Search

This paper presents an off-line design strategy of an intelligent 3D autopilot of Micro-UAV Quadrotor. It consists of hybridization between two fuzzy controllers for the x and y motions and four PID classical controllers for the attitude/altitude motions. Genetic algorithms are used to adapt and optimize the value of the six controllers' parameters to achieve the best performance and decrease the consumed energy. Also, in order to ensure the global optimum control parameters, genetic algorithm named Bi-GA is used to automatically configure the two GAs using for the tuning process. This design strategy can be used to different types of Quadrotor (with cross or X configuration). Initially, in order to get the controller parameters, simulation tests are made on a commercial Quadrotor named AR.Drone V2. Finally, these parameters values are tested in an experiment using the robot operating system. The results of these experimentations confirm the effectiveness of using genetic algorithms in the design of intelligent PID autopilot.

International audience; This paper presents the design of 3-Dof multi-link robot arm that is mounted on the multirotor. To be considered the dynamic characteristics of the manipulation platform, the decoupled dynamic models of the system are derived. The main advantage of the first joint is introduced for more robustness and stability during hovering. The PID controller will be implemented for position and attitude of multirotor control, whereas, a slidingmode controller will be designed for a manipulator robot, which is then compared with the sliding surface that has been integrated with the proportional-derivative (PD) controller. The performance of the proposed technique is demonstrated through a simulation using Simulink and Matlab environment.

Multi-robot systems are a major research topic in robotics. Designing, testing, and deploying aerial robots in the real world is a possibility due to recent technological advances. This book explores different aspects of cooperation in multiagent systems. It covers the team approach as well as deterministic decision-making. It also presents distributed receding horizon control, as well as conflict resolution, artificial potentials, and symbolic planning. The book also covers association with limited communications, as well as genetic algorithms and game theory reasoning. Multiagent decision-making and algorithms for optimal planning are also covered along with case studies. Key features: Provides a comprehensive introduction to multi-robot systems planning and task allocation Explores multi-robot aerial planning; flight planning; orienteering and coverage; and deployment, patrolling, and foraging Includes real-world case studies Treats different aspects of cooperation in multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable.