Khaled A. Ghamry

Formation control of multiple quadrotors based on leader-follower method

This paper proposes a control algorithm to solve multiple quadrotor formation problem in a leader-follower configuration. A combination of sliding mode control (SMC) and linear quadratic regulator (LQR) are used for trajectory tracking control of a single robot. Position control, which acts as the outer loop is controlled by LQR for providing the reference attitude angles for the inner loop. SMC used for stabilizing and converging the error for the inner loop which is responsible for attitude angles. Another SMC algorithm is used for maintaining the leader-follower formation during flying, in both vertical and horizontal planes.

Real-time autonomous take-off, tracking and landing of UAV on a moving UGV platform

This paper presents a control strategy for take-off, tracking, and landing of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV) to be applied to missions of forest fires monitoring, detection, and fighting and other applications. A combination of sliding mode control (SMC) and linear quadratic regulator (LQR) is presented as the UAV local controller, while pure-pursuit strategy is applied as the UGV controller. Leader-follower formation controller approach is used during take-off, tracking and landing phases based on SMC. Experimental results are presented in order to demonstrate the performance of the team in different scenarios.

Cooperative forest monitoring and fire detection using a team of UAVs-UGVs

This paper investigates forest monitoring and fire detection strategies using a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). UAVs have great advantages in forest fire detection and fighting. However they have limited running time and payload capabilities, therefore UGVs are paired with UAVs to avoid their demerits. First, UGVs are used to transport the UAVs to the nearest location to their assigned search area. The UAVs will take-off and start the monitoring and search mission. When one of the UAVs detects the fire, it sends the fire spot coordinates to the leader UGV and ground fire management personnel. Then, the leader UGV that has a powerful computing and image processing capabilities will generate the reference trajectory for UAVs to follow in order to detect and continuously monitor the fire spread. Simulation results are presented in order to demonstrate the effectiveness of the proposed algorithm.

Multiple cooperative UAVs target tracking using Learning Based Model Predictive Control

In this paper, formation of a group of multiple cooperative unmanned aerial vehicles (UAVs) in a desired geometrical pattern while tracking an aerial target is implemented using decentralized Learning Based Model Predictive Control (LBMPC). The LBMPC is a new control technique that combines statistical learning along with control engineering providing guarantees on safety, robustness and convergence. The controller derived in this paper demonstrates the ability of the vehicles to cooperate, in a decentralized manner, to solve the formation problem in the presence of system uncertainties. The proposed controller respects the general formation constraints known as Reynolds rules of flocking during simulations. Our main contribution in this paper lays in the use of decentralized LBMPC in solving the problem of formation for a group of cooperative UAVs tracking an aerial target in the presence of unmodeled dynamics. A theoretical proof for stability will support our proposed controller.

Real-time fault-tolerant cooperative control of multiple UAVs-UGVs in the presence of actuator faults

This paper investigates fault-tolerant cooperative control (FTCC) strategy for a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. When actuator faults occur in one or more of the UGVs, two cases are considered: 1) the faulty UGV cannot complete its assigned task due to a severe fault occurrence, it has to get out from the formation mission. Then, FTCC strategy is designed to re-assign the mission to the remaining healthy vehicles; and 2) the faulty UGV can continue the mission with degraded performance, then the other team members will reconfigure their controllers considering the capability of faulty UGV. Thus, the FTCC strategy is designed to re-coordinate the motion of each UAV-UGV in the team. FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Real-time experiments are presented in order to demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.

Unmanned aerial vehicle based forest fire monitoring and detection using image processing technique

Early forest fire alarm systems are critical in making prompt response in the event of unexpected hazards. Cost-effective cameras, improvements in memory, and enhanced computation power have all enabled the design and real-time application of fire detecting algorithms using light and small-size embedded surveillance systems. This is vital in situations where the performance of traditional forest fire monitoring and detection techniques are unsatisfactory. This paper presents a forest fire monitoring and detection method with visual sensors onboard unmanned aerial vehicle (UAV). Both color and motion features of fire are adopted for the design of the studied forest fire detection strategies. This is for the purpose of improving fire detection performance, while reducing false alarm rates. Indoor experiments are conducted to demonstrate the effectiveness of the studied forest fire detection methodologies.

Cooperative control of multiple UAVs for forest fire monitoring and detection

This paper proposes to use multiple cooperative unmanned aerial vehicles (UAVs) for forest monitoring, fire detection and tracking of its propagation. The proposed algorithm solves the problems of forest fire by including three stages of search, confirmation and observation. During the search stage, the UAVs team moves in a certain formation shape in a leader-follower approach, a distributed sliding mode formation control is designed to keep the desired formation shape during this stage. Once a fire is detected, all sensory data will be sent to the ground station. A new reference trajectory is calculated according to the fire spread model for generating an elliptic fire perimeter. The team begins following the new fire trajectory, afterward the leader will send reconfiguration commands to followers. Therefore, a distributed reconfigurable controller is designed based on sliding mode control (SMC) which converts the formation problem from 2-D Cartesian frame of reference to the Polar frame of reference. This algorithm is used for evenly distributing and tracking UAVs team for elliptical fire perimeter. The effectiveness of the proposed algorithm is demonstrated using a six degree-of-freedom (DOF) quadrotor dynamic model and a simplified fire front model.

Real-Time Fault-Tolerant Cooperative Control of Multiple UAVs-UGVs in the Presence of Actuator Faults

This paper investigates fault-tolerant cooperative control (FTCC) strategy for a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. When actuator faults occur in one or more of the UGVs, two cases are considered: 1) the faulty UGV cannot complete its assigned task due to a severe fault occurrence, it has to get out from the formation mission. Then, FTCC strategy is designed to re-assign the mission to the remaining healthy vehicles; and 2) the faulty UGV can continue the mission with degraded performance, then the other team members will reconfigure their controllers considering the capability of faulty UGV. Thus, the FTCC strategy is designed to re-coordinate the motion of each UAV-UGV in the team. FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Simulation results and real-time experiments are presented in order to demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.

Fault-tolerant cooperative control of multiple UAVs for forest fire detection and tracking mission

A fault-tolerant cooperative control (FTCC) strategy for cooperative unmanned aerial vehicles (UAVs) used in forest monitoring, fire detection and tracking is investigated in this paper. The proposed algorithm solves the problem of monitoring and detection of forest fires, even when fault occurs to one or more UAVs. During the search stage, the UAVs team moves in a certain formation shape, a distributed sliding mode formation control is designed to keep the desired formation shape during this stage. Once a fire is detected, another distributed reconfigurable controller is designed based on sliding mode control (SMC) to evenly distribute UAVs team around the elliptical fire perimeter in fault-free case. When one or more UAVs cannot continue their mission due to a fault or leaving from formation for refueling/recharging, an FTCC strategy will be deployed to decrease the effects of the changed formation condition and the faulty/absent UAVs tasks will be reassigned to the remaining healthy/operable ones. Therefore, the new formation will be reconfigured and the UAVs still be evenly distributed around the fire spot for best coverage of the fire site. Simulation results are used to demonstrate the effectiveness of the proposed algorithm using six degree-of-freedom (DOF) quadrotor dynamic models of UAVs.

Multiple UAVs in forest fire fighting mission using particle swarm optimization

This paper investigates forest fires fighting application using team(s) of unmanned aerial vehicles (UAVs), in view of UAVs having great advantages in performing such tasks. However, important challenges in fire fighting missions in general are to perform the task with high performance in minimum time. In this paper, it is assumed that the fire spots are already detected and their coordinates will be sent to the fire fighting UAVs teams. Once the fire fighting team(s) receive relevant information, the team begins to solve the task assignment problem using the auction-based algorithm. The objective of the algorithm is to assign each UAV to each fire spot according to their relative distances, to minimize the distance traveled between each UAVs initial position and its assigned fire spot. Then, each UAV will optimally plan its path to its assigned fire spot by using particle swarm optimization (PSO) algorithm. The proposed algorithm calculates the optimal control inputs while taking into consideration the control inputs constraints while avoiding potential UAVs collisions during motion.