Autonomous Navigation for Unmanned Aerial Systems - Visual Perception and Motion Planning

L'abstract è presente nell'allegato / the abstract is in the attachmen

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

Enhanced Image-Based Visual Servoing Dealing with Uncertainties

Nowadays, the applications of robots in industrial automation have been considerably increased. There is increasing demand for the dexterous and intelligent robots that can work in unstructured environment. Visual servoing has been developed to meet this need by integration of vision sensors into robotic systems. Although there has been significant development in visual servoing, there still exist some challenges in making it fully functional in the industry environment. The nonlinear nature of visual servoing and also system uncertainties are part of the problems affecting the control performance of visual servoing. The projection of 3D image to 2D image which occurs in the camera creates a source of uncertainty in the system. Another source of uncertainty lies in the camera and robot manipulator's parameters. Moreover, limited field of view (FOV) of the camera is another issues influencing the control performance. There are two main types of visual servoing: position-based and image-based. This project aims to develop a series of new methods of image-based visual servoing (IBVS) which can address the nonlinearity and uncertainty issues and improve the visual servoing performance of industrial robots. The first method is an adaptive switch IBVS controller for industrial robots in which the adaptive law deals with the uncertainties of the monocular camera in eye-in-hand configuration. The proposed switch control algorithm decouples the rotational and translational camera motions and decomposes the IBVS control into three separate stages with different gains. This method can increase the system response speed and improve the tracking performance of IBVS while dealing with camera uncertainties. The second method is an image feature reconstruction algorithm based on the Kalman filter which is proposed to handle the situation where the image features go outside the camera's FOV. The combination of the switch controller and the feature reconstruction algorithm can not only improve the system response speed and tracking performance of IBVS, but also can ensure the success of servoing in the case of the feature loss. Next, in order to deal with the external disturbance and uncertainties due to the depth of the features, the third new control method is designed to combine proportional derivative (PD) control with sliding mode control (SMC) on a 6-DOF manipulator. The properly tuned PD controller can ensure the fast tracking performance and SMC can deal with the external disturbance and depth uncertainties. In the last stage of the thesis, the fourth new semi off-line trajectory planning method is developed to perform IBVS tasks for a 6-DOF robotic manipulator system. In this method, the camera's velocity screw is parametrized using time-based profiles. The parameters of the velocity profile are then determined such that the velocity profile takes the robot to its desired position. This is done by minimizing the error between the initial and desired features. The algorithm for planning the orientation of the robot is decoupled from the position planning of the robot. This allows a convex optimization problem which lead to a faster and more efficient algorithm. The merit of the proposed method is that it respects all of the system constraints. This method also considers the limitation caused by camera's FOV. All the developed algorithms in the thesis are validated via tests on a 6-DOF Denso robot in an eye-in-hand configuration

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

D8.6 OPTIMAI commercialization and exploitation strategy

Deliverable D8.6 OPTIMAI commercialization and exploitation strategy 1 st version is the first version of the OPTIMAI Exploitation Plan. Exploitation aims at ensuring that OPTIMAI becomes sustainable well after the conclusion of the research project period so as to create impact. OPTIMAI intends to develop an industry environment that will optimize production, reducing production line scrap and production time, as well as improving the quality of the products through the use of a variety of technological solutions, such as Smart Instrumentation of sensors network at the shop floor, Metrology, Artificial Intelligence (AI), Digital Twins, Blockchain, and Decision Support via Augmented Reality (AR) interfaces. The innovative aspects: Decision Support Framework for Timely Notifications, Secure and adaptive multi-sensorial network and fog computing framework, Blockchain-enabled ecosystem for securing data exchange, Intelligent Marketplace for AI sharing and scrap re-use, Digital Twin for Simulation and Forecasting, Embedded Cybersecurity for IoT services, On-the-fly reconfiguration of production equipment allows businesses to reconsider quality management to eliminate faults, increase productivity, and reduce scrap. The OPTIMAI exploitation strategy has been drafted and it consists of three phases: Initial Phase, Mid Phase and Final Phase where different activities are carried out. The aim of the Initial phase (M1 to M12), reported in this deliverable, is to have an initial results' definition for OPTIMAI and the setup of the structures to be used during the project lifecycle. In this phase, also each partner's Individual Exploitation commitments and intentions are drafted, and a first analysis of the joint exploitation strategies is being presented. The next steps, leveraging on the outcomes of the preliminary market analysis, will be to update the Key Exploitable Results with a focus on their market value and business potential and to consolidate the IPR Assessment and set up a concrete Exploitation Plan. The result of the next period of activities will be reported in D8.7 OPTIMAI commercialization and exploitation strategy - 2nd version due at month 18 (June 2022

Mobile Robot Navigation for Person Following in Indoor Environments

Service robotics is a rapidly growing area of interest in robotics research. Service robots inhabit human-populated environments and carry out specific tasks. The goal of this dissertation is to develop a service robot capable of following a human leader around populated indoor environments. A classification system for person followers is proposed such that it clearly defines the expected interaction between the leader and the robotic follower. In populated environments, the robot needs to be able to detect and identify its leader and track the leader through occlusions, a common characteristic of populated spaces. An appearance-based person descriptor, which augments the Kinect skeletal tracker, is developed and its performance in detecting and overcoming short and long-term leader occlusions is demonstrated. While following its leader, the robot has to ensure that it does not collide with stationary and moving obstacles, including other humans, in the environment. This requirement necessitates the use of a systematic navigation algorithm. A modified version of navigation function path planning, called the predictive fields path planner, is developed. This path planner models the motion of obstacles, uses a simplified representation of practical workspaces, and generates bounded, stable control inputs which guide the robot to its desired position without collisions with obstacles. The predictive fields path planner is experimentally verified on a non-person follower system and then integrated into the robot navigation module of the person follower system. To navigate the robot, it is necessary to localize it within its environment. A mapping approach based on depth data from the Kinect RGB-D sensor is used in generating a local map of the environment. The map is generated by combining inter-frame rotation and translation estimates based on scan generation and dead reckoning respectively. Thus, a complete mobile robot navigation system for person following in indoor environments is presented