Nowadays Air Traffic Management (ATM) requires high accuracy performance to respect time constraints at specific waypoints and to sequence the traffic flow especially while climbing and landing, in order to increase reliability and safety. Current civil aviation guidance systems proposed real-time control laws to track the reference trajectories provided by the Flight Management System (FMS) while taking in consideration the overfly time constraints. With the improvement of technological devices, several types of flying vehicles are developed. They differ according to the tasks, ranges, flight conditions and performance parameters. However, these flying vehicles are nonlinear dynamic systems with variable parameters from control point of view and often under-actuated. Also, they could be requested for performing missions in unfavorable flight conditions. In this paper, an architecture to design trajectory generation and guidance functions of a Flight Management Computer (FMC) in accordance with an Embedded Flight Management System (E-FMS) requirements is defined. In order to generate a quasi-optimal trajectory as a desired reference, a composition of a path function and monotonically increasing motion function, based on B-spline functions, is proposed. A backstepping flight guidance control approach for an Unmanned Aerial Vehicle (UAV) type quadrotor is developed to achieve the generated trajectory with respect to the overfly time constrains at specific waypoints. Simulation results show the feasibility and the effectiveness of the proposed architecture.

In this paper, we are interested on the development of hybrid control architecture for autonomous mobile robots navigation. The proposed approach consists of an architecture adapted for partially known environments. It includes both reactive navigation methods based on the principle of Sense & Act and deliberative methods based on the principle of Sense-Plan & Act. The used reactive navigation method is a behavioural approach for navigation in unknown environments. Whereas deliberative approach is based on a polynomial method called Random-Profile-Approach (RPA) for optimal trajectory planning in known environments. Controllers used for both trajectories tracking and reactive navigation are fuzzy inference systems. Simulation and experimental results to validate the proposed navigation strategy are presented.

We propose an efficient stochastic scheme for minimum-time trajectory planning of a nonholonomic unicycle mobile robot under constraints on path curvature, velocities, and torques. This problem, which is known to be complex, often requires important runtimes, particularly if obstacles are present and if full dynamics is considered. The proposed technique is a fast variant of the random-profile approach recently applied to wheeled-mobile robots. It incorporates a trapezoidal-velocity-profile constraint that helps reduce the number of unknown parameters and that speeds up the calculation steps. Results are presented for two- and three-wheel mobile robots in free/constrained workspaces. A comparison with reference solutions, which were obtained independently, shows that the proposed variant is able to achieve almost the same quality of calculated trajectories while reducing the runtime considerably.

This paper presents a new method to generate suboptimal trajectories for serial manipulators in both configuration and Cartesian space. The method is based first on the dissociation of the search of optimal transfer time T from that of optimal trajectory profiles. Kinematic and dynamic constraints can then be treated in an easy manner because most of them can be transformed into bounds on the value of T. In addition, trajectory profiles are generated using trapezoidal velocity profiles that reduce the number of optimization parameters to two only. This makes the real-time trajectory generation possible and its implementation on existing industrial controllers quite easy. Furthermore, a stochastic optimization method is suggested to obtain a good approximation of the global optimal motion