaluated through Monte Carlo simulations and compared with six decentralized multi-UAV search algorithms (three self-cognitive searches and three self and social-cognitive-based searches). The results indicate that the MDM framework can efficiently detect and confirm targets in an unknown environment.

Abstract

This article proposes the multiswarm cooperative information-driven search and divide and conquer mitigation control (MSCIDC) approach for faster detection and mitigation of forest fires by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of unmanned aerial vehicles (UAVs) flying together to search and quench the fire areas effectively. The multiswarm cooperative information-driven search uses a two-stage search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire locations. The search level is selected based on the thermal sensor information about the potential fire area. The dynamic nature of swarms acquired from global regulative repulsion and merging between swarms reduces the detection and mitigation time compared to the existing methods. The local attraction among the swarm members helps the nondetector members reach the fire location faster, and divide-and-conquer mitigation control ensures a nonoverlapping fire sector allocation for all members quenching the fire. The performance of the MSCIDC has been compared with different multi- UAV methods using a simulated pine forest environment. The Monte-Carlo simulation results indicate that the MSCIDC reduces the average forest area burnt by 65% and mission time by 60% compared to the best case of the multi- UAV approaches, guaranteeing a faster and more successful mission.

Abstract

Developing a reinforcement learning (RL) framework that works satisfactorily in deterministic and stochastic environments is challenging. To address this problem, a twin agent RL framework is proposed in this work, wherein we amalgamate both stochastic and deterministic agents' actions in a multiagent framework that works with a feedback mechanism that actively monitors the output. The proposed algorithm uses twin actor networks of different agents, corresponding to deterministic and stochastic agents, and an action selection critic network is used to choose the best action from both agents. Here, the algorithm blends the outcomes of two reinforcement learning (RL) agents, a stochastic agent and a deterministic agent, namely, Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3), respectively. We assess the effectiveness of the proposed algorithm by applying it to two case studies: (i) monoclonal antibody (mAb) production and (ii) production of propylene glycol (PG). The studies are conducted in the presence of parametric uncertainties, measurement noise, and nominal conditions. It is observed that for case study 1, the root-mean-square error (RMSE) value for the proposed algorithm is reduced by 40.9% when compared with TD3 and 27.57% when compared with PPO for the simulations. Similarly, for case study 2, the RMSE for the proposed algorithm is reduced by 8.87% when compared with TD3 and 5.8% with PPO. Based on extensive simulations, it is found that the proposed twin agent algorithm has faster convergence and better set-point tracking when compared to the agents operated individually.

Abstract

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students' learning and the platform's usability.

Abstract

Advancements in Deep Reinforcement Learning (DRL) have shown significant promise for the development of self directed Unmanned Aerial Vehicles (UAVs). Our research focuses on examining the impact of time-varying noise such as that present in Inertial Measurement Unit (IMU) sensors on the efficacy of DRL-based waypoint navigation and obstacle avoidance in UAVs. Sensor noise affects localization and obstacle detection, and is assumed to follow a Gaussian probability distribution with an unknown non-zero time varying mean and variance. In this study, we consider environment with static obstacles where noise exhibits a time-varying bias, a characteristic commonly associated with IMU sensors. We evaluate the effectiveness of a DRL agent, trained using the Proximal Policy Optimization (PPO) technique, in the presence of such noise.

Abstract

This paper presents a simple method for stabilizing
the quadrotor dynamics under complete loss of one actuator using
two-stage optimal control. Detailed equilibrium analysis and
subsequent selection of operating point under actuator loss are
provided, incorporating constraints on the maximum available
thrust. A detailed simulation study using a high-fidelity nonlinear
model of the quadrotor is presented showing the stability and
performance of the closed-loop system under complete actuator
loss, in the presence of external disturbances.

Abstract

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. The encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder's domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively. Code, models, and dataset will be made available.