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Operations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. Higher demands in these operations and the requirement to access urban airspace present new challenges in sUAS operational safety. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements to its safe operation in urban environments according to the current legislation, such as the CAP 722 in the United Kingdom (UK). The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Different missions for the defined scenarios are designed and performed within the simulation model in Software Tool Kit (STK) software environment, covering a wide range of practical cases. The acquired data supports assessment of feasibility and requirements to real-time processing. Analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms.

Regular aviation does usually not utilize the very low level airspace below 150 m. Even so, this altitude range cannot be exclusively reserved for the operation of unmanned aircraft. Therefore, measures have to be implemented to avoid mid-air collisions, especially with manned aviation. Primarily, the mid-air collision risk is supposed to be reduced on a strategic level preflight by thorough flight route planning and making use of advanced unmanned aircraft system traffic management concepts. Besides, the residual mid-air collision risk is supposed to be reduced on a tactical level through the use of detect and avoid systems, which support remote pilots especially during flights beyond visual line of sight. However, an accepted technical solution for unmanned aircraft does not exist at the moment. On that account, the contribution of this chapter is three-fold. First, we identify suitable detect and avoid system architectures. Second, we review suitable cooperative sensor technologies. Third, we determine the minimum required sensor range in simulations. As a result, we propose a solution for operation in European airspace that is already available: FLARM cooperative sensors onboard the UA surveille the surrounding airspace and the remote pilot tracks intruders and commands avoidance maneuvers.

Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

One of the cornerstones that should enable inserting unmanned aircraft into the airspace is the development of Detect and Avoid (DAA) systems. DAA systems will improve the Remote Pilot (RP) situational awareness by means of electronic conspicuity devices, providing them with the necessary means to Remain Well Clear (RWC) from other traffic and, if necessary, avoid Mid-Air collisions (MAC). DAA systems will compensate for the loss of a pilot on board, which drastically reduces the capacity to keep a safe separation from traffic, making current Rules of the Air very challenging to achieve. Given the growing popularity of drone operations for commercial and recreational purposes, new standards should include them in the not-too-distant future. Since current DAA standards and algorithms (DO-365 and ED-258) are being developed targeting large, mostly military Remotely Piloted Aircraft Systems (RPAS), this project proposes a new set of detection volumes and alert thresholds for U-Space users according to an aircraft type classification. This will allow adapting the existing DAA algorithms to small drones, complying with the new European framework of services and applications for drones (U-Space). Because testing new safety nets (such as new DAA algorithms) on real aircraft would be dangerous and inadequate, radar reports and computer-based simulations allow for a risk-free and faster evaluation of safety net performances. Due to the current lack of real drone radar tracks, this project has developed a multi-rotor drone encounter generator tool (called DEG). This software is able to generate a large number of synthetic pairwise quadcopter drone conflict tracks, simulating the instant prior to a MAC. The way trajectories are generated by DEG strongly depends on the type of operation being flown (inspection/surveillance flights and logistic flights) and the aircraft type (including a DJI F450 and a faster version called DJI F450 FAST). The results of this project include a drone conflict trajectory example generated with DEG and an investigation of the performance and effectiveness of the DEG tool using a tailored existing DAA algorithm (DAIDALUS). Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Detect and Avoid (DAA) system is an essential part of Remotely Piloted Aircraft System (RPAS) for the safe operations within civil airspace. This paper investigates the performances of a DAA system in a fast-time simulation environment. A pilot decision model was developed to enable fast-time simulation using the Detect and AvoID Alerting Logic for Unmanned Systems that computes the range of maneuvers that are free of conflicts. In addition, realistic constraints about the DAA sensors are applied. Sensor’s limits are set with azimuth angle, vertical angle, and distance as well as update rates. A realistic encounter scenario was generated based on the recorded traffic in a congested terminal area. DAA Sensor performances and update rates as well as alerting criteria were varied to have the total of 24 different configurations. The results revealed that the developed model reasonably resolves conflicts in arrival procedure junctions, but it struggles when the conflict geometry is complicated near a holding pattern. In addition, increasing the update rate was not effective in preventing Loss of Well Clear in these situations.

Conceptual mathematical models play a crucial role in the development of engineering specifications required in the detailed design process and in the test and evaluation of avionics equipment. Specifying aircraft proximity is a precursor to the development of aircraft Detect and Avoid (DAA) systems that are, in turn, critical to enabling Unmanned Aircraft Systems (UAS) greater access to non-segregated airspace. Such specifications can also significantly enhance the safety of conventionally piloted aircraft operations. A more rigorous mathematical approach to specifying aircraft proximity functions is now possible based on the Apollonian Proximity Circle (APC) paradigm. In turn, this paradigm is based on the Cylindrical Bipolar Co-ordinate system that can be used to formally establish a framework of situational Quadrant Equivalence Sets and Test Regimes that exhibit differing proximity and geometrical behaviour. Some Test Regimes only require a sparse allocation of test points while others, because of functional characteristics such as asymptotic behaviour and contour spacing, require a far more intense allocation of test points. Having addressed the static specification of proximity the dynamic aspects related to short-term intent and Conflict Probing also need to be addressed and this leads to the identification of various anomalous behaviours. In this paper the examples identified are: False Positives; Turn Policy Reversals; the role of Decision Delays; and the nature of operational guidance within Well Clear Volumes. Selected examples show how this behaviour can be better characterized by reference to the APC paradigm and, in particular, with reference to the role of the Line of Sight vector between the aircraft in proximity. The APC paradigm presented in this paper is analytic thus enabling a far more complete and accurate specification of proximity than has previously been reported in the literature. The systematic approach provides a foundational basis for resolving the differing and contrary anecdotal viewpoints presently arising in the international literature.

The expected increase of unmanned aerial vehicles (UAVs) in civil airspace is driving research activities in the field of UAV Sense and Avoid. In terms of the application of unmanned air cargo transport, new multifunctional on-board systems can be envisaged, which allow to avoid both, other air traffic as well as hazardous weather areas. In order to prevent icing and turbulence, such a weather function should be able to detect dangerous weather phenomena. In addition, the weather function could offer the possibility to operate UAVs in compliance with visual flight rules (VFR) by observing legal minimum cloud distances. In this paper we propose a combination of AESA (Active Electronically Scanned Array) based weather radar and electro optical-cloud detection onboard UAV in order to increase the weather detection capabilities of both sensor systems. Besides the individual systems, the combination of both sensor systems and their advantages are described in detail. Furthermore, application scenarios are defined and shown using sample images from test flight campaigns.

Small Unmanned Aircraft Systems (UAS’s) will be a new modal for cargo transport within urban and suburban areas and for security services, as they are small, agile and low-cost vehicles at lower altitudes. However, its use in urban areas has several challenges. A crucial one is the detection of cables and power lines on power poles that are exposed on streets, as this detection bring many other challenges to be faced, e.g., they are difficult to be detected because their structure is long and thin when compared to a noisy image background. Therefore, detection systems can avoid accidents that cause material damage and injury for people. One way to avoid accidents caused by this type of vehicles is through the detection of cables and power lines with an embedded camera installed in each small UAS. The proposed method on this work is to realize the semantic segmentation of cables and power lines in small-UAS images through computer vision applications and deep learning techniques. Convolutional neural network (CNN) UNet was customized and implemented, and UAS images dataset of power lines in urban and suburban areas was used to train, test and detection validation. The Adam optimizer algorithm was used in replacement for stochastic gradient descent and an accuracy of 0.96 was obtained, which can be considered a promising result to proceed with streaming videos. The main contribution of this research is to provide a detection of cables and power lines for a detect and avoid systems (DAA) of small UAS, which will turn safe the use of aircraft at lower altitudes in urban areas. In addition, a secondary contributing is to create a test standard in this area.

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Unmanned Aerial Vehicles (UAV) commonly called as “Drones” are pilotless aircrafts which can be used in areas where manned aircrafts are too risky or difficult to operate. They are basically flying Robots which can be manually controlled or made autonomous with the help of software-controlled flight plans. They fly with the help of inputs received by the Embedded Processor from On Board sensors and Global Positioning System (GPS). Usage of Drones are on increasing demand with each year passing because of the wide variety of practical and innovative uses and applications offered by them. There are certain challenges faced by drones irrespective of the application in which they are being used. One of the main challenges faced by them is to detect and avoid objects which come in their path during the navigation. In this paper, we propose a deep learning based frontal object detection using pre-trained neural networks. The image frames obtained using the front facing monocular camera of the drone are processed and fed into the deep learning network for object detection. An overview and comparison of three deep learning algorithms in terms of their accuracy and speed is also considered.

Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate information from other sensors. In this paper, we explore the possibility of using depth information from a movable RGB-D camera mounted on the head of the robot, and investigate, in particular, active control strategies to effectively scan the environment. Existing works combine RGBD-D and 2D LiDAR data passively by fusing the current point-cloud from the RGB-D camera with the occupancy grid computed from the 2D LiDAR data, while the robot follows a given path. In contrast, we propose an optimization strategy that actively changes the position of the robot’s head, where the camera is mounted, at each point of the given navigation path, thus, we can fully exploit the RGB-D camera to detect, and hence avoid, obstacles undetected by the 2D LiDAR, such as overhanging obstacles or obstacles in blind spots. We validate our approach in both simulation environments to gather statistically significant data and real environments to show the applicability of our method to real robots. The platform used is the humanoid robot R1.

To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly, we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively. Project website: https://siddancha.github.io/projects/active-safety-envelopes-with-guarantees, 18 pages, Published at Robotics: Science and Systems (RSS) 2021

Automatic solar grass cutter reduces the effort required for cutting grass in the lawns. Also, solar power will be used to provide the driving force for the cutter and the various sensors will be used to detect and avoid the unnecessary objects in the field during operations. This system also operate on a battery that will also be charged through solar energy rather than using any external power. In this ultrasonic sensors are also used to detect any object/human/or animal while cutting the grass so to avoid them. It consists of microcontroller Arduino ATMEGA328U, LCD display for better response and understanding to the user. This paper will project the operation and working principle of the automatic grass cutter.

The operation of Unmanned Aerial Systems (UAS) is widely recognised to be limited globally by challenges associated with gaining regulatory approval for flight Beyond Visual Line of Sight (BVLOS) from the UAS Remote Pilot. This challenge extends from unmanned aircraft flights having to follow the same ‘see and avoid’ regulatory principles with respect to collision avoidance as for manned aircraft. Due to the technical challenges of UAS and Remote Pilots being adequately informed of potential traffic threats, this requirement effectively prohibits BVLOS UAS flight in uncontrolled airspace, unless a specific UAS operational airspace is segregated from manned aviation traffic, often achieved by use of a Temporary Danger Area (TDA) or other spatial arrangements. The UK Civilian Aviation Authority (CAA) has defined a Detect and Avoid (DAA) framework for operators of UAS to follow in order to demonstrate effective collision avoidance capability, and hence the ability to satisfy the ‘see and avoid’ requirement. The National BVLOS Experimentation Corridor (NBEC) is an initiative to create a drone experimentation facility that incorporates a range of surveillance and navigation information sources, including radars, data fusion, and operational procedures in order to demonstrate a capable DAA System. The NBEC is part located within an active Airodrome Traffic Zone (ATZ) at Cranfield Airport, which further creates the opportunity to develop and test systems and procedures together with an operational Air Traffic Control (ATC) unit. This allows for manned and unmanned traffic to be integrated from both systems and procedural perspectives inside segregated airspace in a first stage, and then subsequently transiting to/from non-segregated airspace. The NBEC provides the environment in which a number of challenges can be addressed. This paper discusses the lack of target performance parameters, the methodology for gaining regulatory approval for non-segregated BVLOS flights and for defining peformance parameters for counter UAS (cUAS).

Automatic Dependent Surveillance Broadcast equipment is increasingly becoming a fundamental means to support cooperative surveillance in the aviation section, including both manned conventional aircraft and unmanned aerial vehicles. The importance of the ADS-B is further increased by its ability to support Detect and Avoid (DAA) systems, which are able to act both as decision support systems for human pilot onboard manned vehicles and for remote pilots in case of Remotely Piloted Aircraft Systems (RPAS) and as automatic systems implemented onboard unmanned aerial vehicles performing fully automatic missions. Such considerations emphasize that the ADS-B technology is able to play the role of enabling technology for the implementation of single pilot operations on manned vehicles and for the integration of unmanned vehicles (totally automatic or remotely piloted) into the Air Traffic Management (ATM) system. In this paper, a system is outlined that has been designed by CIRA in order to exploit the ADS-B as cooperative surveillance sensor supporting the Detect and Avoid applications. In particular, the systems that is here outlined is an integrated system for Separation Assurance and Collision Avoidance, designed for the implementation in the RPAS domain. In the paper, therefore, first a review of the evolution of the CIRA designed system for DAA is provided and, then, an outline of the ADS-B based DAA systems is reported. Finally, some numerical applications are indicated to show the system behavior.

The SkyVision system was developed by the State of Ohio and the Air Force Research Laboratory (AFRL) to promote the research and development of Unmanned Aerial Systems (UAS) technologies. With the goal and vision to deliver a ground-based detect and avoid (GBDAA) capability, SkyVision leverages Air Traffic Control (ATC) level radar data through the Federal Aviation Administration's (FAA) Standard Terminal Automation Replacement System (STARS). This paper details the research and development of a set of subsystems that integrate into SkyVision to provide the system with UAS flight information, seeking to enhance the system, enabling safe beyond visual line of sight (BVLOS) UAS operations throughout the nation.

The solution to mitigating risks associated with beyond Visual Line of Sight (BVLOS) operations of Unmanned Aerial System (UAS) generally focuses on the use of advanced Unmanned Traffic Management (UTM) systems. However, this solution does not take into account other uncooperative objects in the airspace. A more robust approach is to have UTM integrations coupled with onboard machine vision which is tied to automated collision avoidance systems. Future BVLOS regulations in urban situations may require robust embedded software that is capable of detecting air collision hazards in realtime at near and far ranges as uncooperative small aircraft and other unpredictable small objects with fast-changing and unscheduled trajectories pose significant hazards to UAS. This work presents the concept and initial prototyping of a Digital Twin to evaluate the capability of UAS mounted LiDAR to detect small-object air collision risks. A Digital Twin of the Port of Hamburg is augmented with typical port and harbour aerial hazards such as birds, drones, helicopters, and low flying aircraft. The use case scenarios are created in Maya and Unity, with Optix ray tracing of typical LiDAR imaging configurations used to replicate the cause and effect relationship between different LiDAR specifications and their response to small flying objects. Our results demonstrate the inhomogeneous point clouds generated at different spatial-temporal parts of the LiDAR scanning cycle and field of view. These results confirm the challenges of detecting small uncooperative objects by LiDAR.

Currently, Unmanned Aircraft System (UAS) Traffic Management (UTM) is the Federal Aviation Administration’s (FAA) vision for air traffic management below 400 feet. Production UTM systems tend to reside only at specialized test sites and operational centers. UTM has been articulated as a concept of operation (ConOps) by the FAA. The UTM ConOps describes a complex interaction between UAS, UAS Operators, and the UTM system itself. These interactions may involve human operators, or be fully automated. Currently, most UTM studies and experimental prototypes do not look at the UTM concept from end-to-end; instead, they focus on specific aspects of UTM and thus cannot explore and test the holistic performance of a UTM ecosystem. Equally important is ensuring that production UTM can scale to meet the demands of future airspace, which is estimated to be 65,000 UAS operations (takeoffs and landings) per hour by 2035. The busiest US airports currently handle 300 operations per hour.In this paper, we evaluate a portion of the UTM system using a set of autonomous algorithms for flight plan de-confliction. Preliminary results suggest that the autonomy algorithms used for path planning, strategic de-confliction, and detect and avoid (DAA) are capable of scaling to high-congestion scenarios while drastically reducing collisions between UAS, even with almost all UAS deviating from de-conflicted plans (i.e., rogue UAS). We also observed that de-confliction algorithms represent a dominating safety layer in the separation hierarchy, since the strategic de-confliction algorithms manage airspace density, albeit at the cost of longer mission completion times. Our testing was done using a MATLAB simulator, which used the RRT* algorithm for flight planning, two different schedulers (Genetic Algorithm and the NASA Stratway Strategic Conflict Resolution algorithm) for strategic de-confliction, and the Airborne Collision Avoidance System for small unmanned aircraft systems (ACAS sXu) for DAA.

A reliable navigation, guidance and control system of Autonomous Unmanned Surface Vehicle (AUSV) is the major challenge for most researchers, especially when Global Positioning System (GPS) signal outages or its accuracy decreases in some places on the Earth’s surface. Our paper introduces a novel navigation, guidance and control system of AUSV as follows; the proposed navigation system of AUSV is based on integrated Micro Electric Mechanical System –Inertial Navigation System (MEMS-INS) smartphone sensors with GPS and Doppler Velocity Log (DVL) to correct AUSV navigation system errors. The guidance and control system of AUSV is based on Ultrasonic sensors, Raspberry Pi microcontroller, and digital compass sensor. The ultrasonic sensor is used to detect and avoid obstacles on the path of AUSV. The Raspberry Pi microcontroller and digital compass are used to guide AUSV in the required path. Since the GPS and DVL signals are used as the reference sources to correct errors of MEMS-INS system, the accuracy of GPS and DVL decreases due to a bad weather and due to associated noise of DVL measurement, which effects on a whole AUSV navigation system. To resolve this problem, the Adaptive Data Sharing Factor (ADSF) using Combined Filter (CF) is used as integrated method. According to proposed ADSF CF integrated method, the reference source (GPS or DVL) which has the beast accurate system will be used to correct the navigation errors. At same time the least accuracy system will detect and avoid. The efficiency of navigation, guidance and control system of AUSV based on proposed GPS/DVL/MEMS-INS ADFS CF integrated method is tested on surface reference trajectory with four obstacles located on the path of AUSV. During GPS outages, the AUSV was able to reach the target location to implement several tasks such as monitoring, exploring, tracking and returned back to base station depending on the stored path in its memory with high efficiency. Based on estimated results, the proposed navigation system could provide continuous navigation solution and reduce the navigation error to about 85.43% compared to GPS/DVL/MEMS CKF method and to about 68.14% compared to GPS/DVL/MEMS Constant DSF Combined Filter.

Underwater Wireless Sensor Network (UWSN) is one of the promising technologies having a wide range of applications, which includes underwater natural resources exploration, marine life study and underwater pipeline monitoring. Due to the harsh underwater environment, it is very challenging to provide an energy-efficient mobility-aware routing protocol for data collection. Due to the regular movement of the water currents, it becomes difficult to design a routing protocol that manages mobility of sensor nodes without the need of the localization details and with minimum energy utilization. Another issue of the UWSN is how to efficiently detect and avoid void nodes in a void area. The effect of the void node during routing increases energy utilization of the sensor nodes, which leads to decrease in the network lifetime and packet delivery ratio. In this paper, a Mobility-aware IMU-based Energy Efficient Routing (MIER) protocol for UWSN has been proposed to address issues including i) higher transmission overhead due to flooding of localization information exchange ii) and void node occurrence that leads to higher energy utilization and packet loss. The opportunistic data forward approach has been employed for the node communication. Extensive simulation has been carried out and the results show that MIER outperforms existing related research work in terms of end-to-end delay, packet delivery ratio and network lifetime.

The walking mobile robots have several advantages as compared to the wheel mobile robots over uneven terrains. The researchers have done a lot to develop different walking mechanisms for legged robots. Due to merits of legged robots such as scalable design, low payload-to-machine load ratio, bio-inspired locomotion, etc., the researchers have shown a great interest in the walking robots based on the Theo Jansen mechanism. In this paper, the dynamic analysis of a four-legged quadruped robot-based upon Jansen mechanism is proposed. The SimMechanics Toolbox Mathworks software is used for developing the physical modeling of the quadruped robot. Also, the real quadruped robot based on Jansen mechanism is developed and validated with a simulation model for checking the trajectory tracking performance. The proposed model of the quadruped robot is used for the application of obstacle avoidance. The fuzzy logic controller is proposed to detect and avoid the obstacles by the robots. Three Ultrasonic sensors (HC-SR04) are mounted in front of the robot to detect the obstacles. The validation of simulation and experimental results shows the performances of the navigation and obstacle avoidance fuzzy controller.

Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and actual assembly actions would result in the failure of the manual assembly planning. Therefore, the verification for the manual assembly planning is significant to ensure the correctness of the actual assembly operations, performing a convergence between the cyber and physical world. The challenges involved in retrieving and utilizing the actual data about the manual activities on a shop floor. In this paper, a self-contained wearable tracking system is proposed and applied to collect the shop-floor data during the manual assembly operations. And then, an unsupervised classification method is applied to empower semantic knowledge to the shop-floor data derived from the workplace. Thus, an automatic spatial–temporal verification for manual assembly planning is carried out, providing indicators to optimize the current manual assembly planning. Experimental results illustrate that the proposed work can perform the spatial–temporal verification for manual assembly task and indicate evidence to improve the manual assembly planning objectively.

This study elaborates the conflict management framework of unmanned aerial vehicles, focusing on the identification of the spatiotemporal interdependencies between them, with consideration of the future scalability problems in highly dense traffic scenarios. The paper first tries to justify the applied separation criteria among small cooperative unmanned aerial vehicles based on their performance characteristics and the planned missions’ type. The adopted criteria, obtained from the simulations of 160 missions, present a testing asset, referring to a current lack of the spatiotemporal requirements and a need for extending the research in this area to provide a more rapid integration of these vehicles into the civil controlled airspace. The paper then elaborates the computational framework for the conflict detection and resolution function and operational metrics for causal identification of the spatiotemporal interdependencies between two or more cooperative vehicles. The vehicles are considered as a conflict mission system that strives to achieve an efficient solution by applying certain maneuvering measures, before a loss of separation occurs. The operational trials of five local, short-range missions, supported by the simulation scenario, demonstrate the potential for a time-based complexity analysis in the conflict resolution processes with less demanding and more efficient coordinated maneuvers. The results show that those maneuvers would not induce any new conflicts and disrupt the cooperative mission system when the spatial capacity only might not be favorable in provision of the avoidance maneuvers within an available airspace.

Dynamic computer pipelines use feedback loops for iterative computation, due to which, data items, while traversing them often collide with one another. These pipelines, therefore, employ specialized mechanisms to detect and avoid such collisions in real-time. Mostly, this is done by deciding when, and when not, to allow newer data items to enter the pipeline. We will show that such decisions may lead to different throughput, and guaranteeing optimal performance is an optimization problem. We begin by building a mathematical model of dynamic pipelines, and make use of genetic algorithm to yield near-optimal throughput. The proposed optimization technique accounts for the hardware overhead, incurred due to the extensions in the pipeline organization, as suggested by itself. Our confidence in the results stems from simulation of 10,000 dynamic pipeline organizations, and for verification of results, we present hardware implementation of one of those. The proposed framework will specifically be useful in embedded systems for digital signal processing applications.

Time management is an important factor that affects the speed of parallel and distributed simulations. Conservative time management mechanisms advance simulation time after synchronization of logical processes, whereas optimistic time management mechanisms loosen synchronization among processes to speed up simulation. Because of loosened synchronization, the optimistic approaches are vulnerable to causality faults that must be addressed by logical processes during run-time. Repairing a simulation, by means of a rollback mechanism, is one of the most time-consuming processes of optimistic approaches. In this paper, we propose a method that considers precautionary measures against possible future rollbacks for Time Warp mechanism due to Jefferson. Our proposed method, named Predetermined Rollbacks, uses a modified simulation engine that can detect and avoid unnecessary rollbacks. Our experiments demonstrate that the proposed method can significantly improve the speedup of Time Warp in agent-based simulations, where agents communicate in a shared environment.

Background Assistant equipment for the visually impaired has a white cane. If the information in the three-dimensional space is transmitted by sound, the blind can draw a three-dimensional space. Objective This study developed "Visual System," an ambulation aid/guide for the blind that transforms visual-spatial information into auditory information, and verified its utility. Unlike conventional systems, which are in essence simple collision-warning systems, Visual System helps the visually impaired to recreate their surroundings and to be cognizant of the location and proximity of obstacles. Methods Ten subjects with normal vision (mean age: 32.4 years; male-to-female ratio: 6 to 4) were selected for blind tests. The subjects were instructed to detect and avoid obstacles presented in various three-dimensional settings. Prior to the tests, experiments were conducted to determine the distance to each subject. Upon completion of Visual System-based detection training, obstacles were presented and tests conducted. For evaluation, the subjects' vertical position detection, horizontal position detection, distance detection, and overall performance success were each evaluated. Results The total performance scores ranged between 88 (lowest) and 100 (highest), with a mean score of 91.5. Conclusions The results indicate that Visual System as a product can assist the visually impaired in their daily functioning.

Maximum-likelihood estimation of the parameters of a psychometric function typically occurs through an iterative search for the maximum value in the likelihood function defined across the parameter space. This procedure is subject to failure. First, iterative search procedures may converge on a local, not global, maximum in the likelihood function. The procedure also fails when the likelihood function does not contain a maximum. This is the case when either a step function or a constant function is associated with a higher likelihood than the model can attain with finite parameter values. In such cases iterative search procedures may erroneously report having successfully converged on a maximum in the likelihood function. This will lead not only to inaccurate models for the observed data, but may also lead to inaccurate results regarding the reliability of parameter estimates, goodness-of-fit of the model, or model selection. I describe a method by which such false convergences can be reliably detected. I also present results of simulations that systematically investigate how stimulus placement, number of trials, parameters estimated, task (2AFC, 4AFC, etc.), and whether the lapse rate is allowed to vary affect the probability that the likelihood function will not contain a maximum. Based on the results of the simulations recommendations are made regarding experimental design and modeling choices. Software that implements the method is made available for downloading.

An Unmanned Aerial Vehicle (often called a Drone) is an aircraft that is not operated by a pilot on board. Drones are rapidly growing market and increasingly leverage. The Applications of these flying robots are limitless. This paper reviews and discusses about embedded vision and sensor technology which culminates in several innovative applications over drones. It introduces an industry alliance available to help product creators incorporate robust vision capabilities into their drones designs. This paper mainly deals on Autonomous Delivery of light weighted items along with the collision avoidance technology and also tracks the whereabouts of the vehicle in a cost effective approach. This can also be used for carrying out complex assignments such as rescue operations during disasters, mines surveying.

Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario.

Lines of evidence used in ecological risk assessment (ERA) are essentially three: chemistry, biology and ecotoxicology. Until now, the fundamental assumption made when measuring ecological risks is that organisms are forcedly exposed to stressors. However, when organisms can avoid disturbed habitats by escaping to less stressful areas, the assumption that exposure is mandatory to pose risk may not match field disturbance scenarios. A non-forced exposure approach using a linear free-choice multi-compartmented system has been proposed previously as a complementary tool to assess the effects on organisms' spatial avoidance/preference responses. Yet, the linearity of the latter system limits avoidance measurements to one spatial dimension. A novel, heterogeneous multi-habitat assay system (HeMHAS) consisting of 18 connected circular compartments (3 compartments on a vertical axis in each one of 6 zones on a longitudinal axis; a 2D system) is put forward here to be used in heterogeneous-habitat selection studies, as it makes it possible to assess the ability of organisms to detect contamination and other stressors and select more favorable habitats. In the present study, the avoidance to copper by zebrafish (Danio rerio) was tested after exposing organisms to a copper gradient in the HeMHAS and compared with that in the linear system. Avoidance occurred for all copper concentrations: 43% in the lowest (21 μg·L-1) to 72% in the highest (221 μg·L-1). Results obtained within the HeMHAS (AC50: 60 μg·L-1) were statistically (p = 0.72) similar to avoidance of copper by D. rerio in the linear non-forced system (AC50: 89 μg·L-1). In summary, the simulation of a copper gradient in the HeMHAS (2D system) allowed to assess the potential repellency of copper to zebrafish and to corroborate the ability of organisms to detect and avoid potentially toxic concentrations.

In this article, a novel 3D printed knee model for detecting possible errors in the planning and treatment of total knee replacement surgery is presented. This method is a first step towards automation of prosthesis placement. Thanks to the mathematically computed four-bar mechanism, it is now possible to emulate the implant insertion before the operation. This model allows the surgeon to fit the prosthesis to the patient’s knee and to verify if the mounted position is optimal. This process can be repeated until the ideal position is found. Exact copies of the bones are made from the CT images of the patient. These resin copies of the real bones are placed in the motion model, reproducing the real patient’s knee flexion. The model shows the implantation result with respect to the operation plan and the patient’s kinematics. The experiment carried out on a patient’s model according to the standard implantation shows a lift-off and sliding effect of the femoral component outside the joint area. However, this phenomenon is easily resolved if the femoral component is implanted about half a centimeter lateral to the knee’s coronal axis. Until now, no medical research had questioned the lateral positioning of the prosthesis. This knee model provides an understanding of the important biomechanical parameters for total knee replacement surgery. This model will have a significant contribution to secure the surgery’s success, increase patient satisfaction and reduce the overall number of revisions.

An autonomous drone flying near obstacles needs to be able to detect and avoid the obstacles or it will collide with them. In prior work, drones can detect and avoid walls using data from camera, ultrasonic or laser sensors mounted either on the drone or in the environment. It is not always possible to instrument the environment, and sensors added to the drone consume payload and power - both of which are constrained for drones.This paper studies how data mining classification techniques can be used to predict where an obstacle is in relation to the drone based only on monitoring air-disturbance. We modeled the airflow of the rotors physically to deduce higher level features for classification. Data was collected from the drone’s IMU while it was flying with a wall to its direct left, front and right, as well as with no walls present. In total 18 higher level features were produced from the raw data. We used an 80%, 20% train-test scheme with the RandomForest (RF), K-Nearest Neighbor (KNN) and GradientBoosting (GB) classifiers. Our results show that with the RF classifier and with 90% accuracy it can predict which direction a wall is in relation to the drone.

In 2008, the depiction of the predicted ‘conflict space’ was proposed as a means to provide the pilot with the information needed to remain Well Clear.In this paper, it is discussed how results from flight testing, MOPS development and new ideas were the basis for the evolution of the concept of conflict space depiction as first presented in 2008 to today’s implementation in Conflict Prediction and Display System (CPDS) and the DELPHINS Synthetic Vision System (SVS).

The hierarchical Unmanned Aircraft System (UAS) Traffic Management (UTM) in Taiwan is constructed for Regional (RUTM under 400 feet) and National (NUTM above 400 feet) operations. The proposed ADS-B Like on-board unit (OBU) broadcasts 90 Byte flight data from UAV to UTM cloud and displays for surveillance. When multiple UAVs are flying in a small airspace, detect and avoid (DAA) mechanism should be activated to ensure flight safety in UTM. The proposed UAV DAA is completed by software in UTM. The DAA is designed referring to traffic alert and collision avoidance system (TCAS) in manned aircraft system to detect the approaching UAVs in the RUTM and identify possible conflict for resolution. UAVs are detected by heading arrow extrapolation for approach to assess possible conflict. A DAA separation bubble is defined by Traffic Advisory (TA) and Resolution Advisory (RA) among multiple targets with estimated time to conflict (TTC). Once the approach proceeds from conflict to collision alert at RA, the UTM controller will use controller to pilot communication (CPC) to notify the less priority UAV pilot to make ways for conflict resolution. In the system implementation, CPC is established using cell phones by 4G or by Zello broadcast. The proposed DAA function has been implemented into Tainan RUTM system with software manipulation and performed passive avoidance for UAVs in real time operation. In RUTM, the surveillance data period is fixed at 8 seconds. The signal delay in ADS-B like system to UTM display is about 0.5~1 second and Zello CPC is measured about 1~2 second delay. The communication time delay is enough to perform RA for avoidance. The ADS-B Like surveillance has counted with 89% reliability to trace UAV flights based on TTC concept. In the verification, ADS-B Like surveillance has been paid attention on reliability to work with the DAA software. From the RUTM operation in Taiwan, the proposed DAA function is preliminarily tested to assure UTM surveillance into better flight safety. The UTM project in Taiwan is continuing to improve operational efficiency and quality.

This paper proposes a vision-based collision avoidance system for unmanned aerial vehicles (UAVs). A method to detect and avoid approaching objects is necessary for UAVs since they are inherently vulnerable to external impacts. To resolve common issues with motion detection on a moving platform, computer vision algorithms such as optical flow and homography transform are utilized. The robustness of these algorithms is improved by employing characteristics of differential images. The proposed method is implemented in a camera-equipped onboard computer and then mounted onto a UAV as a collision avoidance system. It performs evasive maneuvers to avoid various objects thrown in its flight path, demonstrating its functionality and robustness.

Solidity, a programming language used to write smart contracts, has been improved since its initial release, but a number of vulnerabilities remain. As smart contracts are usually related to cryptocurrency, these vulnerabilities should be avoided to prevent the risk of financial loss. In this paper, we classify common vulnerabilities of function calls of Solidity programs into three groups and suggest a method to avoid them. The proposed method makes use of Ethereum Virtual Machine as well as Solidity extension. Experimental results with real-world smart contracts show that our method will detect and avoid these vulnerabilities.