Team Of Vehicles Research Articles

Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory

This paper addresses a wildlife monitoring problem using a team of unmanned aerial vehicles (UAVs) with the optimal transport theory. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work. In all different simulations, it is shown that the proposed scheme significantly outperforms other UAV-based wildlife monitoring strategies in terms of the target detection rate up to 3.6 times.

Scalable Vehicle Team Continuum Deformation Coordination With Eigen Decomposition

The continuum deformation leader–follower cooperative control strategy models vehicles in a multiagent system as particles of a deformable body. A desired continuum deformation is defined based on leaders’ trajectories and acquired by followers in real time through local communication. The existing continuum deformation theory requires followers to be placed inside the convex simplex defined by leaders. This constraint is relaxed in this article. We prove that, under suitable assumptions, any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n+1$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n=1,2,3$</tex-math></inline-formula> ) vehicles forming an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -D simplex can be selected as leaders while followers, arbitrarily positioned inside or outside the leading simplex, can acquire a desired continuum deformation in a decentralized fashion. The article’s second contribution is to assign a one-to-one mapping between leaders’ smooth trajectories and homogeneous deformation features obtained by continuum deformation eigendecomposition. Therefore, a safe and smooth continuum deformation coordination can be planned either by shaping homogeneous transformation features or by choosing appropriate leader trajectories. This is beneficial to efficiently plan and guarantee collision avoidance in a large-scale group. A simulation case study is reported in which a virtual convex simplex contains a quadcopter vehicle team at any time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> ; A* search is applied to optimize quadcopter team continuum deformation in an obstacle-laden environment.

Stochastic fault and cyber-attack detection and consensus control in multi-agent systems

ABSTRACT In this paper, the stochastic fault and cyber-attack detection and consensus control problems are investigated for multi-agent systems. By using a Markovian approach, Linear Matrix Inequalities (LMI) are derived that incorporate relative information among the agents to detect stochastic faults and cyber-attacks and then resiliently control the system to reach a consensus. A mixed coding and Message Authentication approach is presented to detect data injection cyber-attacks on the communication links. By using the Bayesian inference, useful information regarding the cyber-attack, such as the probability of its occurrence, is derived. Simulation and two case study results corresponding to a team of multi-agent Autonomous Underwater Vehicles (UAVs) confirm and verify the effectiveness and capabilities of our proposed methodologies.

Self-Triggered Based Coordinate Control With Low Communication for Tethered Multi-UAV Collaborative Transportation

In this letter, a self-triggered based coordinate control scheme with low communication requirements is investigated for a team of unmanned aerial vehicles (UAVs) collaboratively transporting a suspended payload. In most existing research on collaborative transportation, the limited communication ability of physical devices has rarely been studied. Hence, this letter suggests a self-triggered cooperative path-following coordinate control scheme for UAVs traveling along a given desired path and synchronously carrying out transportation mission. It is proven that the proposed coordinate control scheme can make communication discrete, thereby significantly reducing the amount of communication. The results of outdoor experiments verify the feasibility of the proposed strategy, and low-frequency communication is demonstrated.

Exploiting a fleet of UAVs for monitoring and data acquisition of a distributed sensor network

This study proposes an efficient data collection strategy exploiting a team of unmanned aerial vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors’ data collection collaboratively while considering existing constrains of the UAV payload and battery capacity. The proposed mission planner system employs the differential evolution optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding redundant collection of sensors’ data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.

Optimal trajectory planning for cinematography with multiple Unmanned Aerial Vehicles

This paper presents a method for planning optimal trajectories with a team of Unmanned Aerial Vehicles (UAVs) performing autonomous cinematography. The method is able to plan trajectories online and in a distributed manner, providing coordination between the UAVs. We propose a novel non-linear formulation for this challenging problem of computing multi-UAV optimal trajectories for cinematography; integrating UAVs dynamics and collision avoidance constraints, together with cinematographic aspects like smoothness, gimbal mechanical limits and mutual camera visibility. We integrate our method within a hardware and software architecture for UAV cinematography that was previously developed within the framework of the MultiDrone project; and demonstrate its use with different types of shots filming a moving target outdoors. We provide extensive experimental results both in simulation and field experiments. We analyze the performance of the method and prove that it is able to compute online smooth trajectories, reducing jerky movements and complying with cinematography constraints.

Aerial Multi-Camera Robotic Jib Crane

A formulation based on a team of unmanned aerial vehicles operating as a fully articulated multi-camera jib crane is proposed for the application of aerial cinematography. An optimization-based controller commands the formation to follow an artistic trajectory defined by the director of photography, while actively avoiding collisions and cameras' mutual visibility. The proposed scheme, based on the cluster-space formulation, presents an intuitive way of maneuvering the virtual camera fixture while automatically adjusting the motions by imposing artistic and safety constraints, facilitating the operator task.

Neural Adaptive Command Filtered Control for Cooperative Path Following of Multiple Underactuated Autonomous Underwater Vehicles Along One Path

The aim of this article is to develop a new cooperative path following control scheme for a team of autonomous underwater vehicles (AUVs) which track one curve with nonlinear uncertainties. Individual path-following controllers are designed to guarantee that each AUV meets the desired tracking performance. For the cooperative path following, a containment control approach is incorporated into the design, where each AUV is forced to evenly disperse on a path that is parameterized by a continuous variable over a communication network. The main features of the paper that unlike the existing designs are that: first, by employing the command filtered control technique, the assumption of second-order derivative of the reference path is removed. Second, a globally uniformly ultimately bounded (GUUB) path following control structure is proposed that enables two kinds of controllers to switch under different cases. Third, coordination between multiple AUVs is achieved through a containment design, and a path variable containment cooperative path following controller is derived that enables multiple AUVs to be evenly dispersed and guided by the virtual leaders. Finally, the stability analysis and simulation example are given to verify the proposed control strategy.

Navigating UAVs for Optimal Monitoring of Groups of Moving Pedestrians or Vehicles

This paper focuses on navigating a team of unmanned aerial vehicles (UAVs) equipped with cameras to monitor groups of ground pedestrians or vehicles that move along given paths with unkown, time-varying but bounded speeds. The objective is to deliver a high-quality surveillance of the pedestrians or vehicles. We formulate a surveillance problem which requires the UAVs to monitor the pedestrians or vehicles from as short as possible distances. We propose a navigation algorithm that enables each UAV to determine its movement locally with a minor participation of a central station. We prove that this algorithm is locally optimal. Simulations confirm its performance.

Multi-Unmanned-Aerial-Vehicle Wildfire Boundary Estimation Using a Semantic Segmentation Neural Network

This paper presents a system to command and control a team of fixed-wing unmanned aerial vehicles (UAVs) to sense dynamic wildfire boundaries. UAV team task and trajectory planning strategies enable the team to rapidly find, rally around, and map the wildfire boundaries. A novel boundary estimation algorithm generates two-dimensional concave polygonal estimates of multiple dynamic boundaries given sparse observation data. The algorithm was tested with simulated wildfire scenario binary fire or free-point observations collected by the UAV team. First, all gathered observations are used to classify groups of points into clusters belonging to individual wildfires; then, spatiotemporal information from wildfire observations is encoded as an image with observation age represented as pixel brightness. A neural network performs semantic segmentation on each image and outputs a predicted binary image of the wildfire. This image is decoded back into a point set that feeds into a boundary estimation algorithm (Polylidar) to extract a concave boundary. Benchmarks for planner and boundary estimation times and accuracy comparisons are provided. Our boundary estimation algorithm and supporting multiagent planning strategies were used to win the 2019 U.S. Air Force Research Laboratory’s Swarm and Search Wildfire challenge using the aerospace multiagent simulation environment.

Wildfire Front Monitoring with Multiple UAVs using Deep Q-Learning

Wildfires destroy thousands of hectares every summer all over the globe. To provide an effective response and to mitigate wildfires impact, firefighters require a real-time monitoring of the fire front. This paper proposes a cooperative reinforcement learning (RL) framework that allows a team of autonomous unmanned aerial vehicles (UAVs) to learn how to monitor a fire front. In the literature, independent Q-learners were proposed to solve a wildfire monitoring task with two UAVs. Here we propose a framework that can be easily extended to a larger number of UAVs. Our framework builds on two methods: multiple single trained Q-learning agents (MSTA) and value decomposition networks (VDN). MSTA trains a single UAV controller, which is then "copied" to each of the UAVs in the team. In contrast, VDN trains agents to learn how to cooperate. We benchmarked in simulations our two considered methods – MSTA and VDN – against two state-of-the-art approaches: independent Q-learners and a joint Q-learner. Simulation results show that our considered methods outperform state-of-the-art approaches in a wildfire front monitoring task with up to 9 fixed-wing and multi-copter UAVs.

Distributed circumnavigation control of autonomous underwater vehicles based on local information

This paper investigates the distributed circumnavigation problem by an autonomous underwater vehicle (AUV) team to enclose a target. When the AUVs do not have a global coordinate system or a common reference direction, moreover when some vehicles cannot detect the target, this circumnavigation problem becomes challenging. The above situation is common in underwater environments and is taken into account in this paper. Considering the kinematic and dynamic models of vehicles, this paper proposes a cascade-based distributed control law. The proposed control law via local relative position measurement can realize a desired circumnavigation formation around the target. Furthermore, we show that with a radius determinant mechanism, this control law enables the vehicles which do not detect the target to keep a specified pattern without exchanging the target position information. Finally, simulations are provided to illustrate the result.

Ocean front detection and tracking using a team of heterogeneous marine vehicles

AbstractOcean monitoring is an expensive and time consuming endeavor, but it can be made more efficient through the use of teams of autonomous robots. In this paper, we present a system for the autonomous identification and tracking of ocean fronts by coordinating the sampling efforts of a heterogeneous team of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). The primary contributions of this study are (1) our algorithm for performing autonomous coordination using general autonomy principles: Sequential Allocation Monte Carlo Tree Search (SA‐MCTS) which incorporates domain knowledge into the environmental estimation through both augmenting a standard Gaussian process with a nearest neighbors prior and planning in a drifting reference frame, (2) our decision support user interface to help human operators oversee the autonomous system, and (3) the demonstration of the system's operation in a 2‐week long deployment in the Gulf of Mexico using a heterogeneous team of four Slocum gliders and two robotic ocean surface samplers. With these contributions, we aim to bridge the gap between state of the art autonomy algorithms and marine vehicle planning methods that have been tested in large‐scale field trials. This paper presents the first deployment of a general, heuristic‐based, multi‐robot coordination algorithm for an extended sampling mission.

Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary.

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

Experimental Evaluation of a Team of Multiple Unmanned Aerial Vehicles for Cooperative Construction

This article presents a team of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative missions for autonomous construction. In particular, the UAVs have to build a wall made of bricks that need to be picked and transported from different locations. First, we propose a novel architecture for multi-robot systems operating in outdoor and unstructured environments, where robustness and reliability play a key role. Then, we describe the design of our aerial platforms and grasping mechanisms to pick, transport and place bricks. The system was particularly developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), where Challenge 2 consisted of building a wall cooperatively with multiple UAVs. However, our approach is more general and extensible to other multi-UAV applications involving physical interaction, like package delivery. We present not only our results in the final stage of MBZIRC, but also our simulations and field experiments throughout the previous months to the competition, where we tuned our system and assessed its performance.

Mobility modelling for urban traffic surveillance by a team of unmanned aerial vehicles

Use of unmanned aerial vehicles (UAVs) for road traffic surveillance is an exciting idea for improving surveillance quality, as a component of intelligent transportation systems and smart cities. Calibrated mobility models help study and analyse several mobility related issues, for their successful deployment in large urban environments. This paper discusses an energy-aware and scalable mobility model for a team of cooperative UAVs monitoring urban road traffic. It also accompanies an extensible framework for territory distribution, to optimise the number of UAVs required for maximising coverage, without compromising operational performance. Since the problem of territory distribution in general is NP-hard, a genetic algorithm-based strategy, considering edge-disjoint paths, is recommended. Simulation results show that the proposed model considerably improves area coverage in comparison with other mobility models. A published dataset simulating vehicular traffic for a mid-size urban city is used to ascertain scalability to realistic urban territories.

Mobility modelling for urban traffic surveillance by a team of unmanned aerial vehicles

Mobility modelling for urban traffic surveillance by a team of unmanned aerial vehicles

Cooperative Estimation to Reconstruct the Parametric Flow Field Using Multiple AUVs

This paper investigates the cooperative estimation problem to recover the parametric flow field through sensor measurements from an autonomous underwater vehicle (AUV) team. We establish the parametric flow model that incorporates the concept of incompressibility to provide a physical property. Then, considering the influence of unknown flow field on AUVs’ trajectories while submerged, we define: 1) the deviation between actual and predicted relative positions between each vehicle and its neighbors as an relative motion-integration error, which is available using local measurement; 2) the deviation between the actual and predicted position of each vehicle as an absolute motion-integration error, which is available from GPS when the AUVs are on the sea surface. Based on relative and absolute motion-integration errors, we formulate a set of nonlinear equations to present the deterministic and continuous relationship between the above errors and parametric flow model. To reconstruct the flow field, an iterative algorithm is proposed to estimate the model parameters by solving an inverse problem for these nonlinear equations. Moreover, a detailed convergence analysis of the proposed algorithm is given. Finally, simulations are conducted to illustrate the effectiveness of the proposed algorithm in a simulated and a real dataset of the flow field.

Control Protocols for Range-Based Navigation of a Networked Group of Underwater Vehicles

This paper tackles the problem of formation reconstruction for a team of vehicles based on the knowledge of the range between agents of a subset of the participants. One main peculiarity of the proposed approach is that the relative velocity between agents, which is a fundamental data to solve the problem, is not assumed to be known in advance neither directly communicated. For the purpose of estimating this quantity, a collaborative control protocol is designed in order to mount the velocity data in the motion of each vehicle as a parameter through a dedicated control protocol, so that it can be inferred from the motion of the neighbor agents. Moreover, some suitable geometrical constraints related to the agents' relative positions are built and explicitly taken into account in the estimation framework providing a more accurate estimate. The issue of the presence of delays in the transmitted signals is also studied and two possible solutions are provided explaining how it is possible to get a reasonable range data exchange to get the solution both in a centralized fashion and in a decentralized one. Numerical examples are presented corroborating the validity of the proposed approach.

Planar Manipulation of an Object by Unmanned Aerial Vehicles Using Sliding Modes

Enabling a team of unmanned aerial vehicles (UAVs) to cooperate and perform tasks collaboratively is a challenging problem. In this paper, sliding-mode-based collaborative maneuvering strategies are developed for a pair of UAVs moving on a two-dimensional plane and carrying a rigid payload. Such strategies are apt for applications such as payload delivery, where the 2-UAV system has to interact with either a stationary or a maneuvering platform. The strategies are designed using the concept of nonsingular terminal sliding mode and a kinematics-based formulation of interaction between the 2-UAV system and a point on the platform. The strategies enable the 2-UAV system to track the maneuvering platform and deliver the payload to the platform from any angle, within a finite-time interval. In addition, the maneuvering strategies also include a controller so that the payload can be oriented to a desired angle. These strategies are then generalized, using the concept of collision cones, to scenarios where the payload is to be delivered to within a region on the platform, rather than to a point.