Adaptive path following control for miniature unmanned aerial vehicle confined to three-dimensional Dubins path: From take-off to landing

This study proposes a method for resolving the challenge of controlling miniature fixed-wing unmanned aerial vehicles (MAVs) along a predetermined three-dimensional (3D) Dubins path while using models with uncertainty and when experiencing external wind disturbances. We provide a multilayered structure that incorporates both guiding and control at the same time. In the guidance layer, a modified vector-field-based approach is presented to enable the MAV to follow a 3D Dubins path, including the takeoff, cruise, and landing processes with three different types of route segments. Then, an adaptive sliding model controller is used for the analysis and management of both wind disturbances and system uncertainties. Finally, both simulated scenarios and in-flight trials demonstrate the applicability of the methodology and the efficiency of the proposed approach.

Coordinated Planar Path-Following Control for Multiple Nonholonomic Wheeled Mobile Robots

This article is concerned with both consensus and coordinated path-following control for multiple nonholonomic wheeled mobile robots. In the design, the path-following control is decoupled into the longitudinal control (speed control) and the lateral control (heading control) for the convenience of implementation. Different from coordinated trajectory tracking control schemes, the proposed control scheme removes the temporal constraint, which greatly improves the coordination robustness. In particular, two new coordinated error variables describing a chasing-and-waiting strategy are introduced in the proposed coordinated path-following control for injective paths and circular paths, respectively. All the closed-loop signals have proved to be asymptotically stable in the Lyapunov sense. Finally, simulation results under three typical paths are presented to verify the proposed coordination controllers.

Vector Field based Control of Quadrotor UAVs for Wildfire Boundary Monitoring

Accurate real-time information about an ongoing wildfire event is important for realizing effective and safe wildfire fighting. This paper is intended to solve the problem of guiding Unmanned Air Vehicles (UAVs) equipped with onboard cameras to monitor dynamic wildfire boundaries. According to whether the prior knowledge of the wildfire boundary is available or not, we propose a model-based vector field and a model-free vector field for UAV guidance. By describing the wildfire boundary with a zero level set function, the propagation of the wildfire boundary is modeled with the Hamilton-Jacobi equation. If the prior knowledge of the boundary is available, the typical radial basis function thin-plate spline is adopted to approximate the wildfire boundary and predicts its propagation. Then a 3D analytical vector field is constructed for an implicit function representing the wildfire boundary. If only partial observation of the wildfire boundary within the UAV’s field of view is available, the horizontal error between the UAV and its sensed segment of wildfire boundary and the vertical error between the UAV and the desired altitude are utilized to construct a 3D distance error based vector field, directly. To guide the UAV to converge to and patrol along the advancing wildfire boundary, the complex nonlinear dynamics of the UAV is exploited with differential flatness and incorporated with the above mentioned vector fields to design a nonlinear geometric controller. Computer simulations have been conducted to evaluate the performance of the proposed 3D vector field based controllers with both synthetic and real data, and simulation results demonstrate that the proposed algorithms can be effective methods to monitor the advancing wildfire boundaries.

Parallel Sensor-Space Lattice Planner for Real-Time Obstacle Avoidance

This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow a vector field, which encodes the desired vehicle path. We evaluated our method in challenging cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. In these experiments, we show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a speedup greater than 25.

Autonomous Navigation System for a Delivery Drone

The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presented a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, information of poses estimated by a marker (ArUco) detection technique using a camera, ultrawideband (UWB) devices, and the drone’s software estimation are merged by utilizing an extended Kalman filter algorithm to improve the landing precision. A vector field-based method controls the drone to follow the desired path smoothly, reducing vibrations or harsh movements that could harm the transported parcel. Real experiments validate the delivery strategy and allow the evaluation of the performance of the adopted techniques. Preliminary results state the viability of our proposal for autonomous drone delivery.

Time-varying formation control of multiple quad-rotors based on ellipsoid

Time-varying formation control problem for a group of multiple quad-rotors has been considered in this article with the help of ellipsoid. Firstly, an elliptic equation with time-varying parameters has been firstly introduced to describe the desired formation patterns for multiple quad-rotors in three-dimensional space. Then position controller and attitude controller are constructed using the method of sliding model control, respectively. Through tuning parameters of the elliptic equation, time-varying formation control of multiple quad-rotors has been realized using the controllers proposed in this article where smoothing transition between rigid formations has been guaranteed. Simulation results for formation control of quad-rotors that perform translation, scaling, and rotating actions have illustrated effectiveness of the time-varying formation controller that proposed in this article.

Safe coordination of robots in cyclic paths

This paper presents a MILP (mixed integer linear programming) based formulation for the coordination of multiple robots. We consider robots that must follow closed intersecting paths persistently. We propose an off-line planning of velocity profiles preventing the need of online collision avoidance maneuvers or path replanning. Our robot model considers minimum and maximum speed constraints, which allows our strategy to be applied to fixed-wing aerial robots. We also deal with three important other issues which are usually disregarded in the related literature: minimum spatial separation; acceleration limits; and uncertainties on the speeds and positions. Simulations with up to 48 robots show the efficiency of our strategy. A real experiment with 3 actual e-puck robots is presented in order to demonstrate the robustness of our formulation in a real world scenario.

Online planning low-cost paths for unmanned surface vehicles based on the artificial vector field and environmental heuristics

The study is concerned with the problem of online planning low-cost cooperative paths; those are energy-efficient, easy-to-execute, and low collision probability for unmanned surface vehicles (USVs) based on the artificial vector field and environmental heuristics. First, we propose an artificial vector field method by following the global optimally path and the current to maximize the known environmental information. Then, to improve the optimal rapidly exploring random tree (RRT*) based planner by the environment heuristics, a Gaussian sampling scheme is adopted to seek for the likely samples that locate near obstacles. Meanwhile, a multisampling strategy is proposed to choose low-cost path tree extensions locally. The vector field guidance, the Gaussian sampling scheme, and the multisampling strategy are used to improve the efficiency of RRT* to obtain a low-cost path for the virtual leader of USVs. To promote the accuracy of collision detection during the execution process of RRT*, an ellipse function-based bounding box for USVs is proposed with the consideration of the current. Finally, an information consensus scheme is employed to quickly calculate cooperative paths for a fleet of USVs guided by the virtual leader. Simulation results show that our online cooperative path planning method is performed well in the practical marine environment.

A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.

Navigation of Semi-autonomous Service Robots Using Local Information and Anytime Motion Planners

This paper deals with the problem of navigating semi-autonomous mobile robots without global localization systems in unknown environments. We propose a planning-based obstacle avoidance strategy that relies on local maps and a series of short-time coordinate frames. With this approach, simple odometry and range information are sufficient to make the robot to safely follow the user commands. Different from reactive obstacle avoidance strategies, the proposed approach chooses a good and smooth local path for the robot. The methodology is evaluated using a mobile service robot moving in an unknown corridor environment populated with obstacles and people.

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

Thermal Image Sensing Model for Robotic Planning and Search

This work presents a search planning system for a rolling robot to find a source of infra-red (IR) radiation at an unknown location. Heat emissions are observed by a low-cost home-made IR passive visual sensor. The sensor capability for detection of radiation spectra was experimentally characterized. The sensor data were modeled by an exponential model to estimate the distance as a function of the IR image's intensity, and, a polynomial model to estimate temperature as a function of IR intensities. Both theoretical models are combined to deduce a subtle nonlinear exact solution via distance-temperature. A planning system obtains feed back from the IR camera (position, intensity, and temperature) to lead the robot to find the heat source. The planner is a system of nonlinear equations recursively solved by a Newton-based approach to estimate the IR-source in global coordinates. The planning system assists an autonomous navigation control in order to reach the goal and avoid collisions. Trigonometric partial differential equations were established to control the robot's course towards the heat emission. A sine function produces attractive accelerations toward the IR source. A cosine function produces repulsive accelerations against the obstacles observed by an RGB-D sensor. Simulations and real experiments of complex indoor are presented to illustrate the convenience and efficacy of the proposed approach.

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.