Kostas Alexis

Norwegian University of Science and Technology | NTNU · Department of Engineering Cybernetics

Enabling robot autonomy in complex environments for mission critical application requires robust state estimation. Particularly under conditions where the exteroceptive sensors, which the navigation depends on, can be degraded by environmental challenges thus, leading to mission failure. It is precisely in such challenges where the potential for Fr...

This study introduces a novel compliant and morphologically aware aerial robot called Morphy. The system is small (primary dimension 25.2 cm), lightweight (260 g), and agile (thrust‐to‐weight ratio of 3.3) while simultaneously integrating sensorized flexible joints in its arms. These elastic joints unlock the potential of experiencing flexible defo...

Motion planning for autonomous active perception in cluttered environments remains a challenging problem, requiring real-time solutions that both maximize safety and achieve a desired behavior. In dynamic underwater environments, such as in aquaculture operations, the robots are additionally expected to deal with state and motion uncertainty and er...

This paper addresses the modeling and attitude control of jumping quadrupeds in low-gravity environments. First, a convex decomposition procedure is presented to generate high-accuracy and low-cost collision geometries for quadrupeds performing agile maneuvers. A hierarchical control architecture is then investigated, separating torso orientation t...

This paper presents a general refractive camera model and online co-estimation of odometry and the refractive index of unknown media. This enables operation in diverse and varying refractive fluids, given only the camera calibration in air. The refractive index is estimated online as a state variable of a monocular visual-inertial odometry framewor...

Enabling robot autonomy in complex environments for mission critical application requires robust state estimation. Particularly under conditions where the exteroceptive sensors, which the navigation depends on, can be degraded by environmental challenges thus, leading to mission failure. It is precisely in such challenges where the potential for FM...

Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments. A novel methodology to jointly learn CBFs and corresponding safe cont...

The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy. This redundancy unnecessarily slows down the estimation pipeline and may cause drift under real-time constraints. Such undue latency becomes a bottleneck for resource-constrained robots (especially UAVs), requiring minimal...

This document outlines the ARL Educational Robotic Autonomy Environment v0.1 (ARL-Edu v0.1) which solely relies on open-source projects of our lab and of the community. ARL-Edu offers an immediate opportunity for the students to comprehensively experiment with autonomy issues for both aerial and ground robotic systems. Below, a relevant summary of...

Autonomous navigation and information gathering in challenging environments are demanding since the robot’s sensors may be susceptible to non-negligible noise, its localization and mapping may be subject to significant uncertainty and drift, and performing collision-checking or evaluating utility functions using a map often requires high computatio...

This paper contributes a novel learning-based method for aggressive task-driven compression of depth images and their encoding as images tailored to collision prediction for robotic systems. A novel 3D image processing methodology is proposed that accounts for the robot’s size in order to appropriately “inflate” the obstacles represented in the dep...

This paper contributes a novel learning-based method for aggressive task-driven compression of depth images and their encoding as images tailored to collision prediction for robotic systems. A novel 3D image processing methodology is proposed that accounts for the robot's size in order to appropriately "inflate" the obstacles represented in the dep...

This paper contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both rea...

Employing robotics for inspection operations Kostas Alexis of the Autonomous Robots Lab, Department of Engineering Cybernetics at the Norwegian University of Science and Technology, explores the use of autonomous robotics for inspection operations. Inspection operations maintain a crucial role within our societies. Industrial inspection is essentia...

Developing learning-based methods for navigation of aerial robots is an intensive data-driven process that requires highly parallelized simulation. The full utilization of such simulators is hindered by the lack of parallelized high-level control methods that imitate the real-world robot interface. Responding to this need, we develop the Aerial Gym...

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that togeth...

This paper surveys recent progress and discusses future opportunities for Simultaneous Localization And Mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary explo...

As aerial robots are tasked to navigate environments of increased complexity, embedding collision tolerance in their design becomes important. In this survey we review the current state-of-the-art within the niche field of collision-tolerant micro aerial vehicles and present different design approaches identified in the literature, as well as metho...

This paper presents a strategy for field estimation and informative path planning towards autonomous mapping and radiological characterization of distributed gamma radiation fields within confined GPS-denied environments using aerial robots. First, an online distributed radiation field estimation and spectroscopic analysis framework is presented wh...

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on l...

This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic a...

This article presents the core technologies and deployment strategies of Team CERBERUS that enabled our winning run in the DARPA Subterranean Challenge finals. CERBERUS is a robotic system-of-systems involving walking and flying robots presenting resilient autonomy, as well as mapping and navigation capabilities to explore complex underground envir...

This work contributes a marsupial robotic system-of-systems involving a legged and an aerial robot capable of collaborative mapping and exploration path planning that exploits the heterogeneous properties of the two systems and the ability to selectively deploy the aerial system from the ground robot. Exploiting the dexterous locomotion capabilitie...

Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In...

This work presents the design, hardware realization, autonomous exploration and object detection capabilities of RMF-Owl, a new collision-tolerant aerial robot tailored for resilient autonomous subterranean exploration. The system is custom built for underground exploration with focus on collision tolerance, resilient autonomy with robust localizat...

This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated...

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particular...

Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy hard to achieve. This has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response...

In this paper, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computa...

This paper presents the system design, modeling, and control of the Aerial Robotic Chain Manipulator. This new robot design offers the potential to exert strong forces and moments on the environment, carry and lift significant payloads, and simultaneously navigate through narrow corridors. We contribute a hybrid modeling framework to model the syst...

This report accompanies a dataset release on visual and thermal camera data and details a procedure followed to align such multi-modal camera frames in order to provide pixel-level correspondence between the two without using intrinsic or extrinsic calibration information. To achieve this goal we benefit from progress in the domain of multi-modal i...

In this research we present a novel algorithm for background subtraction using a moving camera. Our algorithm is based purely on visual information obtained from a camera mounted on an electric bus, operating in downtown Reno which automatically detects moving objects of interest with the view to provide information for collision avoidance and numb...

This manuscript presents enhancements on our motion-primitives exploration path planning method for agile exploration using aerial robots. The method now further integrates a global planning layer to facilitate reliable large-scale exploration. The implemented bifurcation between local and global planning allows for efficient exploration combined w...

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input...

Autonomous exploration of subterranean environments remains a major challenge for robotic systems. In response, this paper contributes a novel graph-based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large-scale tunnel-like networks and complex multi-branched topologie...

Resilient pose estimation for autonomous systems, and especially small unmanned aerial robots, is one of the core capabilities required for these robots to perform their assigned tasks in a reliable and efficient manner. Different sensing modalities have been utilized for the robot pose estimation process, particularly in GPS-denied environments. H...

This paper presents the design concept, modeling and motion planning solution for the aerial robotic chain. This design represents a configurable robotic system of systems, consisting of multi-linked micro aerial vehicles that simultaneously presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potent...

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving ca...

This paper overviews the system design, modeling and control of the Aerial Robotic Chain. This new design corresponds to a reconfigurable robotic system of systems consisting of multilinked micro aerial vehicles that presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potential of distributed sensin...

In this paper we present an overview of the methods and systems that give rise to a flying robotic system capable of autonomous inspection, surveying, comprehensive multi-modal mapping and inventory tracking of construction sites with high degree of systematicity. The robotic system can operate assuming either no prior knowledge of the environment...

This discussion paper aims to support the argument process for the need to develop a comprehensive science of resilient robotic autonomy. Resilience and its key characteristics relating to robustness, redundancy, and resourcefulness are discussed, followed by a selected - but not exhaustive - list of research themes and domains that are crucial to...

The informative path planning problem is a fundamental problem for autonomous robots in which an algorithm aims to find collision-free and feasible paths that optimize a certain objective (or multiple objectives) and respect imposed system and mission constraints such as a maximum available time budget. In this chapter, the emphasis is put on reced...

In this paper we present an experimental results driven system design to enable more robust and self-deployed wireless communications for robotic systems autonomously operating in underground environments such as mines, caves, and tunnels. Subterranean environments pose severe challenges for wireless communications as wireless signal suffers extra...

In this paper we present a comprehensive solution for autonomous underground mine rescue using aerial robots. In particular, a new class of Micro Aerial Vehicles are equipped with the ability to localize and map in subterranean settings, explore unknown mine environments on their own, and perform detection and localization of objects of interest fo...

This paper presents the design concept, modeling and motion planning solution for the aerial robotic chain. This design represents a configurable robotic system of systems, consisting of multi-linked micro aerial vehicles that simultaneously presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potent...

This paper presents a path planning strategy for the autonomous exploration of subterranean environments. Tailored to the specific challenges and particularities of underground settings, and especially the fact that they often are extremely large in scale, tunnel-like, narrow and multi-branched, the proposed method employs a bifurcated local-and gl...

This work presents an uncertainty-aware path-planning strategy to achieve autonomous aerial robotic exploration of unknown environments while ensuring mapping consistency on-the-go. The planner follows a paradigm of hierarchically optimized objectives, which are executed in receding horizon fashion. Initially, a random tree over the known feasible...

Autonomous navigation of microaerial vehicles in environments that are simultaneously GPS‐denied and visually degraded, and especially in the dark, texture‐less and dust‐ or smoke‐filled settings, is rendered particularly hard. However, a potential solution arises if such aerial robots are equipped with long wave infrared thermal vision systems tha...

This paper presents a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner is structured around a bifurcated local-and global...

In this paper a comprehensive approach to enable resilient robotic autonomy in subterranean environments is presented. Emphasizing on the use of aerial robots to explore underground settings such as mines and tunnels, the presented methods address critical challenges related to extreme sensor degradation, path planning in large-scale, multi-branche...