Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles

Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.

Design and Implementation of Intelligent EOD System Based on Six-Rotor UAV

Explosive ordnance disposal (EOD) robots can replace humans that work in hazardous environments to ensure worker safety. Thus, they have been widely developed and deployed. However, existing EOD robots have some limitations in environmental adaptation, such as a single function, slow action speed, and limited vision. To overcome these shortcomings and solve the uncertain problem of bomb disposal on the firing range, we have developed an intelligent bomb disposal system that integrates autonomous unmanned aerial vehicle (UAV) navigation, deep learning, and other technologies. For the hardware structure of the system, we design an actuator constructed by a winch device and a mechanical gripper to grasp the unexploded ordnance (UXO), which is equipped under the six-rotor UAV. The integrated dual-vision Pan-Tilt-Zoom (PTZ) pod is applied in the system to monitor and photograph the deployment site for dropping live munitions. For the software structure of the system, the ground station exploits the YOLOv5 algorithm to detect the grenade targets for real-time video and accurately locate the landing point of the grenade. The operator remotely controls the UAV to grasp, transfer, and destroy grenades. Experiments on explosives defusal are performed, and the results show that our system is feasible with high recognition accuracy and strong maneuverability. Compared with the traditional mode of explosives defusal, the system can provide decision-makers with accurate information on the location of the grenade and at the same time better mitigate the potential casualties in the explosive demolition process.

Planning Fail-Safe Trajectories for Space Robotic Arms

A frequent concern for robot manipulators deployed in dangerous and hazardous environments for humans is the reliability of task executions in the event of a joint failure. A redundant robotic manipulator can be used to mitigate the risk and guarantee a post-failure task completion, which is critical for instance for space applications. This paper describes methods to analyze potential risks due to a joint failure, and introduces tools for fault-tolerant task design and path planning for robotic manipulators. The presented methods are based on off-line precomputed workspace models. The methods are general enough to cope with robots with any type of joint (revolute or prismatic) and any number of degrees of freedom, and might include arbitrarily shaped obstacles in the process, without resorting to simplified models. Application examples illustrate the potential of the approach.

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

Robotised Non-Destructive Testing (NDT) has revolutionised the field, increasing the speed of repetitive scanning procedures and ability to reach hazardous environments. Application of robot-assisted NDT within specific industries such as remanufacturing and Aersopace, in which parts are regularly moulded and susceptible to non-critical deformation has however presented drawbacks. In these cases, digital models for robotic path planning are not always available or accurate. Cutting edge methods to counter the limited flexibility of robots require an initial pre-scan using camera-based systems in order to build a CAD model for path planning. This paper has sought to create a novel algorithm that enables robot-assisted ultrasonic testing of unknown surfaces within a single pass. Key to the impact of this article is the enabled autonomous profiling with sensors whose aperture is several orders of magnitude smaller than the target surface, for surfaces of any scale. Potential applications of the algorithm presented include autonomous drone and crawler inspections of large, complex, unknown environments in addition to situations where traditional metrological profiling equipment is not practical, such as in confined spaces. In simulation, the proposed algorithm has completely mapped significantly curved and complex shapes by utilising only local information, outputting a traditional raster pattern when curvature is present only in a single direction. In practical demonstrations, both curved and non-simple surfaces were fully mapped with no required operator intervention. The core limitations of the algorithm in practical cases is the effective range of the applied sensor, and as a stand-alone method it lacks the required knowledge of the environment to prevent collisions. However, since the approach has met success in fully scanning non-obstructive but still significantly complex surfaces, the objectives of this paper have been met. Future work will focus on low-accuracy environmental sensing capabilities to tackle the challenges faced. The method has been designed to allow single-pass scans for Conformable Wedge Probe UT scanning, but may be applied to any surface scans in the case the sensor aperture is significantly smaller than the part.

LittleBot

<p>Robots to assist in USAR (urban search and rescue) situations have been employed since 2001. Such robots are designed to provide video and sensor feedback to evaluate hazardous environments before human taskforces are sent in. This minimises the risks human personnel are exposed to, while increasing the effectiveness of USAR operations. However, the typically high cost of such robots and the reliance on trained operators puts them out of reach of most USAR teams. In New Zealand, there are no nationally available robots suitable for USAR purposes. This thesis explores the development of new affordable devices that can be deployed for USAR operations, known as LittleBots. Three LittleBot variants are developed. Differing primarily in their locomotive capability, two mobile variants provide tether-less video reconnaissance and selectable gas level readings. The third, stationary variant, may be reconfigured with up to four selectable sensors, and is targeted at providing ongoing environmental monitoring at a disaster site. With all variants costing less than USD $155 in components, LittleBots are sufficiently low cost to be considered disposable, greatly increasing the likelihood they will be employed en masse. The stationary Sentry variant demonstrates a minimum runtime of over 60 hours, while the mobile variants provision up to 6 hours of mobile video reconnaissance. For independent deployment of LittleBots, a compatible Controller device is developed. Through user testing, the Controller device demonstrates easy and intuitive use, with no training required.</p>

LittleBot

<p>Robots to assist in USAR (urban search and rescue) situations have been employed since 2001. Such robots are designed to provide video and sensor feedback to evaluate hazardous environments before human taskforces are sent in. This minimises the risks human personnel are exposed to, while increasing the effectiveness of USAR operations. However, the typically high cost of such robots and the reliance on trained operators puts them out of reach of most USAR teams. In New Zealand, there are no nationally available robots suitable for USAR purposes. This thesis explores the development of new affordable devices that can be deployed for USAR operations, known as LittleBots. Three LittleBot variants are developed. Differing primarily in their locomotive capability, two mobile variants provide tether-less video reconnaissance and selectable gas level readings. The third, stationary variant, may be reconfigured with up to four selectable sensors, and is targeted at providing ongoing environmental monitoring at a disaster site. With all variants costing less than USD $155 in components, LittleBots are sufficiently low cost to be considered disposable, greatly increasing the likelihood they will be employed en masse. The stationary Sentry variant demonstrates a minimum runtime of over 60 hours, while the mobile variants provision up to 6 hours of mobile video reconnaissance. For independent deployment of LittleBots, a compatible Controller device is developed. Through user testing, the Controller device demonstrates easy and intuitive use, with no training required.</p>

Development of a novel tool for automation of the contamination measurement

Considering that about 100 000 m2 of wall area per nuclear facility must be decontaminated (Hübner et al., 2017), the automation of mechanical decontamination work offers high potential to support people in performing their work and reduce errors in the decommissioning process. Furthermore, the exposure potential for people in contaminated environments is reduced and they are protected from health hazards (Petereit et al., 2019). In the ROBDEKON project, a competence center is being established in Germany to develop practical robotic systems for decontamination work in hazardous environments. To this end, four research institutions are working with industrial partners on the development of (partially) autonomous robotic systems for the decommissioning and decontamination of nuclear facilities, the handling of waste, and the remediation of landfills and contaminated sites (Petereit et al., 2019). At the Institute for Technology and Management in Construction (KIT-TMB), the focus is on development of an automated solution for the (clearance) measurement of near-surface contaminations. A mobile elevating working platform is used as the robotic platform with a contamination array as the tool. The array measures the surface activity on the wall areas and verifies compliance with the thresholds. The contamination array is based on two sensor concepts: measurement and localization. Up to four hand-held contamination-measuring devices are attached to the array to parallelize the measurement. In order to avoid damaging the sensitive detector window foil of the contamination probes, the wall surface to be measured is first examined for imperfections with the help of a laser scanner. For localization of the array, up to four laser sensors are used for distance measurements. Measurement results are automatically saved after each measurement in a table specific to the measurement method and are available to users for documentation purposes at any time. In the further course of the project, the measurement results depending on the radiation level will be overlaid with a geometric 3D environment mapping.

Motion Control System Design for a Flying-Type Firefighting System with Water Jet Actuators

This paper presents the design and modeling of a flying-type fire extinguishing system. Fire accidents present very hazardous environments, and firefighters are in danger of losing their lives while putting out the fire. Strict safety measures should be considered to guarantee safe working conditions for firefighters, which is not the case every time, as fatalities and casualties are still being recorded. For this reason, a novel fire extinguishing system is proposed to provide more safe firefighting and survivor searches. The system studied in this paper is a pilot model that consists of a water jet-based actuation system to control the flying motion of the robot. The dynamic model of this flying robot is derived using the actuation forces, water jet system characteristics, and related information. The mathematical system model is detailed, a sliding-mode control system and a proportional-integral-derivative controller are designed, and comparative simulation tests are carried out.

Neural encoding of perceived patch value during competitive and hazardous virtual foraging

Natural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.