Vehicles capable of operating in more than one environment have been developed to solve real problems. Among them, the hybrid unmanned aerial-underwater vehicle (HUAUV) is receiving attention from the robotics community, mainly with a quadrotor-like configuration. However, this vehicle presents high energy consumption because of the larger mass required compared to the only aerial vehicle, limiting its autonomy. This work addresses the trajectory planning problem for a HUAUV. The method is based on Rapidly-exploring Random Trees (RRTs), a highly customizable planning technique. In addition, we propose two new heuristics to increase the energy efficiency of the hybrid vehicle. The first consists of biasing the tree expansion towards the environment with the lowest navigation cost, while the second one assigns estimated costs to nodes in the tree and chooses the least expensive trajectories. These techniques are evaluated in physically realistic simulation experiments performed in 135 scenarios. A comparative analysis of their performances is presented relative to the state of the art. We show that using efficient heuristics can significantly contribute to reducing energy consumption and even increase the average velocity in the missions performed by these vehicles.

This article introduces the minimal exposure escape path (MEEP) problem, in which a mobile robot must determine a path from a start position to a safe region that less exposes it to any sort of industrial environmental risks. It is a generalization of the classic minimal exposure path (MEP) problem, where the exposure is associated with the detection by an industrial wireless sensor network (IWSN). The MEEP problem is applicable in many situations when it is necessary to escape from or penetrate predefined zones avoiding threats along the way, a common task in military operations, or search and rescue missions. We model it as an optimal control problem and use a semi-Lagrangian numerical scheme to solve it. We show that our approach asymptotically converges to the optimal solution while still allowing us to incorporate different characteristics and typical real-world constraints. Simulated experiments show the applicability of our method in 2-D and 3-D spaces, considering obstacle-free or cluttered environments, with nonconvex and multiple goal regions, and heterogeneous networks.

In the last few years, researchers have applied ML strategies in the context of cooperative transportation to increase safety and efficiency. The RL paradigm has been successfully employed in the longitudinal spacing control of CACC systems, but ensuring disturbance rejection remains a challenge. Characteristics such as uncertain parameters in the formation and external interference may imposed constant errors, preventing the agents from reaching the desired formation when traveling at cruising speed. Therefore, this paper proposes a new training model for platooning, such that the acceleration command of each agent provides a stable formation, according to the nonlinear control formalism. The design methodology is general enough to be applied across multiple topologies. Also, the outputs of the controllers design by ML have a term with integral action capabilities, enabling corrective actions to withstand uncertainties that are constant. The effectiveness of our proposal has been illustrated with experiments using different network topologies, uncertain parameters, and external forces. Comparative analyses were conducted against the state-of-the-art, whose findings offer new insights concerning the generalization and robustness of using () in the control of autonomous platoons.

Autonomous platoons are good alternatives for cargo transportation, with many approaches to safety and efficiency issues. In the last decades, much effort has been employed in this context, with solutions ranging from low-level control laws to learning strategies. Although some papers have concentrated on methods robust to disturbances or resilient to disruptions, most of the current literature assumes that the platoon starts and remains connected all the time, even when subjecting vehicles to limited communication ranges and time-delay. Therefore, in this paper, we address the problem of connectivity maintenance and stability of platoons under the complete disconnection of vehicles. The main goal is to increase the tolerance to agent exits, external elements (such as traffic lights and human-driven vehicles), and non-ideal initial conditions, improving traffic safety in mixed traffic scenarios. By modeling the network connection as a Directed Acyclic Graph, we use a state-machine policy to recover connectivity and regulate the spacing distance to other vehicles under heterogeneous time-delays. We demonstrate that this state alteration can be interpreted as a reference tracking problem and propose a design procedure that provides stability and zero spacing error in steady-state. Our control protocol allows vehicles to reach a consensus with the team, even when they start disconnected from other ones. Results with agent-based and nonlinear simulations illustrate the effectiveness of our approach.

A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, theEKF-LOAM, which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, theEKF-LOAMuses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that theEKF-LOAMmethod reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm.Note to Practitioners—This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.

Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully applied to solve a wide range of complex decision-making tasks for many types of vehicles. Based on this context, in this paper, we propose the use of Deep-RL to perform autonomous mapless navigation for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs), robots that can operate in both, air or water media. We developed two approaches, one deterministic and the other stochastic. Our system uses the relative localization of the vehicle and simple sparse range data to train the network. We compared our approaches with an adapted version of the BUG2 algorithm for mapless navigation of aerial vehicles. Based on experimental results, we can conclude that Deep-RL-based approaches can be successfully used to perform mapless navigation and obstacle avoidance for HUAUVs. Our vehicle accomplished the navigation in two scenarios, being capable to achieve the desired target through both environments, and even outperforming the behavior-based algorithm on the obstacle-avoidance capability.

In this paper, we address the problem of finding cost-efficient three-dimensional paths that satisfy the maximum allowed curvature and the pitch angle of the vehicle. For any given initial and final configurations, the problem is decoupled into finding the horizontal and vertical parts of the path separately. Although the individual paths are modeled as two-dimensional Dubins curves using closed-form solutions, the final 3D path is constructed using the proposed local optimization to find a cost-efficient solution. Moreover, based on the decoupled approach, we provide a lower bound estimation of the optimal path that enables us to determine the quality of the found heuristic solution. The proposed solution has been evaluated using existing benchmark instances and compared with state-of-the-art approaches. Based on the reported results and lower bounds, the proposed approach provides paths close to the optimal solution while the computational requirements are in hundreds of microseconds. Besides, the proposed method provides paths with fewer turns than others, which make them easier to be followed by the vehicle's controller.

The problem of decentralized control of vehicular platoon is addressed, subject to non-uniform time-varying delays in the communication links among the vehicles. Our control goal is to drive the vehicles to reach a specified line formation, while moving forward according to the platoon leader speed. The control law in each vehicle is computed only by local relative information regarding its neighboring vehicles. The first result shows that communication delays cause steady-state formation error, whose value can be analytically calculated from the system data, and may lead to instability, if the delays values surpass a threshold. Hence, in the light of this finding, we present a decentralized control law to compensate for the delay effect, providing zero steady-state formation error. The paper is concluded by showing simulated experiments over six different communication topologies illustrating the applicability of our proposal.

In this paper, a study on nonlinear state estimation methods for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) is presented. Based on a detailed dynamic model simulation, we analyse and elect the best nonlinear algorithm among those presented in the state-of-the-art literature addressing local derivative-free nonlinear Kalman Filters (KFs): the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF) and the Transformed Unscented Kalman Filter (TUKF). Here, these three nonlinear probabilistic estimators were compared in terms of the Root Mean Square Error (RMSE) and the average execution time over Monte Carlo simulations. We simulated real-world conditions for our in-production HUAUV prototype using Inertial Measurement Unit (IMU) data and state augmentation for sensor data filtering and trajectory estimation. We have concluded that the CKF proved to be the most interesting KF to low-cost on-board applications for high dimensional state spaces.

This paper addresses a method for stabilizing the attitude of a quadrotor-like Unmanned Aerial Vehicle (UAV). Towards this end, a new fuzzy control technique based on Linear Matrix Inequalities (LMIs) is proposed, relying on a simple Takagi-Sugeno (TS) fuzzy model when compared to others found in the Literature. This strategy guarantees prescribed performance criteria, such as exponential decay rate, allowing stability under aggressive disturbances. The designed local gains are combined through membership functions of the model and are used to regulate the attitude of the vehicle on the hovering operating point, an important flight mode for this kind of robot. Numerical analysis reveal how the transient response can be tuned and tests with disturbances show the stability of the vehicle starting from different initial conditions, illustrating the merits of the proposed approach.

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 ℋ2performance. Gain matrices Kairand Kwatare 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.

Path planning is a fundamental task for any kind of autonomous mobile robot. In this paper, we present a motion planning technique on the three-dimensional space considering vehicles with spatial curvature and pitch (climb or dive) angle constraints. Concerning real fixed-wing Unmanned Aerial Vehicles or underwater Remotely Operated Vehicles, we face the problem of calculating paths with continuous acceleration profiles, preventing the robot from abrupt turning or climbing/diving rates during its navigation. We also focus on the generation of paths with reduced length, allowing energy savings and minimizing the time for the task fulfillments. The proposed methodology provides, in a fast and iterative way, near-optimal paths in some scenarios, but with the advantage of continuous curvature and pitch rate profiles, a fundamental issue regarding the dynamics of real-world robots. The methodology was validated through numerous trials, under different scenarios in a simulated environment, and the results compared to the ones obtained by state-of-the-art techniques, providing a thorough evaluation.

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.

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.

A Wireless Sensor Network consists of several sensor nodes deployed in an environment having as primary goal to collect data. However, due to limited sensor communication range, oftentimes it is necessary to use a mobile node that will visit other nodes to gather up their collected data. This work addresses the problem of planning efficient paths for data collection by a mobile node modeled as a nonholonomic vehicle with curvature constraints. We propose an efficient algorithm to identify areas of intersection among the nodes RF footprints which will guide the identification of a smaller set of waypoints through which the vehicle needs to traverse in order to collect available data. Then, a metric similar to the classical Traveling Salesman Problem is used to determine the best circuit that includes all these collecting points. In order to reduce the total path length for the mobile node, a meta-heuristic is used. The classical Dubins' path technique is employed to generate a feasible tour for the vehicle and a new heuristic is used to generate the required orientation at the collecting points. The methodology was validated in a simulated environment. Our methodology outperforms the classical Alternating Algorithm and the best performing state-of-the-art algorithm.

In this work we propose an efficient and simple three-stage evolutionary algorithm to tackle the difficult problem of planning shorter paths through regions of an environment which are feasible for a nonholonomic vehicle with curvature constraints (e.g. Dubins' vehicle). Our method is able to efficiently solve both the combinatorial and the continuous steps of the problem in a combined manner. In the first phase, the method varies the position of the waypoints within the boundaries of each region, it then optimizes the path orientation at each waypoint, and finally it chooses the best actual sequence of visit. Numerous trials, under different scenarios in a simulated environment, were executed providing a thorough evaluation and validation of the methodology. The results show that a substantial improvement was obtained on the search for optimal paths in the DTSPN over current works in the literature. Numerical simulations also exhibit a significant performance improvement when compared with classical solutions that use the Alternating Algorithm, and they also show that our method outperforms a random sampling based technique. Our results present a reduction on the final path length of about 25% on average when compared to paths generated by the aforementioned methods.

In this paper we deal with the problem of path length optimization for nonholonomic robots modeled as Dubins' vehicles. We present an improved solution that computes paths through two-dimensional waypoints, that are shorter than those produced by one of the most used techniques in the literature. We initially present an optimization cost function that proves to be better suited for the nonholonomic constraints of the vehicles that are the focus of this work. We also propose an improvement for the Alternating Algorithm, used to determinate the orientation angles on the calculation of the Dubins' Path through the waypoints. Finally, we use a recently developed optimization meta-heuristic, called Continuous-GRASP (C-GRASP), to generate a path that is shorter than paths obtained with classical techniques. Our results show significant improvements on the search for optimal paths for the case of nonholonomic vehicles. Our methodology was thoroughly evaluated and validated in simulation, and the results have shown a decrease on path's length of 28% on average, compared with the classic technique found in literature. In some cases a reduction of approximately 50% was obtained.

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.

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.

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.

In this paper we present a methodology based on a variation of the spatial pythagorean hodograph curves to generate smooth feasible paths for autonomous vehicles in three-dimensional space under the restriction of limited climb angles. An fast iterative algorithm is used to calculate the curve. The generated path satisfy three main angular constraints given by the vehicle: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive). A path is considered feasible if these kinematic constraints are not violated. The smoothness vehicle's acceleration profile is indirectly guaranteed between two points. The proposed methodology is applicable to vehicles that move in three-dimensional environments, and that can be modeled by the constraints considered here. We show results for small aerial vehicle.