In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.

This article addresses the control problem of an unmanned quadrotor in the absence of absolute position measurement data (e.g. GPS, external cameras). Based on an attached Inertia Measurement Unit, a sonar and an optic flow sensor, the quadrotor’s translational and rotational motion-vector is estimated using sensor fusion algorithms. A control scheme consisted of three Proportional-Integral-Derivative (PID) controllers for the translational motions, combined with three Proportional-Integral-Derivative-second Derivative (PIDD) controllers for the attitude dynamics is utilized in order to achieve accurate position hold and attitude tracking. The controller is implemented on a quadrotor prototype in indoor position hold experiments and aggressive attitude regulation maneuvers.

The design and experimental verification of a Constrained Finite Time Optimal Controller (CFTOC) for attitude maneuvers of an Unmanned Quadrotor operating under severe wind conditions is the subject of this article. The quadrotor’s nonlinear dynamics are linearized in various operating points resulting in a set of piecewise affine models. The CFTO–controller is designed for set-point maneuvers taking into account the switching between the linear models and the state and actuation constraints. The control scheme is applied on experimental studies on a prototype quadrotor operating both in absence and under presence of forcible atmospheric disturbances. Extended experimental results indicate that the proposed control approach attenuates the effects of induced wind–gusts while performing accurate attitude set–point maneuvers.

This article addresses the control problem of an unmanned quadrotor in an indoor environment where there is lack of absolute localization data. Based on an attached Inertia Measurement Unit, a sonar and an optic flow sensor, the state vector is estimated using sensor fusion algorithms. A novel Switching Model Predictive Controller is designed in order to achieve precise trajectory control, under the presence of forcible wind–gusts. The quadrotor’s attitude, altitude and horizontal linearized dynamics result in a set of Piecewise Affine models, enabling the controller to account for a larger part of the quadrotor’s flight envelope while modeling the effects of atmospheric disturbances as additive–affine terms in the system. The proposed controller algorithm accounts for the state and actuation constraints of the system. The controller is implemented on a quadrotor prototype in indoor position tracking, hovering and attitude maneuvers experiments. The experimental results indicate the overall system’s efficiency in position/altitude/attitude set–point maneuvers.

In this article a Model Predictive Control (MPC) strategy for the trajectory tracking of an unmanned quadrotor helicopter is presented. The quadrotor’s dynamics are modeled by a set of Piecewise Affine (PWA) systems around different operating points of the translational and rotational motions. The proposed control scheme is dual and is consisted by an integral MPC for the translational motions, followed by a MPC–scheme for the quadrotor’sattitude motions’ tracking. By the utilization of PWA representations, the controller is computed for a larger part of the quadrotor’s flight envelope. Theproposed dual control scheme is able to calculate optimal control actions with robustness against atmospheric disturbances (e.g. wind gusts) and physical constraints of the quadrotor (e.g. maximum lifting forces or fixed thrust limitations in order to extend flight endurance). Extended simulation studies prove the efficiency of the MPC–scheme, both in trajectory tracking and aerodynamic disturbances attenuation.

In this article a Switching Model Predictive Attitude Controller for an Unmanned quadrotor Helicopter subject to atmospheric disturbances is presented. The proposed control scheme is computed based on a Piecewise Affine (PWA) model of the quadrotor’s attitude dynamics, where the effects of the atmospheric turbulence are taken into consideration as additive disturbances. The switchings among the PWA models are ruled by the rate of the rotation angles and for each PWA system a corresponding model predictive controller is computed. The suggested algorithm is verified in experimental studies in the execution of sudden maneuvers subject to forcible wind disturbances. The quadrotor rejects the induced wind–disturbances while performing accurate attitude tracking.

This article addresses the control problem of quadrotors in environments where absolute-localization data (GPS, positioning from external cameras) is inadequate. Based on an attached IMU and an optical flow sensor the quadrotor’s translational velocity is estimated using an Extended Kalman Filter. Subject to the velocity measurements, the roll, pitch and yaw (RPY) angles, the angular rates and the translational accelerations a switching Model Predictive Controller is designed. The quadrotor dynamics is linearized at various operating points according to the angular rates and the RP angles. The switching is inferred according to the various linearized models of the quadrotor. The controller is applied on a quadrotor prototype in low-altitude position hold maneuvers at very constrained environments. The experimental results indicate the overall system’s efficiency in position/altitude set–point maneuvers.

In this article, a HUmanoid Robotic Leg (HURL) via the utilization of pneumatic muscle actuators (PMAs) is presented. PMAs are a pneumatic form of actuation possessing crucial attributes for the implementation of a design that mimics the motion characteristics of a human ankle. HURL acts as a feasibility study in the conceptual goal of developing a 10 degree-of-freedom (DoF) lower-limb humanoid for compliance and postural control, while serving as a knowledge basis for its future alternative use in prosthetic robotics. HURL’s design properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion–plantar flexion, eversion–inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm.

In this article, the adhesion modeling and control case of a Vortex Climbing Robot (VCR) is investigated against a surface of variable orientations. The critical adhesion force exerted from the implemented Vortex Actuator (VA) and the VCR's achievable payload are analyzed under 3-DOF rotations of the test surface, while extracted from both geometrical analysis and dynamically-simulated numerical results. A model-based control scheme is later proposed, with the goal of achieving adhesion while the VCR remains immobilized, limiting the power consumption and compensating for disturbances (e.g. moving cables) leading to Center-of-Mass (CoM) changes. Finally, the model-based control scheme is experimentally evaluated, with the VCR prototype on a rotating and moving flat surface. The presented results support the use of the proposed methodology in climbing robots targeting inspection and maintenance of stationary surfaces (flat, curved etc.), as well as future robotic solutions operating on moving structures (e.g. ships, cranes, folding bridges).

The aim of this article is to present a switched system approach for the dynamic modeling of Pneumatic Muscle Actuators (PMAs). PMAs are highly non-linear pneumatic actuators where their elongation is proportional to the interval pressure. During the last two decades, various modeling approaches have been presented that describe the behavior of PMAs. While most mathematical models are characterized by simplicity and accuracy in describing the attributes of PMAs, they are limited to static performance analysis. Static models are proven to be insufficient for real time control applications, thus creating the need for the development of dynamic PMA models. A collection of experimental and simulation results are being presented that prove the efficiency of the proposed approach.

The aim of this article is to present a dynamic analysis and a cascade movement simulation of a Pneumatic Muscle Actuator (PMA). PMAs are highly non–linear pneumatic actuators where their elongation are proportional to the interval pressure. Their non–linear characteristics and the property of the hysteresis are posing several difficulties in simulating these pneumatic actuators and to obtain a comprehension of the PMA’s physical movement. In this article a novel detailed modeling, based on hardware in the loop simulationstudies, capable to describe the dynamic characteristic of the PMA and a detailed simulation environment for studying the cascade movement of PMAs will be presented.

In this article, the design and implementation of a 10 Degree-of-Freedom (DoF) human-inspired two-arm robot is presented. Multiple Pneumatic Artificial Muscles (PAMs) in antagonistic formations are incorporated for undertaking the two arms' movements, while the design goal is the replication of human-like motion patterns, described by smoothness, inherent compliance and accuracy. To evaluate the feasibility of the proposed concept, the 10-DoF robot is developed and experimentally tested in open and closed-loop control scenarios via the use of a multiple Advanced Nonlinear PID (ANPID) based scheme.

In this article, the potential of utilizing a commercially available Electric Ducted Fan (EDF) as a negative-pressure actuator for adhesion purposes is experimentally tested. To this purpose, a novel EDF-based Vortex Actuation System (VAS) is proposed and presented from a design, development and experimental evaluation perspective. The effect of different EDF design properties and design alterations to the actuation system is analyzed, for providing novel considerations on optimizing the adhesion efficiency of such a system.

In this article, the design and implementation of a HUmanoid Robotic Leg (HURL) is presented. The motion of the HURL is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the implementation of a biomimetic design that mimics the motion characteristics of a human ankle. The HURL's properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion - plantar flexion, eversion - inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm

The presented work investigates the potential of utilizing commercially available Electric Ducted Fans (EDFs) as adhesion actuators, while providing a novel insight on the analysis of the adhesion nature related to negative pressure and thrust force generation against a target surface. To this goal, a novel EDF-based Vortex Actuation Setup (VAS) is proposed for monitoring important properties such as adhesion force, pressure distribution, current draw, motor temperature etc. during the VAS’ operation when placed in variable distances from a test surface. In addition, this work is contributing towards the novel evaluation of different design variables and modifications to original EDF structures, with the goal of analyzing their effect on the prototype VAS, while optimizing its adhesion efficiency for its future incorporation in a wall-climbing robot for inspection and repair purposes.

The Pneumatic Artificial Muscle (PAM) is a highly non-linear form of actuation that is characterized by a decrease in the actuating length when pressurized. Its non-linear nature and time-varying parameters cause difficulties in modelling their characteristics and designing controllers for high-performance positioning systems. In this article, the control problem of a PAM is considered. A constrained linear and PieceWise Affine (PWA) system model approximation is utilized and a controller composed of: a) a feedforward term regulating control input at specific setpoints, and b) a Constrained Finite Time Optimal Controller (CFTOC) handling any deviations from the system’s equilibrium points is synthesized. Simulation studies are used to investigate the efficacy of the suggested controller.

In this article, a switching Model Predictive Controller (sMPC) for a Pneumatic Artificial Muscle (PAM) is presented. The control scheme is based on a switching PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM and improve the overall model accuracy, and is composed of: a) a feed-forward term regulating control input at specific reference set-points, and b) a switching Model Predictive Controller handling any deviations from the system's equilibrium points. Extended simulation studies indicate the overall scheme's efficiency.

In this article, the modeling and control problem of a Pneumatic Artificial Muscle (PAM) is being considered. The PAM is an actuator characterized by a decrease in the actuating length when pressurized. Its non-linear nature and time-varying parameters cause difficulties in modeling their characteristics, as well as in designing controllers for high-performance positioning systems. A constrained linear and PieceWise Affine (PWA) system model approximation is formulated and a control scheme composed of: a) a feedforward term regulating control input at specific setpoints, and b) a Constrained Finite Time Optimal Controller (CFTOC) handling any deviations from the system’s equilibrium points is being synthesized. Extended experimental studies are utilized to prove the efficacy of the suggested controller.

In this article, the conceptual design of a 14 Degree-of-Freedom (DOF) upper-body pneumatic humanoid is presented. The movement capabilities of this novel robotic setup are achieved via Pneumatic Artificial Muscles (PAMs), a form of actuation possessing crucial attributes for the development of biologically-inspired robots. To evaluate the feasibility of the humanoid’s design properties, a 5-DOF robotic arm is developed and experimentally tested, while being studied from the scope of implementing a robotic structure capable of producing smooth and human-like motion responses, while maintaining the inherent compliance provided by the PAM technology.

The aim of this article is to present a survey on applications of Pneumatic Artificial Muscles (PAMs). PAMs are highly non–linear pneumatic actuators where their elongation is proportional to the interval pressure. During the last decade, there has been a significant increase in the industrial and scientific utilization of PAMs due to their advantages such as high strength and small weight, while various types of PAMs with different technical characteristics have been appeared in the relative scientific literature. This article will summarize the key enabling applications in PAMs that are focusing in the following areas: a) Biorobotic, b) Medical, c) Industrial, and d) Aerospace applications

The Pneumatic Artificial Muscle (PAM) is a highly non-linear form of actuation that is characterized by a decrease in the actuating length when pressurized. Its nonlinear nature and time-varying parameters cause difficulties in modeling their characteristics and designing controllers for high-performance positioning systems. In this article, the model identification and control problem of a PAM is being considered. The identification of the PAM’s model parameters is being carried out by a Recursive Least Square (RLS) based algorithm, while an Internal Model Control (IMC) structure is being synthesized. Experimental studies are being utilized to prove the overall efficiency of the suggested control scheme, regarding: a) set-point tracking performance through selected positioning scenarios, b) robustness through disturbance cancellation, and c) adaptability through hysteresis shift compensation.

In this paper, the positioning control problem of pneumatic muscle actuators (PMAs) is being considered. A two-degree-of-freedom nonlinear proportional-integral-derivative structure is being synthesized, providing ameliorated compensation of the PMAs' nonlinear hysteretic phenomena and advanced robustness through disturbance cancellation. Experimental studies are being utilized to prove the overall efficiency of the proposed control scheme with regard to set-point tracking performance for the position control of a single PMA, torsion angle control of a nonsymmetrical antagonistic PMA setup, and disturbance rejection in both single and antagonistic control scenarios.

In the past fifty years, several attempts have been made to model the characteristics of Pneumatic Artificial Muscles (PAMs). PAM models based on their geometrical properties are the most commonly found ones in the scientific literature. In the process of deriving those models a lot of assumptions and simplifications are made due to the fact that PAM is a highly non-linear form of actuation. The purpose of this study is to propose additional considerations for future model improvements that will augment the overall model accuracy, and will best describe the relationship between force, displacement and non-linear thermal properties of PAM actuators through extensive observation and analysis of its thermodynamic characteristics during long-run operation experiments. In this article multiple experimental results will be presented that prove the relation between the thermodynamic properties of the PAMs, especially in iterative operations, and the accuracy on the muscle's force-prolongation relationship.

Full or partial loss of function in the shoulder, elbow or wrist is an increasingly common ailment caused by various medical conditions like stroke, occupational and sport injuries, as well as a number of neurological conditions, which increases the need for the development and improvement of upper limb rehabilitation devices. In this article, the design and implementation of the EXOskeletal WRIST (EXOWRIST) prototype is presented. This novel robotic appliance’s motion is achieved via pneumatic artificial muscles, a pneumatic form of actuation possessing crucial attributes for the development of an exoskeleton that is safe, reliable, portable and low-cost. Furthermore, the EXOWRIST’s properties are presented in detail and compared to the recent wrist exoskeleton technology, while its two degrees-of-freedom movement capabilities (extension-flexion, ulnar-radial deviation) are experimentally evaluated via a PID- based control algorithm. Experimental results involving initial testing of the proposed exoskeleton on a healthy human volunteer for the preliminary evaluation of the EXOWRIST’s attributes are also presented.

This article presents the development and control of a novel hybrid controlled vertical climbing robot based on Pneumatic Muscle Actuators (PMAs). PMAs are highly non–linear pneumatic actuators where their elongation is proportional to the internal pressure. The vertical sliding of the robot is based on four PMAs and through the combined and sequential contraction–extension of the pneumatic muscles and cylinders, upward and downward movements are executed. For controlling the movement of the robot and to cope with the high non–linearities of the system, a simplified and highly functional hybrid control scheme, based on PID and On/Off control, has been adopted. The efficacy of the proposed scheme is presented through multiple experimental results where it is shown that the utilized controller is able to provide fast (on/off) and accurate (PID) translations to the robot.

In this article, the thermal expansion effect is considered as the main cause of the gradual shift in the force- displacement relationship, which describes the operation of Pneumatic Artificial Muscles (PAMs). A modified static force modeling approach is proposed, based on fundamental PAM modeling techniques, while incorporating the geometrical properties that are being affected by the thermal build-up occurring during PAM’s continuous operation. The effects of thermal expansion are documented via experimental studies and the acquired data are utilized for the validation of the proposed modeling method. Further evaluation is performed via comparison of modeling accuracy between the proposed modeling approach and the fundamental static force modeling techniques.

In this article, the motion control problem of a robotic EXOskeletal WRIST (EXOWRIST) prototype is considered. This novel robotic appliance’s motion is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the development of an exoskeleton that is safe, reliable, portable and low-cost. The EXOWRIST’s properties are presented in detail and compared to the recent wrist exoskeleton technology, while its two degrees- of-freedom movement capabilities (extension-flexion, ulnar- radial deviation) are experimentally evaluated on a healthy human volunteer via an advanced nonlinear PID-based control algorithm.

In this article, the control problem of Pneumatic Artificial Muscles is being considered. A non-linear PID structure is being synthesized, providing ameliorated compensation of the PAMs’ non-linear hysteretic phenomena and advanced robustness. Experimental studies are being utilized to prove the overall efficiency of the proposed control scheme regarding: a) set-point tracking performance for the position control of a single PAM and torsion angle control of an antagonistic PAM setup, as well as b) disturbance rejection in both single and antagonistic control scenarios.

In this article, an overview of the most significant static force modeling approaches of Pneumatic Muscle Actuators (PMAs) is presented, while a modified static force modeling approach, which is based on fundamental PMA modeling techniques, is proposed. In addition, the thermal expansion effect is considered as the main cause of the gradual shift in the PMA’s force-displacement relationship and the geometric properties, which are being affected by the thermal build-up occurring during PMA’s continuous operation, are incorporated into the static force models. The effects of thermal expansion are documented via experimental studies and the acquired force-displacement data are utilized for the validation of the proposed modeling method in PMAs of different nominal dimensions and at constant test pressures. Finally, an additional evaluation is performed via the comparison of the accuracy between the proposed model and the existing geometric static modeling approaches.

In this article, a switching Model Predictive Controller (sMPC) for a Pneumatic Artificial Muscle (PAM) is presented. The control scheme is based on a switching PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM, while improving the overall model accuracy, and is composed of: a) a feed-forward term regulating control input at specific reference set-points, and b) a switching Model Predictive Controller handling any deviations from the system’s equilibrium points. Extended experimental studies are being presented that prove the overall scheme’s efficiency.

In this article a novel performance improvement scheme is being presented for the problem of designing a trajectory tracking controller for non–holonomic mobile robots with differential drive. Based on the robot kinematic equations, an error dynamics controller is being utilized for allowing the robot to follow an a priori defined reference path, with a desired velocity profile. The main novelty of this article stems from the utilization of a gradient based adaptive scheme that is able to adapt the controller’s gain ruling the rising and settling time of the robot and up to now has been ad–hoc selected. The proposed adaptation scheme is based on the robot’s path tracking errors and is able to provide an on–line adjustment for the performance improvement, independently of the selected path type. Multiple experimental test cases, including the movement of the robot on various path profiles, prove the efficacy of the proposed scheme.

In this paper, we study the multi-robot task assignment and path-finding problem (MRTAPF), where a number of robots are required to visit all given tasks while avoiding collisions with each other. We propose a novel two-layer algorithm SA-reCBS that cascades the simulated annealing algorithm and conflict-based search to solve this problem. Compared to other approaches in the field of MRTAPF, the advantage of SA-reCBS is that without requiring a pre-bundle of tasks to groups with the same number of groups as the number of robots, it enables a part of robots needed to visit all tasks in collision-free paths. We test the algorithm in various simulation instances and compare it with state-of-the-art algorithms. The result shows that SA-reCBS has a better performance with a higher success rate, less computational time, and better objective values.

This article presents an overall system architecture for multi-robot coordination in a known environment. The proposed framework is structured around a task allocation mechanism that performs unlabeled multi-robot path assignment informed by 3D path planning, while using a nonlinear model predictive control(NMPC) for each unmanned aerial vehicle (UAV) to navigate along its assigned path. More specifically, at first a risk aware 3D path planner D∗+ is applied to calculate cost between each UAV agent and each target point. Then the cost matrix related to the computed trajectories to each goal is fed into the Hungarian Algorithm that solves the assignment problem and generates the minimum total cost. NMPC is implemented to control the UAV while satisfying path following and input constraints. We evaluate the proposed architecture in Gazebo simulation framework and the result indicates UAVs are capable of approaching their assigned target whilst avoiding collisions.

This paper presents a complete system architecture for multi-robot coordination for unbalanced task assignments, where a number of robots are supposed to visit and accomplish missions at different locations. The proposed method first clusters tasks into clusters according to the number of robots, then the assignment is done in the form of one-cluster-to-one-robot, followed by solving the traveling salesman problem (TSP) to determine the visiting order of tasks within each cluster. A nonlinear model predictive controller (NMPC) is designed for robots to navigate to their assigned tasks while avoiding colliding with other robots. Several simulations are conducted to evaluate the feasibility of the proposed architecture. Video examples of the simulations can be viewed at https://youtu.be/5C7zTnv2sfo and https://youtu.be/-JtSg5V2fTI?si=7PfzZbleOOsRdzRd. Besides, we compare the cluster-based assignment with a simulated annealing (SA) algorithm, one of the typical solutions for the multiple traveling salesman problem (mTSP), and the result reveals that with a similar optimization effect, the cluster-based assignment demonstrates a notable reduction in computation time. This efficiency becomes increasingly pronounced as the task-to-agent ratio grows.

Purpose of Review: The article provides an extensive overview on the resilient autonomy advances made across various missions, orbital or deep-space, that captures the current research approaches while investigating the possible future direction of resiliency in space autonomy.

Recent Findings: In recent years, the need for several automated operations in space applications has been rising, that ranges from the following: spacecraft proximity operations, navigation and some station keeping applications, entry, decent and landing, planetary surface exploration, etc. Also, with the rise of miniaturization concepts in spacecraft, advanced missions with multiple spacecraft platforms introduce more complex behaviours and interactions within the agents, which drives the need for higher levels of autonomy and accommodating collaborative behaviour coupled with robustness to counter unforeseen uncertainties. This collective behaviour is now referred to as resiliency in autonomy. As space missions are getting more and more complex, for example applications where a platform physically interacts with non-cooperative space objects (debris) or planetary bodies coupled with hostile, unpredictable, and extreme environments, there is a rising need for resilient autonomy solutions.

Summary: Resilience with its key attributes of robustness, redundancy and resourcefulness will lead toward new and enhanced mission paradigms of space missions.

In this letter, a floating robotic emulation platform is presented with an autonomous maneuverability for a virtual demonstration of a satellite motion. Such a robotic platform design is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface. The design of the robotic platform, integrated with the sensor and actuator units, is briefly described, including the related component specification along with the mathematical model, describing its dynamic motion. Additionally, the article establishes a nonlinear optimal control architecture consisting of a unified model predictive approach for the overall manoeuvre tracking. The efficacy of the proposed modeling and control scheme is demonstrated in multiple experimental studies, where it is depicted that the proposed controller has the potential to address a precise point-to-point manoeuvre with terminal objectives, as well as an excellent path following capability. The proposed design is validated with extensive experimental studies, and it is supported with related results.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

The aim of this article is to present a survey on inspection applications of Pneumatic Wall-Climbing Robots (PWCR). In general, a PWCR utilizes negative pressure as its adhesion method, through mainly suction cups or negative pressure thrust-based mechanisms. Their main advantage being their ability to climb non-ferromagnetic surfaces, such as glass and composite materials, in comparison with climbing robots based on magnetic adhesion methods. A growing application area is the utilization of PWCRs for inspection purposes for accelerating the otherwise time consuming procedures of manual inspection, while offering the important advantage of protecting human workers from hazardous and/or unreachable environments. This article will summarize the key enabling inspection applications of PWCRs in the following areas: a) Construction, b) Industrial Infrastructures, as well as c) Aircraft applications.

In this article, the potential of utilizing an Electric Ducted Fan (EDF) as an adhesion actuator is investigated in detail, where an experimental setup is implemented for evaluating the EDF's ability to adhere to a test surface through negative pressure generation. Different design variables and modifications to the original EDF structure are tested, while their impact on the adhesion efficiency is experimentally evaluated. The presented investigation acts as a preliminary study to the goal of incorporating the resulting optimized negative pressure-based actuation method in a wall-climbing robot for inspection of aircraft fuselages

In this article, the analytical modeling of a Vortex Robotic Platform (VRP) is investigated. Following the design of the Vortex Actuation (VA) unit and VRP presented in authors' previous work, the target goal is focused on providing a modeling methodology to include system dependencies on surfaces of different curvatures and robot orientations. The critical force model for guaranteeing successful adhesion is extracted for each case, while an overview of the maximum payload is also provided. The validity of the proposed methodology is evaluated through comparative simulations.

A new challenging area of research in autonomous systems focuses on the collaborative multi-agent exploration of unknown environments where a reliable communication infrastructure among the robotic platforms is absent. Factors like the proximity between agents, the characteristics of the network nodes, and environmental conditions can significantly impact data transmission in real-world applications. We present a novel decentralized collaborative architecture based on multi-agent reinforcement learning to address this challenge. In this framework, homogeneous agents autonomously decide to communicate or not, that is whether to share locally collected maps with other agents in the same communication networks or to navigate and explore the environment further. The agents' policies are trained using the heterogeneous-agent proximal policy optimization (HAPPO) algorithm and through a novel reward function that balances inter-agent communication and exploratory behaviors. The proposed architecture enhances mapping efficiency and robustness while minimizing inter-agent redundant data transmission. Finally, this paper demonstrates the advantages of the investigated approach compared to a strategy that does not incentivize communicative behaviors.

In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. In general, communication between autonomous systems is crucial when exploring large and unstructured environments. In such real-world scenarios, data transmission is limited and relies heavily on inter-agent proximity and the attributes of the autonomous platforms. In the proposed approach, each agent's policy is optimized by utilizing the heterogeneous-agent proximal policy optimization algorithm to autonomously choose whether to communicate or explore the environment. To accomplish this, multiple novel reward functions are formulated by integrating inter-agent communication and exploration. The investigated approach aims to increase efficiency and robustness in the mapping process, minimize exploration overlap, and prevent agent collisions. The D-MARL policies trained on different reward functions have been compared to understand the effect of different reward terms on the collaborative attitude of the homogeneous agents. Finally, multiple simulation results are provided to prove the efficacy of the proposed scheme.

In this article, a novel Vertical Take-Off and Landing (VTOL) Single Rotor Unmanned Aerial Vehicle (SR- UAV) will be presented. The SRUAV’s design properties will be analysed in detail, with respect to technical novelties outlining the merits of such a conceptual approach. The system’s model will be mathematically formulated, while a cascaded P-PI and PID-based control structure will be utilized in extensive simulation trials for the preliminary evaluation of the SR-UAV’s attitude and translational performance.

Robust localization is a fundamental capability to increase the autonomy levels of robotic platforms. A core processing step in vision based odometry methods is the extraction and tracking of distinctive features in the image frame. Nevertheless, when deploying robots in challenging environments like underground tunnels, the sensor measurements are noisy with lack of information due to low light conditions, introducing a bottleneck for feature detection methods. This paper proposes a deep classifier Convolutional Neural Network (CNN) architecture to retain detailed and noise tolerant feature maps from RBG images, establishing a novel feature tracking scheme in the context of localization. The proposed method is feeding the RGB image into the AlexNet or VGG-16 network and extracts a feature map at a specific layer. This feature map consists of feature points which are then paired between frames resulting in a discrete vector field of feature change. Finally, the proposed method is evaluated with RGB camera footage of the Micro Aerial Vehicle (MAV) flights in dark underground mines and the performance is compared with existing feature extraction methods, while the noise is added to the images.

This survey on Control Configuration Selection (CCS) includes methods based on relative gains, gramian-based interaction measures, methods based on optimization schemes, plantwide control, and methods for the reconfiguration of control systems. The CCS problem is discussed, and a set of desirable properties of a CCS method are defined. Open questions and research tracks are discussed, with the focus on new challenges in relation to the emerging area of Wireless Sensors and Actuator Networks.

This article proposes local bidding strategies for autonomous agents participating in an auction-based multi-agent coordination system, in order to improve the scalability and reactivity of the architecture in large-scale coordination scenarios. Based on a careful analysis of the reactivity requirements and the computational costs of the central auctioneer (costs for solving Linear Integer Programs) and the local agents (costs for path-planning and task execution), this article explores the idea of each agent bidding for a subset of available tasks that are locally relevant to the agent. Each agent first employs a computationally light euclidean distance-based and percentile-based screening method to choose a subset of available tasks, followed by a more computationally complex, but realistic path-planning based cost-estimation and bidding for the chosen subset. The proposed strategy not only reduces the overall computational cost at the agents, but also at the central auctioneer, by reducing the size of the combinatorial optimization problems and the overall communication requirements of the architecture, thereby improving the scalability and reactivity of the overall system. It is shown that, through a one-time simulation-guided design of the bidding parameters, the improved reactivity and scaling is achieved while retaining the optimality or near-optimality of the resulting task-allocation. The performance of the proposed bidding strategies is evaluated in two large-scale simulation scenarios and the reduction in computational costs and the near-optimality of the task allocation is demonstrated.