Armando Alves Neto

On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for a team of autonomous aerial vehicles with holonomic constraints in environments with obstacles. Our approach uses Pythagorean Hodograph (PH) curves to connect vertices of the tree, which makes it possible to generate paths for which the main kinematic constraints of the vehicle are not violated. These paths are converted into trajectories based on feasible speed profiles of the robot. The smoothness of the acceleration profile of the vehicle is indirectly guaranteed between two vertices of the RRT tree. The proposed algorithm provides fast convergence to the final trajectory. We still utilize the properties of the RRT to avoid collisions with static, environment bound obstacles and dynamic obstacles, such as other vehicles in the multi-vehicle planning scenario. We show results for a set of small unmanned aerial vehicles in environments with different configurations.

Feasible RRT-based path planning using seventh order Bézier curves

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous vehicles with holonomic constraints in environments with obstacles. Our approach is based on seventh order Bézier curves to connect vertexes of the tree, generating paths that do not violate the main kinematic constraints of the vehicle. The methodology also does not require complex kinematic and dynamic models of the vehicle. The smoothness of the acceleration profile of the entire path is directly guaranteed by controlling the curvature values at the extreme points of each Bézier that composes the tree. The proposed algorithm provides fast convergence to the final result with several other advantages, such as the reduction in the number of vertexes of the tree because the method enable connections between vertexes of the tree with unlimited range. In an environment with few obstacles, a very small quantity of vertexes (sometimes only two) is sufficient to take the robot between two points. The properties of the seventh order Bézier formulation are also used to avoid collisions with static obstacles in the environment.

Hybrid Unmanned Aerial Underwater Vehicle: Modeling and simulation

The complete modeling and simulation of an unmanned vehicle with combined aerial and underwater capabilities, called Hybrid Unmanned Aerial Underwater Vehicle (HUAUV), is presented in this paper. The best architecture for this kind of vehicle was evaluated based on the adaptation of typical platforms for aerial and underwater vehicles, to allow the navigation in both environments. The model selected was based on a quadrotor-like aerial platform, adapted to dive and move underwater. Kinematic and dynamic models are presented here, and the parameters for a small dimension prototype was estimated and simulated. Finally, controllers were used and validated in realistic simulation, including air and water navigation, and the environment transition problem. To the best of our knowledge, it is the first vehicle that is able to navigate in both environment without mechanical adaptation during the medium transitions.

Efficient target visiting path planning for multiple vehicles with bounded curvature

In this paper, we introduce the k-Dubins Traveling Salesman Problem with Neighborhoods (k-DTSPN), the problem of planning efficient paths among target regions for multiple robots with bounded curvature constraints (Dubins vehicles). This paper presents two approaches for the problem. Firstly, we present a heuristic that solves it in two steps, based on classical techniques found in the literature. Secondly, we employ a Memetic Algorithm to solve both combinatorial and continuous phases of the problem in a combined manner. We provide formal analysis about both proposed techniques, presenting upper bounds to the length of the longest tour. Numerous trials in simulated environments were executed, providing statistical examination of the final results.

Feasible UAV path planning using genetic algorithms and Bézier curves

With the growing in the use of UAVs (Unmanned Aerial Vehicles), it is necessary to develop techniques that allow the generation of feasible paths for these vehicles. These paths take into account the nonholonomic constraints intrinsic to UAVs, such as minimum curvature, minimum torsion and maximum climb (or dive) angle. Thus, this paper proposes the use of genetic algorithms to generate paths for these vehicles in the three-dimensional space, using Bezier curves with several advantages. We consider all these three constraints in order to generate a feasible path for a small fixed-wing aircraft with severe limitations. We show results for this vehicle.

On the generation of feasible paths for aerial robots with limited climb angle

This paper presents a methodology based on a variation of the Quintic Pythagorean Hodographs curves for generating smooth feasible paths for autonomous vehicles in three-dimensional space under the restriction of limited climb angles. A given path is considered feasible if the main kinematic constraints of the vehicle are not violated. The generated paths satisfy three main angular constraints: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive). The smoothness the vehicle acceleration profile is indirectly guaranteed between two consecutive points of the profile. The proposed methodology is applicable to vehicles that move in three-dimensional environments, and that can be modelled under the constraints assumed. We apply our methodology and show the results for a small autonomous aerial vehicle.

Feasible path planning for fixed-wing UAVs using seventh order Bézier curves

This study presents a novel methodology for generating smooth feasible paths for autonomous aerial vehicles in the three-dimensional space based on a variation of the Spatial Quintic Pythagorean Hodographs curves. Generated paths must satisfy three main constraints: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive) angle. A given path is considered to be feasible if the main kinematic constraints of the vehicle are not violated, which is accomplished in our approach by connecting different waypoints with seventh order Bézier curves. This also indirectly insures the smoothness of the vehicle’s acceleration profile between two consecutive points of the curve and of the entire path by controlling the curvature values at the extreme points of each composing Bézier curve segment. The computation of the Pythagorean Hodograph is cast as an optimization problem, for which we provide an algorithm with fast convergence to the final result. The proposed methodology is applicable to vehicles in three-dimensional environments, which can be modeled presuming the imposed constraints. Our methodology is validated in simulation with real parameters and simulated flight data of a small autonomous aerial vehicle.

Adaptive complementary filtering algorithm for mobile robot localization

As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot’s motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.

Attitude control for an Hybrid Unmanned Aerial Underwater Vehicle: A robust switched strategy with global stability

This paper presents a method for stabilizing the attitude of a Hybrid Unmanned Aerial Underwater Vehicle. Firstly, we present aerodynamic and hydrodynamic models for the angular motion of our robot, discussing effects like buoyancy force and added inertia. Next, we apply robust control techniques for both environment, aerial and underwater, based on linear uncertain models with only four vertices and well-defined stability criteria, such as D-stability and ℋ2 performance. Gain matrices Kair and Kwat are computed and the attitude of the vehicle at the hovering operation point for each environment is controlled, respectively. Finally, a procedure is proposed to check the global stability for the switching control case, when the robot changes from air to water (or vice-versa). Numerical simulations with disturbances and switching control are presented to show the stability at different initial conditions.

On the generation of feasible paths for aerial robots in environments with obstacles

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous aerial vehicles with holonomic constraints in environments with obstacles. Our approach uses Pythagorean Hodograph (PH) curves to connect vertices of the tree, which makes it possible to generate paths for which the main kinematic constraints of the vehicle are not violated. The smoothness of the acceleration profile of the vehicle is indirectly guaranteed between two vertices of the RRT tree. The proposed algorithm provides fast convergence to the final trajectory. We still utilize the properties of the RRT to avoid collisions with static obstacles of a environment. We show results for a small unmanned aerial vehicles in environments with different configurations.