Autonomous Landing for Indoor Flying Robots Using Optic Flow

2003 ◽  
Author(s):  
William E. Green ◽  
Paul Y. Oh ◽  
Keith Sevcik ◽  
Geoffrey Barrows
Keyword(s):  
Collision Avoidance ◽  
Depth Perception ◽  
Optic Flow ◽  
Urban Environments ◽  
Search And Rescue ◽  
Autonomous Landing ◽  
Aerial Robots ◽  
Aerial Robot ◽  
Detection Search ◽  
Flying Robots

Urban environments are time consuming, labor intensive and possibly dangerous to safe guard. Accomplishing tasks like bomb detection, search-and-rescue and reconnaissance with aerial robots could save resources. This paper describes a prototype called CQAR: Closed Quarter Aerial Robot, which is capable of flying in and around buildings The prototype was analytically designed to fly safely and slowly. An optic flow microsensor for depth perception, which will allow autonomous takeoff and landing and collision avoidance, is also described.

scholarly journals Resizable Drone Designs: For Maneuvering through Paths of Varying Dimensions

2019 ◽  
Vol 8 (2) ◽  
pp. 3162-3166
Keyword(s):  
Embedded Systems ◽  
Unmanned Aerial Vehicle ◽  
Cost Effective ◽  
Search And Rescue ◽  
The Other ◽  
Complex Structures ◽  
Variable Size ◽  
Aerial Vehicle ◽  
Flying Robot ◽  
Flying Robots

An unmanned aerial vehicle, commonly known as a drone, is an aircraft without a human pilot aboard. Essentially, a drone is a flying robot that can be remotely controlled or fly autonomously through software-controlled flight plans in their embedded systems, Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. Their maneuverability and hovering capabilities allow them to navigate through complex structures, inspect damaged buildings, and even explore underground tunnels and caves. Since their size is fixed, maneuvering over the compact areas and tunnels of variable size becomes an issue. To overcome this issue, we propose a model of quadrotor design which has the capability to change its size. The arm length of the quadrotor is changed dynamically so that it can fly in areas of variable sizes that would be hard to reach with the quadrotor of fixed arm length. On the other hand, our model is cost-effective, since the arm of the drone is designed with PVC (Polyvinyl Chloride). Using this model, drones will be able to move over compact areas and passages of variable sizes, thus aiding in better exploration during search and rescue operations.

scholarly journals Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 37
Author(s):  
Farouk Mezghani ◽  
Nathalie Mitton
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Urban Environments ◽  
Search And Rescue ◽  
Cooperative Scheme ◽  
Disaster Area ◽  
Time Simulation ◽  
Short Period ◽  
Short Time ◽  
Ubiquitous Presence

Disaster scenarios are particularly catastrophic in urban environments, which are very densely populated in many cases. Disasters not only endanger the life of people, but also affect the existing communication infrastructures. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its limited resources within a short period of time. This work evaluates the performances of smartphones and leverages the ubiquitous presence of mobile devices in urban scenarios to assist search and rescue activities following a disaster. Specifically, it proposes a collaborative protocol that opportunistically organizes mobile devices in multiple tiers by targeting a fair energy consumption in the whole network. Moreover, it introduces a data collection scheme that employs drones to scan the disaster area and to visit mobile devices and collect their data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes and smart self-organization, thereby effectively assisting search and rescue operations.

scholarly journals Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6507 ◽  
Author(s):  
Liang Lu ◽  
Carlos Redondo ◽  
Pascual Campoy
Keyword(s):  
Clustering Algorithm ◽  
Information Gain ◽  
State Of The Art ◽  
Search And Rescue ◽  
Autonomous Exploration ◽  
Aerial Robots ◽  
Exploration Time ◽  
Feasible Path ◽  
Path Planner ◽  
To Receive

Aerial robots are widely used in search and rescue applications because of their small size and high maneuvering. However, designing an autonomous exploration algorithm is still a challenging and open task, because of the limited payload and computing resources on board UAVs. This paper presents an autonomous exploration algorithm for the aerial robots that shows several improvements for being used in the search and rescue tasks. First of all, an RGB-D sensor is used to receive information from the environment and the OctoMap divides the environment into obstacles, free and unknown spaces. Then, a clustering algorithm is used to filter the frontiers extracted from the OctoMap, and an information gain based cost function is applied to choose the optimal frontier. At last, the feasible path is given by A* path planner and a safe corridor generation algorithm. The proposed algorithm has been tested and compared with baseline algorithms in three different environments with the map resolutions of 0.2 m, and 0.3 m. The experimental results show that the proposed algorithm has a shorter exploration path and can save more exploration time when compared with the state of the art. The algorithm has also been validated in the real flight experiments.

scholarly journals AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5223 ◽  
Author(s):  
Junjie Chen ◽  
Shuai Li ◽  
Donghai Liu ◽  
Xueping Li
Keyword(s):  
Situational Awareness ◽  
Satellite System ◽  
Search And Rescue ◽  
Measurement Unit ◽  
Urban Search And Rescue ◽  
Critical Spaces ◽  
Rescue Operation ◽  
Holistic View ◽  
Urban Disaster ◽  
Aerial Robot

Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.

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