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.

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.

We introduce Adaptive Geometry-based Velocity Obstacle(AGVO), a novel approach for collision avoidance in Un manned Traffic Management(UTM) scenarios with heterogenous UAVs, namely fixed-wing and quadrotor UAVs. Our approach allows collision avoidance while maintaining a minimum safe distance from other vehicles using state information from UTM in the case of heterogeneous UAVs with different collision-avoidance algorithms. To enable collision avoidance for heterogeneous UAVs with different collision avoidance algorithms, we have modified the Velocity Obstacle algorithm to account for the relative speed of all the surrounding UAVs when searching for collision avoidance velocity. We evaluate our algorithm's effectiveness against other State-of-the-art online planners for non-reciprocal and heterogeneous collision avoidance in different mission scenarios with varying numbers of UAVs.

This paper presents a novel algorithm for a swarm of unmanned aerial vehicles to search for an unknown source. The proposed method is inspired by the well-known particle swarm optimization (PSO) algorithm and is called acceleration- based particle swarm optimization (APSO) to address the source- seeking problem with no a priori information. Unlike the conventional particle swarm optimization algorithm, where the particle velocity is updated based on the self-cognition and social- cognition information, here the update is performed on the particle acceleration. A theoretical analysis is provided, showing the stability and convergence of the proposed acceleration-based particle swarm optimization algorithm. Conditions on the parameters of the resulting third-order update equations are obtained using Jury's stability test. High-fidelity simulations performed in CoppeliaSim, show the improved performance of the proposed acceleration-based particle swarm optimization algorithm for searching an unknown source when compared with the state-of- the-art particle swarm-based source-seeking algorithms. From the obtained results, it is observed that the proposed method performs better than the existing methods under scenarios like different inter unmanned aerial vehicle communication network topologies, varying numbers of unmanned aerial vehicles in the swarm, different sizes of search regions, restricted source movement, and in the presence of measurements noise.

In this article, we propose a bilevel segmentation framework with metacognitive learning (BS-McL) to detect power lines with an RGB camera mounted on an unmanned aerial vehicle (UAV) platform. The proposed framework consists of two levels based on spectral and spatial techniques. In the first level, spectral classification is carried out using the McL method, which is an evolving online learning neural network architecture. Due to similarities in spectral intensities, few nonpower line pixels are grouped along with power line pixels. The nonpower line pixels are removed by spatial segmentation in the second level. The second level includes morphological operations such as geometric features (shape and density indices), which are applied to detect the power lines. The processing steps of BS-McL are illustrated using a synthetic image of size$9 \times 6$pixels. Also, two datasets consisting of 64 images with varying backgrounds, different locations, and dimensions of power lines are used to demonstrate the performance of the proposed BS-McL. The obtained results for BS-McL are compared with five commonly used methods. For both datasets, the efficiency of the BS-McL for power line extraction is better than for the methods used for comparison. Furthermore, the trained knowledge from our experimental set-up (Dataset 1: suburban scene) can be transferred to another dataset that is available publicly (Dataset 2: urban and mountain scenes) if the power line spectral values are in relevance with the distribution in the training dataset. The proposed approach BS-McL is based on online learning with a self-adaptive architecture, which provides improved generalization ability.

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input–multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum$v-$gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

In this paper, we propose a mission aware motion planning (MAP) framework for a swarm of autonomous unmanned ground vehicles (UGVs) or mobile stations in an uncertain environment for efficient supply of resources/services to unmanned aerial vehicles (UAVs) performing a specific mission. The MAP framework consists of two levels, namely, centralized mission planning and decentralized motion planning. On the first level, the centralized mission planning algorithm estimates the density of UAV in a given environment for determining the number of UGVs and their initial operating location. In the subsequent level, a decentralized motion planning algorithm which provides a closed-form expression for velocity command using adaptive density estimation has been proposed. Further, the physical and geographical constraints are integrated into motion planning. A Monte-Carlo simulation is performed to evaluate the advantages of the MAP over distributed stationary stations (DSSs) often used in the literature. The obtained results clearly indicate that in comparison with DSS, MAP reduces the average distance traveled by UAVs about 20%, reduces the loss of mission time by 90 s per interruption and power loss by 3 dB.

In this work, a simple evolving neuro-fuzzy system with less learning parameters is utilized to develop an intelligent controller namely Reduced Parsimonious Controller (RedPAC). The proposed RedPAC is a simplified version of one of the recently developed intelligent controller called Parsimonious Controller (PAC). In RedPAC, the network parameters are reduced into two steps. Firstly, unlike the conventional fuzzy logic or neuro-fuzzy-based intelligent controller, it has no premise parameters. Secondly, in contrast with PAC, the number of consequent parameters have further reduced to one parameter per rule in RedPAC. The sliding mode control (SMC) technique is utilized to adapt consequent parameters of RedPAC, where the SMC-based auxiliary robustifying control term has guaranteed the uniform asymptotic convergence of tracking error to zero. The proposed controller's performance has been evaluated by implementing it to control a quadcopter unmanned aerial vehicle (UAV) simulator namely Dronekit. In addition, trajectory tracking performance of the quadcopter is compared with three different benchmark controllers namely a linear PID, a nonlinear SMC, and an intelligent controller called PAC. RedPAC outperforms PID and SMC techniques. The results of tracking trajectories are also comparable to PAC; however, RedPAC needs comparatively less learning parameters to obtain a similar or better tracking accuracy.

This paper presents the design of a robust evolving neuro-fuzzy output feedback control of a novel tilt-rotor vertical takeoff and landing (VTOL) aircraft. Detailed six degrees of freedom nonlinear model of the VTOL is presented. The asymmetry in the longitudinal centre of gravity position and the motor-propellers with diverse thrust and torque characteristics poses unique challenges for the control. The evolving neuro-fuzzy controller described here is called Parsimonious Autonomous Controller (PAC). PAC is based on model free design and is robust against system uncertainties and input disturbances. The effectiveness of the proposed controller is demonstrated through high fidelity numerical simulations for attitude and altitude control of the VTOL.

This paper presents an Oxyrrhis Marina-inspired search and dynamic formation control (OMS-DFC) framework for multi-unmanned aerial vehicle (UAV) systems to efficiently search and neutralize a dynamic target (forest fire) in an unknown/uncertain environment. The OMS-DFC framework consists of two stages, viz., the target identification stage without communication between UAVs and the mitigation stage with restricted communication. In the first stage, each UAV adapts proposed OMS with three levels to select between Levy flight, Brownian search, and directionally driven Brownian (DDB) search for accurate target identification (“fire location”). The selection of each level is based on the available sensor information about the possible fire location. In the second stage, the UAVs that identified a fire location fly in a dynamic formation to quench the fire using water. The proposed formation is achieved through decentralized control, where a UAV computes the control action based on the fire profile and also the angular position and angular separation with its succeeding neighbor. The proposed formation control law guarantees asymptotic convergence to the desired time-varying angular position profile of UAVs based on the nature of fire spread (circular/elliptical). To evaluate the performance of the proposed OMS-DFC for the multi-UAV system, a search and fire quenching mission in a typical pine forest is simulated. A Monte Carlo simulation study is conducted to evaluate the average performance of the proposed OMS-DFC-based multi-UAV mission, and the results clearly highlight the advantages of the proposed OMS-DFC in forest firefighting.

The problem of estimating the position, velocity, and acceleration of an agile target from imperfect position measurements is addressed in this paper. A discrete sliding mode observer (DSMO) is used that can handle measurement inaccuracies apart from unmeasured disturbance inputs. The target acceleration input acts as an unmeasured disturbance input to the observer. The parameters of DSMO are derived considering the worst case measurement error and unmeasured disturbance inputs. The selected parameters guarantee the convergence of the error dynamics into the boundary layer and finite state estimation error within the boundary layer. A numerical simulation study is presented for the state estimation of a sinusoidally maneuvering target. Experimental results are presented for the state estimation of a target unmanned ground vehicle (UGV), with position information obtained from a camera mounted on an unmanned air vehicle (UAV).