arXiv subjects

Stjepan Bogdan

Publications and source records attributed to Stjepan Bogdan.

31 records · Page 2Linked to original sources

arXiv2022

Dynamic Median Consensus for Marine Multi-Robot Systems Using Acoustic Communication

In this paper, we present a dynamic median consensus protocol for multi-agent systems using acoustic communication. The motivating target scenario is a multi-agent system consisting of underwater robots acting as intelligent sensors, applied to continuous monitoring of the state of a marine environment. The proposed protocol allows each agent to track the median value of individual measurements of all agents through local communication with neighbouring agents. Median is chosen as a measure robust to outliers, as opposed to average value, which is usually used. In contrast to the existing consensus protocols, the proposed protocol is dynamic, uses a switching communication topology and converges to median of measured signals. Stability and correctness of the protocol are theoretically proven. The protocol is tested in simulation, and accuracy and influence of protocol parameters on the system output are analyzed. The protocol is implemented and validated by a set of experiments on an underwater group of robots comprising of aMussel units. This experimental setup is one of the first deployments of any type of consensus protocol for an underwater setting. Both simulation and experimental results confirm the correctness of the presented approach.

cs.RO↗

arXiv2022

Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks

In this paper, we study a cooperative aerial-ground robotic team and its application to the task of automated construction. We propose a solution for planning and coordinating the mission of constructing a wall with a predefined structure for a heterogeneous system consisting of one mobile robot and up to three unmanned aerial vehicles. The wall consists of bricks of various weights and sizes, some of which need to be transported using multiple robots simultaneously. To that end, we use hierarchical task representation to specify interrelationships between mission subtasks and employ effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We evaluate the performance of the method under different optimization criteria and validate the solution in the realistic Gazebo simulation environment.

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arXiv2022

Vision-based system for a real-time detection and following of UAV

In this paper a vision-based system for detection, motion tracking and following of Unmanned Aerial Vehicle (UAV) with other UAV (follower) is presented. For detection of an airborne UAV we apply a convolutional neural network YOLO trained on a collected and processed dataset of 10,000 images. The trained network is capable of detecting various multirotor UAVs in indoor, outdoor and simulation environments. Furthermore, detection results are improved with Kalman filter which ensures steady and reliable information about position and velocity of a target UAV. Preserving the target UAV in the field of view (FOV) and at required distance is accomplished by a simple nonlinear controller based on visual servoing strategy. The proposed system achieves a real-time performance on Neural Compute Stick 2 with a speed of 20 frames per second (FPS) for the detection of an UAV. Justification and efficiency of the developed vision-based system are confirmed in Gazebo simulation experiment where the target UAV is executing a 3D trajectory in a shape of number eight.

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arXiv2022

Exploiting Null Space in Aerial Manipulation through Model-In-The-Loop Motion Planning

This paper presents a method for aerial manipulator end-effector trajectory tracking by encompassing dynamics of the Unmanned Aerial Vehicle (UAV) and null space of the manipulator attached to it in the motion planning procedure. The proposed method runs in phases. Trajectory planning starts by not accounting for roll and pitch angles of the underactuated UAV system. Next, we propose simulating the dynamics on such a trajectory and obtaining UAV attitude through the model. The full aerial manipulator state obtained in such a manner is further utilized to account for discrepancies in planned and simulated end-effector states. Finally, the end-effector pose is corrected through the null space of the manipulator to match the desired end-effector pose obtained in trajectory planning. Furthermore, we have applied the TOPP-RA approach on the UAV by invoking the differential flatness principle. Finally, we conducted experimental tests to verify effectiveness of the planning framework.

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arXiv2022

Sim2Air - Synthetic aerial dataset for UAV monitoring

In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photorealism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic data with randomized textures confirmed our approach by increasing the mAP value (17 and 3.7 percentage points for YOLO; 20 and 1.1 percentage points for Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV images with motion blur. Testing on different domains, we conclude that the more the generalisation ability is put to the test, the more apparent are the advantages of the shape-based representation.

cs.CV↗

arXiv2022

Autonomous, Mobile Manipulation in a Wall-building Scenario: Team LARICS at MBZIRC 2020

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.

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arXiv2021

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfill the mission, which directly feed into the next stage of the method. We propose a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The method is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the method's ability to adapt the planning strategy depending on the available robots and the given optimization criteria.

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arXiv2021

A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration

In this paper, we address the problem of autonomous exploration of unknown environments with an aerial robot equipped with a sensory set that produces large point clouds, such as LiDARs. The main goal is to gradually explore an area while planning paths and calculating information gain in short computation time, suitable for implementation on an on-board computer. To this end, we present a planner that randomly samples viewpoints in the environment map. It relies on a novel and efficient gain calculation based on the Recursive Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner uses a cuboid-based evaluation method that results in an enviably short computation time. To reduce the overall exploration time, we also use a dead end resolving strategy that allows us to quickly recover from dead ends in a challenging environment. Comparative experiments in simulation have shown that our approach outperforms the current state-of-the-art in terms of computational efficiency and total exploration time. The video of our approach can be found at https://www.youtube.com/playlist?list=PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl.

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arXiv2021

A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles

This paper investigates the use of LiDAR SLAM as a pose feedback for autonomous flight. Cartographer, LOAM and HDL graph SLAM are first introduced on a conceptual level and later tested for this role. They are first compared offline on a series of datasets to see if they are capable of producing high-quality pose estimates in agile and long-range flight scenarios. The second stage of testing consists of integrating the SLAM algorithms into a cascade PID UAV control system and comparing the control system performance on step excitation signals and helical trajectories. The comparison is based on step response characteristics and several time integral performancecriteria as well as the RMS error between planned and executed trajectory.

cs.RO↗

arXiv2021

Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended Kalman filter (ES -EKF) in the context of multi-sensor fusion. Its implementation is adapted to fuse measurements from multiple sensor sources and the state model is extended to account for sensor drift and possible calibration inaccuracies. Experimental validation is performed by fusing IMU data obtained from the PixHawk 2.1 flight controller with pose measurements from LiDAR Cartographer SLAM, visual odometry provided by the Intel T265 camera and position measurements from the Pozyx UWB indoor positioning system. The estimated odometry from ES-EKF is validated against ground truth data from the Optitrack motion capture system and its use in a position control loop to stabilize the UAV is demonstrated.

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arXiv2021

Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.

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arXiv2021

Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as path planning and obstacle avoidance.

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arXiv2020

A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration

In this paper we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors such as lidars, which produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by clustering of frontier points and selecting the best frontier point to be explored. Compared to existing frontier-based approaches, the planner is more scalable, i.e. it requires less time for the same data set size while ensuring similar exploration time. Performance is achieved by not relying on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we use the properties of the Octree environment representation, which allows easy analysis with different resolutions. The planner is tested in the simulation environment and in an outdoor test area with a UAV equipped with a lidar sensor. The results show the advantages of the approach.

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