Showing papers in "Journal of Intelligent and Robotic Systems in 2016"
TL;DR: This paper first proposes an energy model derived from real measurements, and then uses this model to implement a coverage path planning algorithm for reducing energy consumption, as well as guaranteeing a desired image resolution.
Abstract: Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. In this context, the problem of finding a path that covers the entire area of interest is known as Coverage Path Planning (CPP). Although this problem has been addressed by several authors from a geometrical point of view, other issues such as energy, speed, acceleration, and image resolution are not often taken into account. To fill this gap, this paper first proposes an energy model derived from real measurements, and then uses this model to implement a coverage path planning algorithm for reducing energy consumption, as well as guaranteeing a desired image resolution. In addition, two safety mechanisms are presented: the first, executed off-line, checks whether the energy stored in the battery is sufficient to perform the planned path; the second, performed online, triggers a safe return-to-launch (RTL) operation when the actual available energy is equal to the energy required by the UAV to go back to the starting point.
162 citations
TL;DR: The contribution of this work includes the definition of metrics for path planning benchmarks, actual benchmarks of the most common global path planning algorithms and an educated algorithm parameterization based on a global obstacle density coefficient.
Abstract: Path planning constitutes one of the most crucial abilities an autonomous robot should possess, apart from Simultaneous Localization and Mapping algorithms (SLAM) and navigation modules. Path planning is the capability to construct safe and collision free paths from a point of interest to another. Many different approaches exist, which are tightly dependent on the map representation method (metric or feature-based). In this work four path planning algorithmic families are described, that can be applied on metric Occupancy Grid Maps (OGMs): Probabilistic RoadMaps (PRMs), Visibility Graphs (VGs), Rapidly exploring Random Trees (RRTs) and Space Skeletonization. The contribution of this work includes the definition of metrics for path planning benchmarks, actual benchmarks of the most common global path planning algorithms and an educated algorithm parameterization based on a global obstacle density coefficient.
104 citations
TL;DR: This paper addresses the problem of robust flight control of unmanned rotorcrafts by proposing and experimentally evaluating a real–time robust model predictive control scheme in various challenging conditions, aiming to capture the demanding nature of the potential requirements for their efficient and safe integration in real–life operations.
Abstract: This paper addresses the problem of robust flight control of unmanned rotorcrafts, by proposing and experimentally evaluating a real---time robust model predictive control scheme in various challenging conditions, aiming to capture the demanding nature of the potential requirements for their efficient and safe integration in real---life operations. The control derivation process is based on state space representations applicable in most rotorcraft configurations and incorporate the effects of external disturbances. Exploiting this modeling approach, two different unmanned rotorcraft configurations are presented and experimentally utilized. The formulated control strategy consists of a receding horizon scheme, the optimization process of which employs the minimum peak performance measure, while incorporating and accounting for the modeled dynamics and input and state constraints. This strategy aims to ensure the minimum possible deviation subject to the worst---case disturbance, while robustly respecting and satisfying the physical limitations of the system, or a set of mission-related requirements, as encoded in the constraints. The proposed framework is further augmented in order to provide obstacle avoidance capabilities into a unified scheme. Multiparametric methods were utilized in order to provide an explicit solution to the controller computation optimization problem, therefore allowing for fast real---time execution and seamless integration into any digital avionics system. The efficiency of the proposed strategy is validated via several experimental case studies on the two unmanned rotorcraft platforms. The experiments set consists of: trajectory tracking subject to atmospheric disturbances, slung load operations, fast highly disturbed maneuvers, collisions handling, as well as avoidance of known obstacles.
92 citations
TL;DR: This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings and discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.
Abstract: In Human-Robot Interactions (HRI), robots should be socially intelligent They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI
90 citations
TL;DR: A complete six-degree-of-freedom nonlinear mathematical model of a tilt rotor unmanned aerial vehicle (UAV) for the design of a hover to forward flight and forward flight to hover transition control system and shows the successful transition of TURAC in experiment.
Abstract: This paper describes a complete six-degree-of-freedom nonlinear mathematical model of a tilt rotor unmanned aerial vehicle (UAV). The model is specifically tailored for the design of a hover to forward flight and forward flight to hover transition control system. In that respect, the model includes the aerodynamic effect of propeller-induced airstream which is a function of cruise speed, tilt angle and angle of attack. The cross-section area and output velocity of the propeller-induced airstream are calculated with momentum theory. The projected area on the UAV body that is affected by the propeller-induced airstream is specified and 2D aerodynamic analyses are performed for the airfoil profile of this region. Lookup-tables are generated and implemented in the nonlinear mathematical model. In addition, aerodynamic coefficients of the airframe are calculated by using CFD method and these data are embedded into the nonlinear model as a lookup-table form. In the transition flight regime, both aerodynamic and thrust forces act on the UAV body and the superimposed dynamics become very complex. Hence, it is important to define a method for hover-to-cruise and cruise-to-hover transitions. To this end, both transition scenarios are designed and a state-schedule is developed for flight velocity, angle of attack, and thrust levels of each of the thrust-propellers. This transition state schedule is used as a feedforward state for the flight control system. We present the simulation results of the transition control system and show the successful transition of TURAC in experiment.
88 citations
TL;DR: A novel framework called MITE is proposed to characterize both the properties and applications of MRS from four perspectives of Module, Information, Task, and Environment, based on more than 120 domain-specific features.
Abstract: Employing Modular Robotic Systems (MRS) in different application domains confronts a large number of challenging problems in design, optimization, and planning, and so identifying characteristics of such problems is an important step toward finding proper solution approaches for them. In this paper, we address this issue and provide a comprehensive study on MRS through a structured survey about MRS characteristics and their applications. A novel framework called MITE is proposed to characterize both the properties and applications of MRS from four perspectives of Module, Information, Task, and Environment, based on more than 120 domain-specific features, supplemented by a mapping scheme for describing the interrelations of the four basic aspects of the Task component, namely, Application (for describing high-level tasks such as navigation and rescue), Behavior (for referring to constitutive behaviors like locomotion and manipulation which bring about Applications), Goal (for characterizing the way Behaviors are accomplished), and Operation (for designating activities specific to modular robots, such as self-reconfiguration and gait control). Also, by providing a methodical review on modular robotics, the paper deals with some analyses on recent trends, research gaps and challenges, as well as open problems in the field of MRS.
86 citations
TL;DR: An important aspect of the proposed method is that the cooperating MAVs are localized relatively to each other, rather than using a global localization system, which increases robustness of the system and its deploy-ability in scenarios, in which compact shapes of the MAV group with short relative distances are required.
Abstract: The task of cooperative surveillance of pre-selected Areas of Interest (AoI) in outdoor environments by groups of closely cooperating Micro Aerial Vehicles (MAVs) is tackled in this paper. In the cooperative surveillance mission, finding distributions of the MAVs in the environment to properly cover the AoIs and finding feasible trajectories to reach the obtained surveillance locations from the initial depot are crucial tasks that have to be fulfilled. In addition, motion constraints of the employed MAVs, environment constraints (e.g. non-fly zones), and constraints imposed by localization of members of the groups need to be satisfied in the planning process. We formulate the task of cooperative surveillance as a single high-dimensional optimization problem to be able to integrate all these requirements. Due to numerous constraints that have to be satisfied, we propose to solve the problem using an evolutionary-based optimization technique. An important aspect of the proposed method is that the cooperating MAVs are localized relatively to each other, rather than using a global localization system. This increases robustness of the system and its deploy-ability in scenarios, in which compact shapes of the MAV group with short relative distances are required.
85 citations
TL;DR: A proposed algorithm is introduced based on an integrated structure between the Nonlinear Integral-Backstepping technique (NIB) and the MFC that shows robust performance compared to the other algorithms under fault-free and actuator fault conditions.
Abstract: The objective of this paper is to deal with a new technique based on Model-Free Control (MFC). The concept of this controller is to use a basic controller along with an ultra-local model to compensate for system's uncertainties and disturbances. In this paper, a proposed algorithm is introduced based on an integrated structure between the Nonlinear Integral-Backstepping technique (NIB) and the MFC. The LQR, NIB, LQR-MFC, and NIB-MFC are implemented on a real quadrotor UAV. Various real-time flight tests are conducted to validate the importance of using the MFC side by side with NIB. The proposed combination shows robust performance compared to the other algorithms under fault-free and actuator fault conditions.
80 citations
TL;DR: Results indicate the effectiveness, adaptiveness and robustness of the control strategy for quadrotor to track 3D trajectory subject to payload variation and fast time-varying wind gust disturbance.
Abstract: This work proposes a hierarchical nonlinear control scheme for quadrotor to track 3D trajectory subject to payload variation and fast time-varying wind gust disturbance. In terms of dynamics model, the 6 DOF dynamics model with parametric and nonparametric uncertainties is built up. Wind gust and propeller momentum drag model are implemented to quantify the wind impact (force and moment disturbances) on quadrotor. In terms of control design, adaptive robust controller is developed for dynamic subsystem to deal with moment disturbance and estimate the system parameters. Global sliding mode controller is implemented for kinematic subsystem to generate adequate desired attitude angles for tracking the planned 3D trajectory. Simulations and experiments under various conditions are carried out for verification, and the results indicate the effectiveness, adaptiveness and robustness of the control strategy.
80 citations
TL;DR: A quadrotor modelled as a Linear Parameter Varying (LPV) system is considered as a target to design and to illustrate the proposed methodologies, and a robust LPV observer is designed in order to perform fault detection and isolation.
Abstract: This work is dedicated to the design of a robust fault detection and tracking controller system for a UAV subject to external disturbances. First, a quadrotor modelled as a Linear Parameter Varying (LPV) system is considered as a target to design and to illustrate the proposed methodologies. In order to perform fault detection and isolation, a robust LPV observer is designed. Sufficient conditions to guarantee asymptotic stability and robustness against disturbance are given by a set of feasible Linear Matrix Inequalities (LMIs). Furthermore, the observer gains are designed with a desired dynamic by considering pole placement based on LMI regions. Then, a fault detection and isolation scheme is considered by mean of an observer bank in order to detect and isolate sensor faults. Second, a feedback controller is designed by considering a comparator integrator control scheme. The goal is to design a robust controller, such that the UAV tracks some reference positions. Finally, some simulations in fault-free and faulty operations are considered on the quadrotor system.
78 citations
TL;DR: The design of an open educational low-cost modular and extendable mobile robot based on Android and Arduino, with Local Area Network (LAN) and Internet connection capabilities, to be used as an educational tool in labs and classrooms of information and communications technology (ICT) vocational training, or in engineering courses, as well as in e-learning or massive open online courses (MOOC).
Abstract: This work presents the design of an open educational low-cost (35 euros) modular and extendable mobile robot based on Android and Arduino, with Local Area Network (LAN) and Internet connection capabilities, to be used as an educational tool in labs and classrooms of information and communications technology (ICT) vocational training, or in engineering courses, as well as in e-learning or massive open online courses (MOOC) as an alternative or complementary to virtual labs. It is a first step introducing what we call "BYOR: Bring Your Own Robot" education policy equivalent to "BYOD: Bring your own devices" in computers' world.
TL;DR: This paper proposes to split the original problem into simpler behaviors that can be modeled by scalable POMDPs, then auctioned during the mission among the UAVs, which follow different policies depending on the behavior assigned.
Abstract: Surveillance is an interesting application for Unmanned Aerial Vehicles (UAVs). If a team of UAVs is considered, the objective is usually to act cooperatively to gather as much information as possible from a set of moving targets in the surveillance area. This is a decision-making problem with severe uncertainties involved: relying on imperfect sensors and models, UAVs need to select targets to monitor and determine the best actions to track them. Partially Observable Markov Decision Processes (POMDPs) are quite adequate for optimal decision-making under uncertainties, but they lack scalability in multi-UAV scenarios, becoming tractable only for toy problems. In this paper, we take a step forward to apply POMDP methods in real situations, where the team needs to adapt to the circumstances during the mission and foster cooperation among the team-members. We propose to split the original problem into simpler behaviors that can be modeled by scalable POMDPs. Then, those behaviors are auctioned during the mission among the UAVs, which follow different policies depending on the behavior assigned. We evaluate the performance of our approach with extensive simulations and propose an implementation with real quadcopters in a testbed scenario.
TL;DR: This paper presents a novel technique to integrate both the activation and deactivation of tasks and the inequality control objectives in the priority based control, called iCAT (inequality control objectives, activations and transitions) task priority framework, which exploits novel regularization methods.
Abstract: The task priority based control is a formalism which allows to create complex control laws with nice invariance properties, i.e. lower priority tasks do not affect the execution of higher priority ones. However, the classical task priority framework (Siciliano and Slotine) lacked the ability of enabling and disabling tasks without causing discontinuities. Furthermore, tasks corresponding to inequality control objectives could not be efficiently represented within that framework. In this paper we present a novel technique to integrate both the activation and deactivation of tasks and the inequality control objectives in the priority based control. The technique, called iCAT (inequality control objectives, activations and transitions) task priority framework, exploits novel regularization methods to activate and deactivate any row of a given task in a prioritized hierarchy without incurring in practical discontinuities, while maintaining as much as possible the invariance properties of the other active tasks. Finally, as opposed to other techniques, the proposed approach has a linear cost in the number of tasks. Simulations, experimental results and a time analysis are presented to support the proposed technique.
TL;DR: A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target that is comprised of a target loitering algorithm and a formation flight algorithm.
Abstract: A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV's speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target's speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.
TL;DR: This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution.
Abstract: End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included
TL;DR: Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.
Abstract: In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.
TL;DR: The backbone of this scheme is a modeling representation that incorporates the separate internal dynamics of the two actuation principles and their interferences as they concurrently act on the free-flying vehicle body, while tractably representing their differentiated effects on the evolution of the longitudinal dynamics.
Abstract: This paper addresses the exploitation of the combined potential of the directly-actuated and the underactuated control authorities of unmanned aerial vehicles with thrust-vectoring actuation. For the modeling, control synthesis and experimental evaluation a custom developed unmanned tri-tiltrotor is employed, equipped with rotor-tilting mechanisms which enable the direct actuation of its longitudinal dynamics, while retaining the standard body-pitching underactuated authority. An explicit model predictive control scheme relying on constrained multiparametric optimization is proposed for the dual-authority optimal control. The backbone of this scheme is a modeling representation that incorporates the separate internal dynamics of the two actuation principles and their interferences as they concurrently act on the free-flying vehicle body, while tractably representing their differentiated effects on the evolution of the longitudinal dynamics. This paper additionally presents the key implemented features that enable the autonomous operation of the employed tilt-rotor platform, in order to provide a reliable testbed for experimental evaluation. Finally, extensive experimental studies which conclusively validate this strategy's increased efficiency are demonstrated.
TL;DR: A multipurpose system architecture for autonomous multi-UAV platforms is presented that can be used by the system designers as a template when developing their own systems and aims to be a reference for all designers.
Abstract: During the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi-robot system. The second one is the difficulty of having an autonomous prototype of the system for the developers that allows to test the performance of each module even in an early stage of the project. These two problems motivate this paper. A multipurpose system architecture for autonomous multi-UAV platforms is presented. This versatile system architecture can be used by the system designers as a template when developing their own systems. The proposed system architecture is general enough to be used in a wide range of applications, as demonstrated in the paper. This system architecture aims to be a reference for all designers. Additionally, to allow for the fast prototyping of autonomous multi-aerial systems, an Open Source framework based on the previously defined system architecture is introduced. It allows developers to have a flight proven multi-aerial system ready to use, so that they can test their algorithms even in an early stage of the project. The implementation of this framework, introduced in the paper with the name of "CVG Quadrotor Swarm", which has also the advantages of being modular and compatible with different aerial platforms, can be found at https://github.com/Vision4UAV/cvg_quadrotor_swarm with a consistent catalog of available modules. The good performance of this framework is demonstrated in the paper by choosing a basic instance of it and carrying out simulation and experimental tests whose results are summarized and discussed in this paper.
TL;DR: An Adaptive Fuzzy Backstepping Control (AFBC) approach with state observer is developed to overcome the problem of trajectory tracking for a Quadrotor Unmanned Aerial Vehicle under wind gust conditions and parametric uncertainties.
Abstract: In this paper, an Adaptive Fuzzy Backstepping Control (AFBC) approach with state observer is developed. This approach is used to overcome the problem of trajectory tracking for a Quadrotor Unmanned Aerial Vehicle (QUAV) under wind gust conditions and parametric uncertainties. An adaptive fuzzy controller is directly used to approximate an unknown nonlinear backstepping controller which is based on the exact model of the QUAV. Besides, a state observer is constructed to estimate the states. The stability analysis of the whole system is proved using Lyapunov direct method. Uniformly Ultimately Bounded (UUB) stability of all signals in the closed-loop system is ensured. The proposed control method guarantees the tracking of a desired trajectory, attenuates the effect of external disturbances such as wind gust, and solves the problem of unavailable states for measurement. Extended simulation studies are presented to highlight the efficiency of the proposed AFBC scheme.
TL;DR: A prototype UAV monitoring system that captures flight data, and then performs real time estimation and tracking of the airframe and controller parameters, utilizing the Recursive Least Squares Method (RLSM).
Abstract: The proliferation of Unmanned Aerial Vehicles (UAVs) brings about many new security concerns. A common concern with UAV security is for an intruder to take control of a UAV, which leads for a need for a real time anomaly detection system. This research resulted in a prototype UAV monitoring system that captures flight data, and then performs real time estimation and tracking of the airframe and controller parameters. The aforementioned is done by utilizing the Recursive Least Squares Method (RLSM). Using statistical validation and trend analysis, parameter estimates are critical for the detection of cyber attacks and incipient hardware failures that can invariably jeopardize mission success. The results demonstrate that achieving efficient anomaly detection during flight is possible through the application of statistical methods to profile system behavior. The anomaly detection system that was designed can be performed in real time while the UAV is in flight, constantly verifying its parameters.
TL;DR: Simulation results demonstrate that the proposed method is effective for optimizing the fuzzy tracking controller on-line and counteracting the side effects of actuator faults, and the control performance is significantly improved as well.
Abstract: This paper presents a novel learning-based fault tolerant tracking control approach by using an extended Kalman filter (EKF) to optimize a Mamdani fuzzy state-feedback tracking controller. First, a robust state-feedback tracking controller is designed as the baseline controller to guarantee the expected system performance in the fault-free condition. Then, the EKF is employed to regulate the shape of membership functions and rules of fuzzy controller to adapt with the working conditions automatically after the occurrence of actuator faults. Next, based on the modified fuzzy membership functions and rules, the baseline controller is readjusted to properly compensate the adverse effects of actuator faults and asymptotically stabilize the closed-loop system. Finally, in order to verify the effectiveness of the proposed method, several groups of numerical simulations are carried out by comparing the performance of a tracking control scheme and the presented technique. Simulation results demonstrate that the proposed method is effective for optimizing the fuzzy tracking controller on-line and counteracting the side effects of actuator faults, and the control performance is significantly improved as well.
TL;DR: A computational system designed to perform autonomous indoor flights using low-cost equipment that embeds the last two subsystems just mentioned, plus a communication link between the ground computer and the aircraft, to read the sensory data and to send the control signals necessary to guide the vehicle.
Abstract: This paper presents a computational system designed to perform autonomous indoor flights using low-cost equipment. Depending on the mission to be accomplished, one or two Parrot AR.Drone 2.0 quadrotors are supposed to fly in a three-dimensional workspace, guided by the navigation algorithms embedded in the proposed framework, which runs in a ground control computer. The tasks addressed involve positioning, trajectory tracking and leader-follower formation control. The key techniques required to solve such problems are reported in topics, including the mathematic modelling of the quadrotor, a model-based nonlinear flight controller and a state estimation strategy for sensory data fusion. The framework embeds the last two subsystems just mentioned, plus a communication link between the ground computer and the aircraft, to read the sensory data and to send the control signals necessary to guide the vehicle. Some experimental results are also presented and discussed, which allow concluding that the proposed methods are efficient in accomplishing the tasks addressed.
TL;DR: A mixed integer linear programming (MILP) formulation for the problem of providing simultaneous UAV escort service to multiple customers across a field of operations with multiple sharable LSSs is developed and efficient heuristics to rapidly derive near optimal solutions are developed.
Abstract: A networked system consisting of unmanned aerial vehicles (UAVs), automated logistic service stations (LSSs), customer interface software, system orchestration algorithms and UAV control software can be exploited to provide persistent service to its customers. With efficient algorithms for UAV task planning, the UAVs can autonomously serve the customers in real time. Nearly uninterrupted customer service may be accomplished via the cooperative hand-off of customer tasks from weary UAVs to ones that have recently been replenished at an LSS. With the goal of enabling the autonomy of the task planning tasks, we develop a mixed integer linear programming (MILP) formulation for the problem of providing simultaneous. UAV escort service to multiple customers across a field of operations with multiple sharable LSSs. This MILP model provides a formal representation of our problem and enables use in a rolling horizon planner via allowance of arbitrary UAV initial locations and consumable reservoir status (e.g., battery level). As such, it enables automation of the orchestration of system activities. To address computational complexity, we develop efficient heuristics to rapidly derive near optimal solutions. A receding horizon task assignment (RHTA) heuristic and sequential task assignment heuristic (STAH) are developed. STAH exploits properties observed in optimal solutions obtained for small problems via CPLEX. Numerical studies suggest that RHTA and STAH are 45 and 2100 times faster than solving the MILP via CPLEX, respectively. Both heuristics perform well relative to the optimal solution obtained via CPLEX. An example demonstrating the use of the approach for rolling horizon planning is provided.
TL;DR: This article proposes a complete navigation system with a multimodal sensor setup for omnidirectional environment perception for fully autonomous operation of micro aerial vehicles in restricted environments and evaluates the approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnid Directional perception and quick navigation reactions.
Abstract: Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.
TL;DR: A hybrid SLAM (VineSLAM) considering low cost landmarks to increase the robot localization accuracy, robustness and redundancy on these steep slope vineyards and a cost-effective robot to carry-out crop monitoring tasks in steep slope VineSLAM environment is presented.
Abstract: Develop ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge. Because of two main reasons: harsh condition of the terrain and unstable localization accuracy got from Global Positioning Systems (GPS). This paper presents a hybrid SLAM (VineSLAM) considering low cost landmarks to increase the robot localization accuracy, robustness and redundancy on these steep slope vineyards. Also, we present a cost-effective robot to carry-out crop monitoring tasks in steep slope vineyard environment. Test results got in a simulated and in a real test case supports the proposed approach and robot.
TL;DR: Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal, and the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, reduces the complexity of the control problem.
Abstract: This manuscript presents an application of the multi-layer control scheme to guide a formation of unmanned aerial vehicles (UAV) in positioning and trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. In the sequel, a strategy to guide a formation of multiple unmanned aerial vehicles (MUAV) to accomplish positioning and trajectory-tracking tasks is also proposed. Delaunay triangulation is here used to split the platoon of UAVs in N-triangles, which are individually guided by the multi-layer control scheme (MLCS). The advantage of the MLCS in such proposal is the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, which reduces the complexity of the control problem. Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal.
TL;DR: This paper presents a method of autonomously detecting weld seam profiles from molten pool background in metal active gas (MAG) arc welding using a novel model of saliency-based visual attention, which shows that the extracted weld seam profile can basically meet the requirements of seam tracking and the guidance of welding torches.
Abstract: This paper presents a method of autonomously detecting weld seam profiles from molten pool background in metal active gas (MAG) arc welding using a novel model of saliency-based visual attention. First, a vision sensor based on structured light is employed to capture laser stripes and molten pools simultaneously in the same frame. Second, to effectively detect the weld seam profile from molten pool background for next autonomous guidance of initial welding positions and seam tracking, a model of visual attention based on saliency is proposed. With respect to the enhanced effect of saliency, the proposed model is much better than the classic models in the field. According to the comprehensive saliency map created by the proposed model, the weld seam profile can be extracted after threshold segmentation and clustering are applied to it in turn. Third, different weld seam images are used to demonstrate the robustness of the proposed methodology and last, to evaluate the performance of the proposed method, a measure called profile extraction rate (PER) is computed, which shows that the extracted weld seam profile can basically meet the requirements of seam tracking and the guidance of welding torches.
TL;DR: This paper addresses the issue how to coordinate a multi-robot system to clear blocked paths and proposes four strategies as to when to collaborate, showing that the heuristics decrease the time required to explore unknown environments considering blocked paths.
Abstract: Work on coordinated multi-robot exploration often assumes that all areas to be explored are freely accessible. This common assumption does not always hold, especially not in search and rescue missions after a disaster. Doors may be closed or paths blocked detaining robots from continuing their exploration beyond these points and possibly requiring multiple robots to clear them. This paper addresses the issue how to coordinate a multi-robot system to clear blocked paths. We define local collaborations that require robots to collaboratively perform a physical action at a common position. A collaborating robot needs to interrupt its current exploration and move to a different location to collaboratively clear a blocked path. We raise the question when to collaborate and whom to collaborate with. We propose four strategies as to when to collaborate. Two obvious strategies are to collaborate immediately or to postpone any collaborations until only blocked paths are left. The other two strategies make use of heuristics based on building patterns. While no single strategy behaves optimal in all scenarios, we show that the heuristics decrease the time required to explore unknown environments considering blocked paths.
TL;DR: A new fuzzy reinforcement learning algorithm that is a decentralized algorithm as no communication among the pursuers is required and is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games.
Abstract: In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.
TL;DR: This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously using a 2D laser range finder in foliage environment without GPS using a real-time dual layer control, navigation state estimation and online path planning.
Abstract: This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously using a 2D laser range finder in foliage environment without GPS. The navigation framework consists of real-time dual layer control, navigation state estimation and online path planning. In particular, the inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The navigation state estimation consists of real-time onboard motion estimation and trajectory smoothing using the GraphSLAM technique. The onboard real-time motion estimation is achieved by a Kalman filter, fusing the planar velocity measurement from matching the consecutive scans of a laser range finder and the acceleration measurement of an inertial measurement unit. The trajectory histories from the real-time autonomous navigation together with the observed features are fed into a sliding-window based pose-graph optimization framework. The online path planning module finds an obstacle-free trajectory based the local measurement of the laser range finder. The performance of the proposed navigation system is demonstrated successfully on the autonomous navigation of a small-scale UAV in foliage environment.