A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s). While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex extrinsic dynamics are generated organically through the natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be found at https://flightgoggles.mit.edu. Revision includes description of new FlightGoggles features, such as a photogrammetric model of the MIT Stata Center, new rendering settings, and a Python AP

Autonomous Navigation for Mobile Robots in Crowded Environments

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

Aerial vehicle trajectory design for spatio-temporal task satisfaction and aggregation based on utility metric

Flight trajectories are mainly designed in order to make sure that the aerial vehicle reaches the destination point from the start point. In addition to that, the flight path is designed in such a way that the flight avoids dangerous zones and the maneuvers required for the path are practically feasible for the flight. This thesis focuses on the aerial vehicle trajectory generation passing through a set of pre-defined way points while satisfying the task points sent by the ground base station in between the way points. Each specified waypoint is reached exactly at the specified location and time. Intermediate waypoints are generated in such a way that they satisfy the task points, which give high benefit measure, i.e. satisfying tasks with high task priority and QoS (Quality of Service) priority. Generated waypoints are points from where imagery data of the tasks are collected. Task points are aggregated by the TABUM (Task Aggregation Based on Utility Metric) approach, which takes into consideration factors such as task points\u27 priorities, sensory capability and deviation required from the shortest path to satisfy that waypoint. We generate a 4D flight trajectory, which is a collection of predefined waypoints and generated waypoints by taking the velocities, maneuverability of the aerial vehicle into consideration while ensuring that the vehicle avoids the no-fly zones. We finally frame the problem of trajectory generation in a constrained environment as an optimization problem and solve it by increasing the benefit measure and decreasing the cost measure (deviation from line-of-sight path). We perform experiments to show the effect of the utility metric threshold value and compare the performance of the vehicles\u27 trajectory with flight maneuverability and helicopter maneuverability capabilities. We also show how the performance of the sensor/camera attached to the flight will effect the benefit measure --Abstract, page iv

A Decentralized Interactive Architecture for Aerial and Ground Mobile Robots Cooperation

This paper presents a novel decentralized interactive architecture for aerial and ground mobile robots cooperation. The aerial mobile robot is used to provide a global coverage during an area inspection, while the ground mobile robot is used to provide a local coverage of ground features. We include a human-in-the-loop to provide waypoints for the ground mobile robot to progress safely in the inspected area. The aerial mobile robot follows continuously the ground mobile robot in order to always keep it in its coverage view.Comment: Submitted to 2015 International Conference on Control, Automation and Robotics (ICCAR

3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs

UAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm