This paper presents a novel framework based on edge computing, implemented using Kubernetes orchestration, to optimally offload the computational tasks required for centralized control of multiple robotic agents. Edge-based centralized control architectures are prone to failure due to communication delays. The proposed framework computes the maximum round-trip time delay for which the system remains stable and modifies the controller parameters to ensure the control computation within the critical time. For higher processing and communication delays, the complexity of the controller needs to be reduced by reducing the number of agents, the prediction horizon, and the efficient use of edge resources. The edge resources are dynamic, and the controller needs to be designed to guarantee the online computation within a desired time. A dynamic resource allocation method (based on an approximate function of the controller parameters, complexity, and computational resources) is proposed to design the controller parameters to ensure the bounded computation time. To validate the effectiveness of the proposed approach, we conduct experimental evaluations that analyze system behavior under various conditions, providing valuable insights into the performance, scalability, and robustness of multi-agent control systems deployed on edge infrastructure.

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A* algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems.

As industries increasingly adopt large robotic fleets, there is a pressing need for computationally efficient, practical, and optimal conflict-free path planning for multiple robots. Conflict-Based Search (CBS) is a popular method for multi-agent path finding (MAPF) due to its completeness and optimality; however, it is often impractical for real-world applications, as it is computationally intensive to solve and relies on assumptions about agents and operating environments that are difficult to realize. This article proposes a solution to overcome computational challenges and practicality issues of CBS by utilizing structural-semantic topometric maps. Instead of running CBS over large grid-based maps, the proposed solution runs CBS over a sparse topometric map containing structural-semantic cells representing intersections, pathways, and dead ends. This approach significantly accelerates the MAPF process and reduces the number of conflict resolutions handled by CBS while operating in continuous time. In the proposed method, robots are assigned time ranges to move between topometric regions, departing from the traditional CBS assumption that a robot can move to any connected cell in a single time step. The approach is validated through real-world multi-robot path-finding experiments and benchmarking simulations. The results demonstrate that the proposed MAPF method can be applied to real-world non-holonomic robots and yields significant improvement in computational efficiency compared to traditional CBS methods while improving conflict detection and resolution in cases of corridor symmetries.

Autonomous navigation in unknown environments is a fundamental challenge in robotics, particularly in coordinating ground and aerial robots to maximize exploration efficiency. This paper presents a novel approach that utilizes a hierarchical graph to represent the environment, encoding both geometric and semantic traversability. The framework enables the robots to compute a shared confidence metric, which helps the ground robot assess terrain and determine when deploying the aerial robot will extend exploration. The robot's confidence in traversing a path is based on factors such as predicted volumetric gain, path traversability, and collision risk. A hierarchy of graphs is used to maintain an efficient representation of traversability and frontier information through multi-resolution maps. Evaluated in a real subterranean exploration scenario, the approach allows the ground robot to autonomously identify zones that are no longer traversable but suitable for aerial deployment. By leveraging this hierarchical structure, the ground robot can selectively share graph information on confidence-assessed frontier targets from parts of the scene, enabling the aerial robot to navigate beyond obstacles and continue exploration.

This work establishes the concept of commonsense scene composition, with a focus on extending Belief Scene Graphs by estimating the spatial distribution of unseen objects. Specifically, the commonsense scene composition capability refers to the understanding of the spatial relationships among related objects in the scene, which in this article is modeled as a joint probability distribution for all possible locations of the semantic object class. The proposed framework includes two variants of a Correlation Information (CECI) model for learning probability distributions: (i) a baseline approach based on a Graph Convolutional Network, and (ii) a neuro-symbolic extension that integrates a spatial ontology based on Large Language Models (LLMs). Furthermore, this article provides a detailed description of the dataset generation process for such tasks. Finally, the framework has been validated through multiple runs on simulated data, as well as in a real-world indoor environment, demonstrating its ability to spatially interpret scenes across different room types. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/f0tqtPVFZ2A

In this article, we address the problem of collaborative task assignment, sequencing, and multi-agent pathfinding (TSPF), where a team of agents must visit a set of task locations without collisions while minimizing flowtime. TSPF incorporates agent-task compatibility constraints and ensures that all tasks are completed. We propose a Conflict-Based Search with Task Sequencing (CBS-TS), an optimal and complete algorithm that alternates between finding new task sequences and resolving conflicts in the paths of current sequences. CBS-TS uses a mixed-integer linear program (MILP) to optimize task sequencing and employs Conflict-Based Search (CBS) with Multi-Label A* (MLA*) for collision-free path planning within a search forest. By invoking MILP for the next-best sequence only when needed, CBS-TS efficiently limits the search space, enhancing computational efficiency while maintaining optimality.We compare the performance of our CBS-TS against Conflict-based Steiner Search (CBSS), a baseline method that, with minor modifications, can address the TSPF problem. Experimental results demonstrate that CBS-TS outperforms CBSS in most testing scenarios, achieving higher success rates and consistently optimal solutions, whereas CBSS achieves near-optimal solutions in some cases. The supplementary video is available at https://youtu.be/QT8BYgvefmU.

The process of object search and relocation in an indoor environment, while intuitive for humans, remains a complex challenge for robots. Enabling robots to perform this task autonomously could have a substantial impact towards automation in both domestic and industrial settings. In this article, assuming a familiar environment, a set of target objects with their desired locations, and a robot with limited carrying capacity, we propose a novel methodology for object search and relocation. Given the human-like intuition exhibited by modern large language models (LLMs), they can be leveraged to guide object localization based on environmental context. Our approach integrates LLM-based prediction with graph-based path planning to create a human-like iterative search and relocation framework. The framework consists of an LLM predictor that suggests likely object locations (along with a likelihood score) and an adaptive path planner that dynamically updates the robot’s future path as new information becomes available during the search process. Prior relevant literature that employs LLM inference in indoor environments primarily focuses on assigning new or misplaced objects to appropriate locations. The aspect of enabling a search for a set of missing objects and planning their relocation to desired locations sets this article apart from prior literature. We compare our method to a patrol-based baseline with respect to the distance traversed by the robot in completing the search and relocation mission. In a medium sized indoor environment we demonstrate that it outperforms the baseline on an average by 31.2%.

This paper presents a novel high-level task planning and optimal coordination framework for autonomous masonry construction, using a team of heterogeneous aerial robotic workers, consisting of agents with separate skills for brick placement and mortar application. This introduces new challenges in scheduling and coordination, particularly due to the mortar curing deadline required for structural bonding and ensuring the safety constraints among UAVs operating in parallel. To address this, an automated pipeline generates the wall construction plan based on the available bricks while identifying static structural dependencies and potential conflicts for safe operation. The proposed framework optimizes UAV task allocation and execution timing by incorporating dynamically coupled precedence deadline constraints that account for the curing process and static structural dependency constraints, while enforcing spatio-temporal constraints to prevent collisions and ensure safety. The primary objective of the scheduler is to minimize the overall construction makespan while minimizing logistics, traveling time between tasks, and the curing time to maintain both adhesion quality and safe workspace separation. The effectiveness of the proposed method in achieving coordinated and time-efficient aerial masonry construction is extensively validated through Gazebo simulated missions. The results demonstrate the framework’s capability to streamline UAV operations, ensuring both structural integrity and safety during the construction process. - A video with the framework is available at https://youtu.be/kGvFGDCUkDQ

In this work, we introduce SPADE, a path planning framework designed for autonomous navigation in dynamic environments using 3D scene graphs. SPADE combines hierarchical path planning with local geometric awareness to enable collision-free movement in dynamic scenes. The framework bifurcates the planning problem into two: (a) solving the sparse abstract global layer plan and (b) iterative path refinement across denser lower local layers in step with local geometric scene navigation. To ensure efficient extraction of a feasible route in a dense multi-task domain scene graphs, the framework enforces informed sampling of traversable edges prior to path-planning. This removes extraneous information not relevant to path-planning and reduces the overall planning complexity over a graph. Existing approaches address the problem of path planning over scene graphs by decoupling hierarchical and geometric path evaluation processes. Specifically, this results in an inefficient replanning over the entire scene graph when encountering path obstructions blocking the original route. In contrast, SPADE prioritizes local layer planning coupled with local geometric scene navigation, enabling navigation through dynamic scenes while maintaining efficiency in computing a traversable route. We validate SPADE through extensive simulation experiments and real-world deployment on a quadrupedal robot, demonstrating its efficacy in handling complex and dynamic scenarios.

In this article, we present the Layered Semantic Graphs (LSG), a novel actionable hierarchical scene graph, fully integrated with a multi-modal mission planner, the FLIE: A First-Look based Inspection and Exploration planner [1]. The novelty of this work stems from aiming to address the task of maintaining an intuitive and multi-resolution scene representation, while simultaneously offering a tractable foundation for planning and scene understanding during an ongoing inspection mission of apriori unknown targets-of-interest in an unknown environment. The proposed LSG scheme is composed of locally nested hierarchical graphs, at multiple layers of abstraction, with the abstract concepts grounded on the functionality of the integrated FLIE planner. Furthermore, LSG encapsulates real-time semantic segmentation models that offer extraction and localization of desired semantic elements within the hierarchical representation. This extends the capability of the inspection planner, which can then leverage LSG to make an informed decision to inspect a particular semantic of interest. We also emphasize the hierarchical and semantic path-planning capabilities of LSG, which could extend inspection missions by improving situational awareness for human operators in an unknown environment. The validity of the proposed scheme is proven through extensive evaluations of the proposed architecture in simulations, as well as experimental field deployments on a Boston Dynamics Spot quadruped robot in urban outdoor environment settings.

The fifth generation (5G) cellular network technology is mature and increasingly utilized in many industrial and robotics applications, while an important functionality is the advanced Quality of Service (QoS) features. Despite the prevalence of 5G QoS discussions in the related literature, there is a notable absence of real-life implementations and studies concerning their application in time-critical robotics scenarios. This article considers the operation of time-critical applications for 5G-enabled unmanned aerial vehicles (UAVs) and how their operation can be improved by the possibility to dynamically switch between QoS data flows with different priorities. As such, we introduce a robotics oriented analysis on the impact of the 5G QoS functionality on the performance of 5G-enabled UAVs. Furthermore, we introduce a novel framework for the dynamic selection of distinct 5G QoS data flows that is autonomously managed by the 5G-enabled UAV. This problem is addressed in a novel feedback loop fashion utilizing a probabilistic finite state machine (PFSM). Finally, the efficacy of the proposed scheme is experimentally validated with a 5G-enabled UAV in a real-world 5G stand-alone (SA) network. https://www.youtube.com/watch?v=lWtMOlVMEFI&t=1s

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method’s online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time.

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which an adaptive controller tracks to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of Belief Scene Graphs (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of unseen-objects.For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/hsGlSCa12iY

This article introduces an experimental emulation of a novel chunk-based flexible multi-DoF aerial 3D printing framework. The experimental demonstration of the overall autonomy focuses on precise motion planning and task allocation for a UAV, traversing through a series of planned space-filling paths involved in the aerial 3D printing process without physically depositing the overlaying material. The flexible multi-DoF aerial 3D printing is a newly developed framework and has the potential to strategically distribute the envisioned 3D model to be printed into small, manageable chunks suitable for distributed 3D printing. Moreover, by harnessing the dexterous flexibility due to the 6 DoF motion of UAV, the framework enables the provision of integrating the overall autonomy stack, potentially opening up an entirely new frontier in additive manufacturing. However, it’s essential to note that the feasibility of this pioneering concept is still in its very early stage of development, which yet needs to be experimentally verified. Towards this direction, experimental emulation serves as the crucial stepping stone, providing a pseudo mockup scenario by virtual material deposition, helping to identify technological gaps from simulation to reality. Experimental emulation results, supported by critical analysis and discussion, lay the foundation for addressing the technological and research challenges to significantly push the boundaries of the state-of-the-art 3D printing mechanism. - Full mission video available at https://youtu.be/gfZuYCA8jAw

In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet’s methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments1confirming its efficacy and potential for real-world applications.

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems.

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, we present a reactive multi-agent coordination architecture for the management of material flows between production/pickup stages and delivery/drop-off stages, in scenarios such as underground mines and automated factory floors. The pickup and delivery stages are modelled as variable task queues, with no a priori information about the inflow into the production queues. The proposed solution coordinates the movement of a group of mobile agents operating between the two stages in a reactive and scalable manner, so that the material is transported from multiple production queues to multiple delivery queues in a balanced/equalized manner. In such a scenario, centralized planners suffer from low reactivity and poor scaling, as the number of agents and number of queues increases. To overcome this problem, we propose a decentralized approach comprising of two separate auction-based task distribution systems for the production and delivery stages, along with behavior-tree based management of agent autonomy and task bidding. Each auction system tracks the length of production/delivery queues and solves the optimal task assignment, based on the bids submitted by the agents. The agents participate in one of the two auction systems at any given time, based on the status of the behavior tree executing the two-stage tasks. We analytically show that the proposed decentralized auctioning approach along with agent autonomy and bidding managed by behavior trees, offers better scalability and reactiveness compared to the centralized approach. The proposed methodology is experimentally validated in a lab environment, in three illustrative material flow management scenarios, using TurtleBot3 robots as agents.

In this article, we present a control barrier function (CBF)-based control strategy for safe and precise landing of an unmanned aerial vehicle (UAV) on a moving target. The CBF is time-varying, as it depends on the velocity of the landing platform and captures three crucial safety constraints: (a) collision avoidance with the landing platform, (b) precise vertical descent on a narrow landing platform, and (c) ground clearance throughout the landing maneuver. The proposed CBF’s parameters can be adjusted to set the desired width and height of the descending cone. A quadratic programbased CBF safety filter is designed, which takes a nominal position tracking control input and yields a minimally invasive control input that enforces the safety constraints throughout the landing maneuver. The controller’s feasibility is analyzed and its performance is validated through multiple experiments using a quadrotor UAV and an unmanned ground vehicle.

This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object’s inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://youtu.be/BDCMVx2GiQE

This work presents EAT (Environment Agnostic Traversability for Reactive Navigation) a novel framework for traversability estimation in indoor, outdoor, subterranean (SubT) and other unstructured environments. The architecture provides updates on traversable regions online during the mission, adapts to varying environments, while being robust to noisy semantic image segmentation. The proposed framework considers terrain prioritization based on a novel decay exponential function to fuse the semantic information and geometric features extracted from RGB-D images to obtain the traversability of the scene. Moreover, EAT introduces an obstacle inflation mechanism on the traversability image, based on mean-window weighting module, allowing to adapt the proximity to untraversable regions. The overall architecture uses two LRASPP MobileNet V3 large Convolutional Neural Networks (CNN) for semantic segmentation over RGB images, where the first one classifies the terrain types and the second one classifies see-through obstacles in the scene. Additionally, the geometric features profile the underlying surface properties of the local scene, extracting normals from depth images. The proposed scheme was integrated with a control architecture in reactive navigation scenarios and was experimentally validated in indoor and outdoor environments as well as in subterranean environments with a Pioneer 3AT mobile robot.

This article introduces a novel collaborative optimal motion planning framework for parallel aerial 3D printing. The proposed novel framework is efficiently capable of handling conflicts between the utilized Unmanned Aerial Vehicles (UAVs), as they follow predefined paths, allowing for a seamless enhancement of aerial 3D printing capabilities by employing multiple UAVs to collaborate in a parallel printing process. The established approach ingeniously formulates UAVs’ motion planning as a multi-constraint optimization problem, ensuring minimal adjustments to their velocities within specified limits. This guarantees smooth and uninterrupted printing while preventing collisions and adhering to the requirements of aerial printing. To substantiate the effectiveness of our proposed motion planning algorithm, an extensive array of simulation studies have been undertaken, encompassing scenarios where multiple UAVs engage in the fabrication of diverse construction shapes. The overall novel concept is being extensively validated in simulations, while the obtained results promise for enhancing the viability and advancing the landscape of aerial additive manufacturing.

Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-k candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments.

In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

This work presents a fully integrated tree-based combined exploration-planning algorithm: exploration-rapidly-exploring random trees (RRT) (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating “where to go next” by considering a tradeoff between maximizing information gain and minimizing the distances traveled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, and real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully robot operating system (ROS) integrated and straightforward to use.

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots.

The future of 3D printing utilizing unmanned aerial vehicles (UAVs) presents a promising capability to revolutionize manufacturing and to enable the creation of large-scale structures in remote and hard-to-reach areas e.g. in other planetary systems. Nevertheless, the limited payload capacity of UAVs and the complexity in the 3D printing of large objects pose significant challenges. In this article we propose a novel chunk-based framework for distributed 3D printing using UAVs that sets the basis for a fully collaborative aerial 3D printing of challenging structures. The presented framework, through a novel proposed optimisation process, is able to divide the 3D model to be printed into small, manageable chunks and to assign them to a UAV for partial printing of the assigned chunk, in a fully autonomous approach. Thus, we establish the algorithms for chunk division, allocation, and printing, and we also introduce a novel algorithm that efficiently partitions the mesh into planar chunks, while accounting for the inter-connectivity constraints of the chunks. The efficiency of the proposed framework is demonstrated through multiple physics based simulations in Gazebo, where a CAD construction mesh is printed via multiple UAVs carrying materials whose volume is proportionate to a fraction of the total mesh volume.

Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This article proposes an innovative approach for change detection in 3D point clouds using deep learned place recognition descriptors and irregular object extraction based on voxel-to-point comparison. The proposed method first aligns the bi-temporal point clouds using a map-merging algorithm in order to establish a common coordinate frame. Then, it utilizes deep learning techniques to extract robust and discriminative features from the 3D point cloud scans, which are used to detect changes between consecutive point cloud frames and therefore find the changed areas. Finally, the altered areas are sampled and compared between the two time instances to extract any obstructions that caused the area to change. The proposed method was successfully evaluated in real-world field experiments, where it was able to detect different types of changes in 3D point clouds, such as object or muck-pile addition and displacement, showcasing the effectiveness of the approach. The results of this study demonstrate important implications for various applications, including safety and security monitoring in construction sites, mapping and exploration and suggests potential future research directions in this field.

This article establishes the novel D+∗, a risk-aware and platform-agnostic heterogeneous global path planner for robotic navigation in complex environments. The proposed planner addresses a fundamental bottleneck of occupancy-based path planners related to their dependency on accurate and dense maps. More specifically, their performance is highly affected by poorly reconstructed or sparse areas (e.g. holes in the walls or ceilings) leading to faulty generated paths outside the physical boundaries of the 3-dimensional space. As it will be presented, D+∗ addresses this challenge with three novel contributions, integrated into one solution, namely: (a) the proximity risk, (b) the modeling of the unknown space, and (c) the map updates. By adding a risk layer to spaces that are closer to the occupied ones, some holes are filled, and thus the problematic short-cutting through them to the final goal is prevented. The novel established D+∗ also provides safety marginals to the walls and other obstacles, a property that results in paths that do not cut the corners that could potentially disrupt the platform operation. D+∗ has also the capability to model the unknown space as risk-free areas that should keep the paths inside, e.g in a tunnel environment, and thus heavily reducing the risk of larger shortcuts through openings in the walls. D+∗ is also introducing a dynamic map handling capability that continuously updates with the latest information acquired during the map building process, allowing the planner to use constant map growth and resolve cases of planning over outdated sparser map reconstructions. The proposed path planner is also capable to plan 2D and 3D paths by only changing the input map to a 2D or 3D map and it is independent of the dynamics of the robotic platform. The efficiency of the proposed scheme is experimentally evaluated in multiple real-life experiments where D+∗ is producing successfully proper planned paths, either in 2D in the use case of the Boston dynamics Spot robot or 3D paths in the case of an unmanned areal vehicle in varying and challenging scenarios.

Edge computing is becoming more and more popular among researchers who seek to take advantage of the edge resources and the minimal time delays, in order to run their robotic applications more efficiently. Recently, many edge architectures have been proposed, each of them having their advantages and disadvantages, depending on each application. In this work, we present two different edge architectures for controlling the trajectory of an Unmanned Aerial Vehicle (UAV). The first architecture is based on docker containers and the second one is based on kubernetes, while the main framework for operating the robot is the Robotic Operating System (ROS). The efficiency of the overall proposed scheme is being evaluated through extended simulations for comparing the two architectures and the overall results obtained.

To deploy Micro Aerial Vehicles (MAVs) in real-world applications, there is a need for online methods to cope with uncertainties in localization and external disturbances. In this article, we propose a set of novel real-time embedded Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimation (NMHE) modules for MAV based external disturbance rejection. The NMPC and NMHE are based on the dynamic model of the MAV, thus, avoiding the need for system identification and creating specific aerodynamic models, a benefit that results in a generic solution capable of being independent of the type of the MAVs. As it will be presented, the NMHE estimates the external forces, while the NMPC generates thrust and attitude commands for the low-level controller to compensate the various disturbances that could occur, such as wind gusts, tethered payload, and varying center of gravity. The proposed method is evaluated extensively in multiple experimental results that include the scenarios of position hold against an actuating wind-wall, adding payload, and changing the MAV’s arm configurations.

Robots’ simultaneous relative pose estimation has become an essential step in most robotic-oriented problems, such as map merging, collision avoidance, path planning, and multi-Simultaneous Localization and Mapping (SLAM). This article addresses the problem of 3D and ego-centric relative pose estimation for a team of robots equipped with Ultra WideBand (UWB) nodes. More specifically, the article introduces a novel optimization framework to obtain pose information based on the embodiment of UWB ranges, without relying on any fixed external infrastructure configuration of UWB anchors on the surrounding environment. In the proposed method, we demonstrate the validity through the utilization of a Micro Aerial Vehicle (MAV) and a ground vehicle that are equipped with multiple UWB transceivers, and each platform simultaneously acts as a based anchor for the other platform for extracting an ego-centric position estimation of the UWB nodes. Additionally, for the pose estimation, the obtained information is fused with the onboard Inertial Measurement Unit (IMU) measurements on each of the considered robotic platforms. Finally, the efficacy of the proposed theoretical framework is evaluated in multiple experiments, where the aerial and ground platforms are simultaneously and separately navigating, and the ego-centric collaborative pose-estimation is compared with a VICON ground truth positioning system.

This article establishes the Exploration-RRT algorithm: A novel general-purpose combined exploration and path planning algorithm, based on a multi-goal Rapidly-Exploring Random Trees (RRT) framework. Exploration-RRT (ERRT) has been specifically designed for utilization in 3D exploration missions, with partially or completely unknown and unstructured environments. The novel proposed ERRT is based on a multi-objective optimization framework and it is able to take under consideration the potential information gain, the distance travelled, and the actuation costs, along trajectories to pseudorandom goals, generated from considering the on-board sensor model and the non-linear model of the utilized platform. In this article, the algorithmic pipeline of the ERRT will be established and the overall applicability and efficiency of the proposed scheme will be presented on an application with an Unmanned Aerial Vehicle (UAV) model, equipped with a 3D lidar, in a simulated operating environment, with the goal of exploring a completely unknown area as efficiently and quickly as possible.

In this article we propose a distributed collision avoidance scheme for multi-agent unmanned aerial vehicles (UAVs) based on nonlinear model predictive control (NMPC), where other agents in the system are considered as dynamic obstacles with respect to the ego agent. Our control scheme operates at a low level and commands roll, pitch and thrust signals at a high frequency, each agent broadcasts its predicted trajectory to the other ones, and we propose an obstacle prioritization scheme based on the shared trajectories to allow up-scaling of the system. The NMPC problem is solved using an embedded solver generated by Optimization Engine (OpEn) where PANOC is combined with an augmented Lagrangian method to compute collision-free trajectories. We evaluate the proposed scheme in several challenging laboratory experiments for up to ten aerial agents, in dense aerial swarms.

This article focuses on a spacecraft trajectory planning algorithm that allows observation of multiple site locations on the asteroid surface, while avoiding any collision with debris objects trapped in the asteroid's gravity field. Asteroids provide a challenging target for satellite based visual coverage missions, since they are partially illuminated, rotating, irregular shaped celestial bodies with a low but also irregular gravity field. For addressing this problem, an optimization approach for visual coverage is proposed with an objective to determine the sequence of the imaging site locations and the associated safe and fuel efficient trajectories, while considering rotational dynamics of the asteroid, changing illumination condition for each site, irregular gravity constraints of the asteroid and the safe separation distance from the moving debris object. Numerical simulations are performed to demonstrate the ability of the trajectory planner to ensure successful optimal coverage of all the desired asteroid site locations. letterpaper, 10 pt.

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.

Over the last years, there is a growing need for climbing robots performing autonomous inspection tasks of large-scale infrastructure, to reduce inspection time and the overall operation costs. Thickness measurement, visual inspection, fault detection, etc. are a few examples of inspection and maintenance applications that could be performed autonomously by robotic platforms like climbing robots. One of the main challenges of inspecting large infrastructures, is the problem of path planning, as the path should be optimal to reduce the inspection time, incorporate sensor properties, and account for important robot requirements such as power supply cabling. This article proposes a novel path planner targeting inspection tasks, where the restrictions posed by the cabling on a Vortex Robot (VR), the attached sensor, and the properties of the scanned surfaces are taken into consideration. The presented framework is successfully evaluated in multiple closed-loop experiments, under different surface inclinations and VR orientations to demonstrate the efficacy of the path planning and control scheme.

The aim of this article is the formulation of a switching model predictive control framework for the case of a foldable quadrotor with the ability to retain the overall control quality during online structural reformations. The majority of the related scientific publications consider fixed morphology of the aerial vehicles. Recent advances in mechatronics have brought novel considerations for generalized aerial robotic designs with the ability to alter their morphology in order to adapt to their environment, thus enhancing their capabilities. Simulation results are provided to prove the efficacy of the selected control scheme.

Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)‐c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.

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.

This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aerial Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.

This article presents a Convolutional Neural Network (CNN) method to enable autonomous navigation of low-cost Micro Aerial Vehicle (MAV) platforms along dark underground mine environments. The proposed CNN component provides online heading rate commands for the MAV by utilising the image stream from the on-board camera, thus allowing the platform to follow a collision-free path along the tunnel axis. A novel part of the developed method consists of the generation of the data-set used for training the CNN. More specifically, inspired from single image haze removal algorithms, various image data-sets collected from real tunnel environments have been processed offline to provide an estimation of the depth information of the scene, where ground truth is not available. The calculated depth map is used to extract the open space in the tunnel, expressed through the area centroid and is finally provided in the training of the CNN. The method considers the MAV as a floating object, thus accurate pose estimation is not required. Finally, the capability of the proposed method has been successfully experimentally evaluated in field trials in an underground mine in Sweden.

This article establishes a Constrained Finite Time Optimal Control (CFTOC) design for unknown, discrete-time systems with parametric uncertainty, whose dynamics are identified via Set-Membership Identification (SMI). The control scheme is composed of: a) the SMI module, which identifies the dynamics of the system through a Weighted Recursive Least Squares (WRLS) algorithm, and b) the CFTO controller, which can be online updated based on the SMI provided information, hence, providing the ability to adapt online the overall control framework based on the overall confidence intervals from the SMI. The proposed scheme has been adapted for the case of controlling a Vortex Actuator, based on an Electric Ducted Fan and utilized for wall climbing robotic applications. Simulation results are provided to demonstrate the efficacy of the overall proposed scheme.

In this article, the critical adhesion force and achievable payload of a Vortex Actuator (VA) are analyzed under 3-DOF surface rotations. A model-based control scheme is later proposed, with the goal of maintaining VA adhesion when immobilized, while limiting the power consumption and counteracting disturbances leading to Center-of-Mass (CoM) variations. Finally, the model-based control scheme is experimentally evaluated with the VA prototype on a flat surface under linear motions and rotations, thus supporting the incorporation of the VA in Climbing Robots (CRs) for inspection and maintenance of both stationary and moving surfaces.

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).

This work presents an aerial tool towards the autonomous cooperative coverage and inspection of a large scale 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). In the presented approach the UAVs are relying only on their onboard computer and sensory system, deployed for inspection of the 3D structure. In this application each agent covers a different part of the scene autonomously, while avoiding collisions. The autonomous navigation of each platform on the designed path is enabled by the localization system that fuses Ultra Wideband with inertial measurements through an Error- State Kalman Filter. The visual information collected from the aerial team is collaboratively processed to create the 3D model. The performance of the overall setup has been experimentally evaluated in realistic wind turbine inspection experiments, providing dense 3D reconstruction of the inspected structures.

This article presents an integrated vision-based guiding system for aerial manipulation. More specifically, a 4 DoF planar dexterous manipulator, with a stereo camera attached on the end-effector, is endowed to a multirotor aerial platform enabling active manipulation capabilities. The proposed novel approach combines a visual processing scheme for object detection and tracking, as well as a manipulator positioning for allowing the aerial platform to approach the surface of interaction efficiently. In the developed scheme, the object detection is based on correlation filters to track the target robustly, while the depth information, from the stereo camera on board the manipulator, is used to extract the centroid of the manipulated object, compute its relative configuration with respect to the UAV and align the end-effector properly with the grasping point. The effectiveness of the proposed scheme is demonstrated in multiple experimental trials and simulations, highlighting it's applicability towards autonomous aerial manipulation.

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.

The aim of this paper is to establish a generalized parameter estimation scheme to online estimate the Center of Gravity (COG) for multirotors, while using a geometric controller to perform position tracking for applications in aerial manipulation. The proposed scheme is developed so the controller uses the estimated COG to compensate and remove constant offset in the position tracking. The efficiency and validity of the proposed parameter estimation and compensation scheme is proved through two experimental evaluations, one when step changes to the COG are applied and one tracking experiment where a compact aerial manipulator is attached to the multirotor and performs sweeping motions.

In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three “problem transformation” methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.

This article presents a simple method for the detection and diagnosis of bearing faults, by fusing the information coming from two accelerometers. The method relies on three simple and intuitive features, extracted from the data coming from accelerometers placed at two different locations of the system under investigation. Our preliminary results indicate that by using simple statistical measures, such as the elements of the covariance matrix of the two sensors, faults at an early stage can be detected. In the proposed scheme, the extracted features are fed to a k-nearest neighbor classifier for diagnosis purposes or to an ensemble of one-class detectors, if only the information from normal situation is available. As it is proven, based on experimental results, in both scenarios a remarkably high detection/diagnostic performance is achieved.

In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

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.

The aim of this article is to present a novel quaternion based control scheme for the attitude control problem of a quadrotor and experimentally evaluate its performance. A quaternion is a hyper complex number of rank 4 that can be utilized to avoid the inherent geometrical singularity when representing rigid body dynamics with Euler angles or the complexity of having coupled differential equations with the Direction Cosine Matrix (DCM). In the presented approach the novel contributions consist of: a) the quadrotor's attitude model and b) the proposed non-linear Proportional squared (P2) control algorithm, which have been proposed and experimentally evaluated fully in the quaternion space, without any transformations nor calculations in the Euler angle nor the DCM spaces. The established control scheme is combined with a quaternion based Madwick Complementary filter for estimating the attitude quadrotor's responses. Multiple experimental results, including the case where external disturbances are acting on the quadrotor, are being presented for proving the efficiency and the robustness of the proposed novel quaternion based controller.

Implementation of unsupervised induction motor condition monitoring systems has drawn an increasing attention recently among motor drives manufacturers. In the case of soft-starters the possibility of incorporating fault detection features to their conventional functions provides an added value to those elements. Design and development of advanced algorithms that are able to automatically detect and alert about possible failures without requiring continuous human inspection is a challenging research goal. In this paper, an algorithm for the automatic detection of rotor damages in induction motors in the case of soft starting is proposed. The twofold approach relies, first, on the application of a time-frequency transform to the starting current signal and, second, on a pattern recognition stage based on the treatment of the time-frequency representation as a symbolic sequence. The innovation of this work is the implementation of the proposed approach for the automatic detection of rotor cage faults in soft-started motors. The experimental results prove the usefulness of the approach for the automatic detection of such faults and its potential for possible future implementation in soft-started machines.

In this article a method for the detection of one, two, and three broken bars in induction motors under full load condition is presented. The proposed methodo is based on current envelope analysis. The information obtained from the envelope current is valuable in manifesting and validating the presence of a broken bar fault, since it contains important information about the existence of a fault as well as its severity. The proposed method mainly focuses on the case of steady-state operation under full load. In the established fault diagnosis scheme six features are extracted from the envelope of the current and after the application of a Principal Component Analysis stage are fed to a classifier to perform the diagnosis. Three different classifiers, a linear, a quadratic and a nearest neighbour are investigated for the final stage of the diagnosis. The presented approach manifested promising results using experimental data.

This article presents an experimental evaluation of a modified obstacle based artificial potential field algorithm for an off-road mobile robot. The first contribution of the presented approach concerns the transformation of the artificial potential field method for the guidance of the vehicle and obstacle avoidance, in order to make it suitable for utilising a visual feedback. The visual feedback is relying on a depth image, provided by the low cost kinect sensor. The second contribution concerns the proposal of a novel scheme for the identification and perception of obstacles. Based on the proposed methodology, the vehicle is capable of categorising the obstacles based on their height in order to alter the calculated forces, for enabling a cognitive decision regarding their avoidance or the driving over them, by utilising the robot's off road capabilities. The proposed scheme is highly suggested for off road robots, since in the normal cases, the existence of small rocks, branches, etc. can be accidentally identified as obstacles that could make the robot to avoid them or block its further movement. The performance of the proposed modified potential field algorithm has been experimentally applied and evaluated in multiple robotic exploration scenarios, where from the obtained results the efficiency and the advantages of such a modified scheme have been depicted.

In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the frequency domain, our method operates in the time domain. The scheme is based on the use of a Principal Component Analysis (PCA) fault/anomaly detector. PCA is applied on the three stator currents to subsequently calculate the Q statistic which is employed for detecting the presence/absence of a fault. The efficiency of the proposed scheme was experimentally evaluated using different fault severity levels, ranging from 1/4 of a broken bar to three broken bars. The obtained results indicate that the method can detect the caused asymmetry with a very restricted amount of data.

In this article a fault detection scheme for different percentage of stator winding short circuit is presented for three phase induction motors. In the examined case, the induction motor in the faulty and healthy case has been transformed in the two phase (q−d) model. The model has been identified by the utilization of a Least Squares Set Membership Identification (SMI) algorithm, where additional to the identified parameters, confidence intervals can be also calculated, based on a priori knowledge for the corrupting measurement noise. The identified confidence intervals in an μ–dimensional space can be represented as hyper–ellipsoids having as a center the identified parameters’ vector. The novelty of this article stems from the proposal of a fast and geometrical based scheme, which relies on the calculation of the distance among centers of hyper–ellipsoids and the corresponding intersection in each iteration of the identification procedure. Detailed analysis of the proposed fault detection strategy, as also extended simulation results are being presented that prove the efficiency of the suggested scheme.

This article proposes a novel online dynamic smooth path planning scheme based on a bug like modified path planning algorithm for an articulated vehicle under limited and sensory reconstructed surrounding static environment. In the general case collision avoidance techniques can be performed by altering the articulated steering angle to drive the front and rear parts of the articulated vehicle away from the obstacles. In the presented approach factors such as the real dynamics of the articulated vehicle, the initial and the goal configuration (displacement and orientation), minimum and total travel distance between the current and the goal points, and the geometry of the operational space are taken under consideration to calculate the update on the future way points for the articulated vehicle. In the sequel the produced path planning is being online and iteratively smoothen by the utilization of Bezier lines before producing the necessary rate of change for the vehicle’s articulated angle. The efficiency of the proposed scheme is being evaluated by multiple simulation studies.

This paper proposed a new technique for an experimental evaluation of a broken rotor bar fault detection based on Uncertainty Bounds violation. The novelty of this article stems from the establishment and the experimental evaluation of fault detection scheme being able to detect faults at the beginning of its occurrence, based on Set Membership Identification and novel proposed boundary violation rules for the identified motor's parameters. By the utilization of the SMI technique, the simplified equivalent model of the induction motor is being identified during the steady state operation (non-fault case), while at the same time safety bounds for the identified variables are being provided, based on an a priori defined corrupting additive noise. On the event of a fault, specific fault detection conditions are being proposed that can capture the fault of a broken bar. Detailed analysis of the proposed approach as also extended experimental results are being presented that prove the efficiency of the proposed scheme.

This article is focusing on the problem of path following for an articulated vehicle under varying velocities and slip conditions. The proposed control architecture consists of a switching control scheme based on multiple model predictive controllers, fine tuned for dealing with different operating speeds and slip angles. In the presented analysis for the non-holonomic articulated vehicle, the corresponding kinematic model is being transformed into an error dynamics model, which is linearized around multiple nominal slip angle cases and various operating speeds. The existence of the slipping and varying speed has a significant effect on the vehicle's path following capability and can significantly deteriorate the performance of the overall control scheme. Based on the derived multiple dynamics modeling, the current slip and vehicle's speed are being considered as the signal selector for the proposed switching model predictive control scheme. The efficacy of the proposed controller is being evaluated by an extended set of simulation results.

The aim of this article is to present a fault diagnosis scheme for the case of squirrel-cage Three Phase Induction Motors based on uncertainty bounds violation conditions. The suggested scheme has the capability to diagnose two types of faults: a) broken rotor bar and b) short circuit in stator winding. The fault diagnosis is being performed through a two steps procedure. In the first step the parameters of the healthy induction motor are being identified by utilizing a Set Membership Identification approach, where corresponding uncertainty bounds are also being provided. In the second step, specific proposed bound violation conditions for the fault detection and fault diagnosis are being on-line evaluated during a sliding time window. Multiple simulation results are being presented that prove the efficacy of the proposed scheme towards fault detection and fault diagnosis.

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 the design and the experimental verification of a Constrained Finite Time Optimal (CFTO) control scheme for the attitude control of an Unmanned Quadrotor Helicopter (UqH) subject to wind gusts is being presented. In the proposed design the UqH has been modeled by a set of Piecewise Affine (PWA) linear equations while the wind gusts effects are embedded in the system model description as the affine terms. In this approach the switching among the PWA model descriptions are ruled by the rate of the rotation angles. In the design of the stabilizing CFTO-controller both the magnitude of external disturbances (worst case applied wind gust), and the mechanical constraints of the UqH such as maximum thrust in the rotors and UqH's angles rate are taken under consideration in order to design an off-line controller that could rapidly be applied to a UqH in a form of a look-up table. The proposed control scheme is applied in experimental studies and multiple test-cases are presented that prove the efficiency of the proposed scheme.