Mohamed A. Kamel

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.

Design of fault-tolerant cooperative control algorithm applied to WMRs against actuator faults

This paper investigates new fault-tolerant cooperative control (FTCC) strategies for multiple wheeled mobile robots (WMRs) in the presence of actuator faults. When actuator faults occur in one of the robots of the team, two cases are considered: 1) the faulty robot cannot complete its assigned task due to a severe fault occurrence, and it has to get out from the formation mission. As a result, the FTCC strategy is designed to re-assign the mission to the remaining healthy robots; and 2) the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the remaining capability of faulty robot. Thus, the FTCC strategy is developed to re-coordinate the motion of each robot in the team. A fault detection and diagnosis (FDD) scheme using a two-stage Kalman filter is presented. Simulation results are presented to demonstrate the performance of the team in different fault scenarios.

Decentralized leader-follower formation control with obstacle avoidance of multiple unicycle mobile robots

This work investigates the formation control and obstacle avoidance of multiple differentially driven wheeled mobile robots (WMRs) based on the kinematic model and the leader-follower approach. A combination of a linear model predictive control and input-output feedback linearization is implemented on a team of WMRs in order to accomplish a formation task. The linear model of each robot with nonlinear dynamics is found through feedback linearization, while model predictive control is applied to the linear model to perform the formation control. An obstacle avoidance algorithm also implemented to each robot in formation. The obstacle avoidance strategy is based on generating a virtual force that is considered to make corrections in the linear and angular velocities of each robot in formation. Simulation results are presented in order to demonstrate the performance of a team of WMRs with two formation mission 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.

Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots

This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.

Fault-tolerant control for cooperative unmanned aerial vehicles formation via fuzzy logic

In this paper, we investigate the problem of fault tolerant for a group of multiple autonomous cooperative unmanned aerial vehicles (UAVs) during execution their mission in a desired geometrical pattern. A decentralized linear model predictive control (LMPC) is designed and implemented on a team of cooperative UAVs to achieve the desired formation in the absence of faults (normal/fault-free cases). When faults occur in one or more of the UAVs, a fault-tolerant cooperative control (FTCC) strategy is designed via fuzzy logic such that each UAV in the team will take its own decision in a decentralized manner, to reconfigure the formation of the whole team ensuring the execution of the required mission. 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 fuzzy logic control by the cooperative UAVs in taking the decision to solve the problem of formation reconfiguration for an autonomous team of UAVs in the presences of faults.

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.

Task assignment/trajectory planning for unmanned vehicles via HFLC and PSO

This paper investigates the problems of task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization is proposed. Initially, teams of UAVs are moving in a pre-determined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the targets degree of threat, degree of importance, and the separating distance between each team and each detected target. First, the ground station assigns the teams to the targets based on the gathered information. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Then, each team plans its own path by formulating the path planning problem as an optimization problem, while the objective is to minimize the time to reach their destination considering the UAVs dynamic constraints and the collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve the optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

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.

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.