Collision Detection Mechanism Research Articles

Simulation Design and Research of Flame Generation in Virtual Fire Scene Based on Unity3D

Since the 21st century, with the continuous development and progress of science and technology, the possibility of fire danger is also rising, the requirements of public fire safety management have also been improved accordingly, and the problems in its fire safety management work make it difficult to meet the management requirements. The selection of firefighters has also become the top priority of management, good psychological quality of firefighters can keep calm in the face of fire explosions and various emergencies, and can make the most correct choice to complete the rescue and fire fighting work. In this paper, a building in a certain location is selected as the location to simulate the scene of fire. Through human-computer interaction to judge the psychological quality of firefighters. First use major modeling software such as 3ds Max, Adobe Fusecc, or find material in the Unity3D store to create models of fire scenes and people and import them into Unity3D. Using Unity3D's own particle system component to build flame models, adding collision body components to all models to detect their collision detection mechanism and the mechanism of flame spreading combustion. A Scenario-based Communication Ability Evaluation System Based on Traditional Cognitive Behavior Measurement and Advanced Eye Movement, EEG and NIR Acquisition Techniques.

Unleashing mixed-reality capability in Deep Reinforcement Learning-based robot motion generation towards safe human–robot collaboration

The integration of human–robot collaboration yields substantial benefits, particularly in terms of enhancing flexibility and efficiency within a range of mass-personalized manufacturing tasks, for example, small-batch customized product inspection and assembly/disassembly. Meanwhile, as human–robot collaboration lands broader in manufacturing, the unstructured scene and operator uncertainties are increasingly involved and considered. Consequently, it becomes imperative for robots to execute in a safe and adaptive manner rather than solely relying on pre-programmed instructions. To tackle it, a systematic solution for safe robot motion generation in human–robot collaborative activities is proposed, leveraging mixed-reality technologies and Deep Reinforcement Learning. This solution covers the entire process of collaboration starting with an intuitive interface that facilitates bare-hand task command transmission and scene coordinate transformation before the collaboration begins. In particular, mixed-reality devices are employed as effective tools for representing the state of humans, robots, and scenes. This enables the learning of an end-to-end Deep Reinforcement Learning policy that addresses both the uncertainties in robot perception and decision-making in an integrated manner. The proposed solution also implements policy simulation-to-reality deployment, along with motion preview and collision detection mechanisms, to ensure safe robot motion execution. It is hoped that this work could inspire further research in human–robot collaboration to unleash and exploit the powerful capabilities of mixed reality.

RDT-RRT: Real-time double-tree rapidly-exploring random tree path planning for autonomous vehicles

The complexity of the environment makes rapidly-exploring random tree (RRT) difficult to handle dynamic obstacle avoidance and system constraint in real-time path planning for autonomous vehicles. To handle this issue, this paper proposes a novel real-time double-tree rapidly-exploring random tree (RDT-RRT) algorithm framework. The collision-free path by RRT after B-spline smooth treatment is adopted as the reference path to reduce invalid sampling. Integrating G1 Hermite interpolation with G2 Hermite interpolation reduces the sampling dimension and takes more efficient samples. The optimal distance metric is designed considering dynamic collision detection mechanism and utilized to estimate the costs of the samples in terms of path curvature. Moreover, to have a better understanding of the environment, convolutional neural network (CNN) is embedded to strengthen the collision detection mechanism. By RDT-RRT the smooth, collision-free paths with small curvature changes can be evaluated. For the evaluations of our proposal in global and local planning, the experiments for a real scaled autonomous vehicle are implemented through parallel computing. By comparing with the mainstream RRT-based algorithms, it has shown that in terms of the path quality, our method reduces 92 % and 88 % of cumulative curvature change respectively in obstacle-free and static obstacle scenarios. Compared with other RRT methods, RDT-RRT performs faster convergence rate and Parallel computing increases the updating frequency from 1.1 Hz to 5.5 Hz. The obstacle avoidance capabilities are also improved.

GrpAvoid: Multigroup Collision-Avoidance Control and Optimization for UAV Swarm.

Collision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups. When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. A group of UAVs may be temporally blocked by other groups in a narrow space and cannot progress toward achieving its goal. To this end, this article proposes a modeling and optimization approach to multigroup UAV collision avoidance. Specifically, group level collision detection and adaption mechanism are introduced, efficiently detecting potential collisions among different UAV groups and restructuring a group into subgroups for better collision and deadlock avoidance. A two-level control model is then designed for realizing collision avoidance among UAV groups and of UAVs within each group. Finally, an evolutionary multitask optimization method is introduced to effectively calibrate the parameters that exist in different levels of our control model, and an adaptive fitness evaluation strategy is proposed to reduce computation overhead in simulation-based optimization. The simulation results show that our model has superior performances in deadlock resolution, motion stability, and distance maintenance in multigroup UAV scenarios compared to the state-of-the-art collision-avoidance models. The model optimization results also show that our model optimization method can largely reduce execution time for computationally-intensive optimization process that involves UAV swarm simulation.

Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm

Aiming at the problems of low global path quality and poor dynamic obstacle avoidance performance in underwater three-dimensional AUV autonomous path planning, an AUV global path planning method based on an adaptive genetic algorithm (AGA) is proposed in this paper. First, the traditional genetic algorithm are optimized to reduce the time of global path generation and improve the quality of global path generation. The path optimization strategy based on the collision detection mechanism is used to optimize the global path. Then, a local obstacle avoidance strategy based on the vector artificial potential field method (VAPF) is proposed. The space vector method is used to improve the calculation method of the resultant force direction to improve the calculation efficiency of the algorithm. Finally, the key path point is used as the local target point, and local obstacle avoidance is carried out under the guidance of the vector artificial potential field method. The simulation results show that the adaptive genetic algorithm can generate effective and high-quality global paths in a three-dimensional underwater environment. The new obstacle avoidance strategy can make AUVs effectively avoid all kinds of obstacles and reduce the obstacle avoidance cost of AUVs.

Exploiting Spontaneous Transmissions for Broadcasting and Leader Election in Radio Networks

We study two fundamental communication primitives: broadcasting and leader election in the classical model of multi-hop radio networks with unknown topology and without collision detection mechanisms. It has been known for almost 20 years that in undirected networks with n nodes and diameter D , randomized broadcasting requires Ω( D log n / D + log 2 n ) rounds, assuming that uninformed nodes are not allowed to communicate (until they are informed). Only very recently, Haeupler and Wajc (PODC'2016) showed that this bound can be improved for the model with spontaneous transmissions, providing an O ( D log n log log n /log D + log O (1) n )-time broadcasting algorithm. In this article, we give a new and faster algorithm that completes broadcasting in O ( D log n /log D + log O (1) n ) time, succeeding with high probability. This yields the first optimal O ( D )-time broadcasting algorithm whenever n is polynomial in D . Furthermore, our approach can be applied to design a new leader election algorithm that matches the performance of our broadcasting algorithm. Previously, all fast randomized leader election algorithms have used broadcasting as a subroutine and their complexity has been asymptotically strictly larger than the complexity of broadcasting. In particular, the fastest previously known randomized leader election algorithm of Ghaffari and Haeupler (SODA'2013) requires O ( D log n / D min {log log n , log n / D } + log O (1) n )-time, succeeding with high probability. Our new algorithm again requires O ( D log n /log D + log O (1) n ) time, also succeeding with high probability.

A Low-Latency and Energy-Efficient Neighbor Discovery Algorithm for Wireless Sensor Networks.

Wireless sensor networks have been widely adopted, and neighbor discovery is an essential step to construct the networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are fully connected or only two nodes compose the network. However, networks are partially connected in reality: some nodes are within radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially connected networks. We find that the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, we present an efficient algorithm called Panacea to address these challenges by alleviating collisions. To begin with, we design Panacea-NCD (Panacea no collision detection) for nodes that do not have a collision detection mechanism. When n is large, we show the discovery latency is bounded by for any duty cycle (the percentage time to turn on the radio), where each node has n neighbors on average. For nodes that can detect collisions, we then present Panacea-WCD which also bounds the latency within slots. Finally, we conduct extensive evaluations and the results also corroborate our analyses.

Security Concerns in Cloud Computing: Analysis and Solution

Cloud computing present comprehensive way for the user to interact with resources that they didn’t possess. Using cloud computing, resources can be accessed on pay per use basis. Mass community of user access resources from cloud and due to presence of sensitive information presents within cloud, security mechanism become need of the hour. This paper discusses security mechanisms used within cloud along with shortcomings and future enhancement of each technique. DNA encryption mechanism is discussed in detail along with research gap and problem definition. Collision problem is discovered from DNA encryption and its rectification using future enhancement process of folding with chaining is also presented through this literature. Results obtained in terms of parameters such as key size and execution time is presented in tabular structure. DNA encryption with folding method has least execution time in key formation but collision problem hamper performance of DNA encryption. Parameters such as throughput, key size and execution time must be enhanced through collision detection mechanism and discussed through this literature.

SPP 2.0: Simple Packet Protocol for ultra low cost wired networks

This article presents the Simple Packet Protocol version 2.0 (SPP 2.0). The protocol core is technology-independent and it is designed to provide communication for low data rate wired bus networks. The protocol is characterized by its low hardware requirements which allows for implementing it on embedded systems with limited hardware resources and achieving a very low price per node. An important feature of the SPP 2.0 is that its design addresses the multi-master asynchronous communication with flexible addressing model. Thanks to an effective collision avoidance and detection mechanism, the protocol is reliable under heavy network load conditions even without any additional feedback from a receiver. The presented SPP 2.0 is designed to provide wired communication for embedded systems connected with economical and maintenance-free wired network. SPP 2.0 systems can be easily integrated into larger networks, including IoT applications. The possible use cases for the protocol include: wired sensor and actuator networks, building automation systems, and modular embedded hardware platform prototyping.

FAGA: Feedback-Aided Greedy Algorithm for Periodic Messages in LTE V2V Communications

Vehicle-to-vehicle (V2V) communication based on the device-to-device (D2D) communication in long term evolution (LTE) system is emerging as a key enabler for connected vehicles today. In this paper, we consider safety-related messages periodically transmitted by vehicle to announce its current status to other neighboring vehicles. The current state-of-the-art LTE V2V communication uses greedy algorithm for the transmission of such periodic messages. Greedy algorithm is known to have decent performance, but the performance is limited due to the lack of a collision–detection mechanism. In this paper, we propose feedback-aided greedy algorithm (FAGA) to complement the drawback of greedy algorithm. We evaluate FAGA via realistic simulations to show that it outperforms comparison schemes in terms of overall message reception ratio (MRR). As a result, it is shown that FAGA operates well even with small amount of feedback and its performance is close to the performance of centralized scheduling method. Moreover, FAGA outperforms greedy algorithm by up to 23.7% in terms of MRR performance.

Force Aware Haptic Rendering for Intubation Simulation

We introduce a new haptic rendering method for neonatal endotracheal intubation simulation. The challenging procedure involves multiple models of different material properties, and is performed in the narrow oral cavity involving multiple contacts with tongue, lips, and laryngoscope. Our method first sets up simple collision detection mechanism with adaptive inner sphere tree structure for deformable tongue tissue. Then a collision response is handled with a novel force aware projective position correction. Our method is proved to be effective for heterogeneous simulation environment, therefore can be applied to surgical simulation with similar difficult settings.

Semi-Immersive Virtual Turbine Engine Simulation System

AbstractThe design and verification of assembly operations is essential for planning product production operations. Recently, virtual prototyping has witnessed tremendous progress, and has reached a stage where current environments enable rich and multi-modal interaction between designers and models through stereoscopic visuals, surround sound, and haptic feedback. The benefits of building and using Virtual Reality (VR) models in assembly process verification are discussed in this paper. In this paper, we present the virtual assembly (VA) of an aircraft turbine engine. The assembly parts and sequences are explained using a virtual reality design system. The system enables stereoscopic visuals, surround sounds, and ample and intuitive interaction with developed models. A special software architecture is suggested to describe the assembly parts and assembly sequence in VR. A collision detection mechanism is employed that provides visual feedback to check the interference between components. The system is tested for virtual prototype and assembly sequencing of a turbine engine. We show that the developed system is comprehensive in terms of VR feedback mechanisms, which include visual, auditory, tactile, as well as force feedback. The system is shown to be effective and efficient for validating the design of assembly, part design, and operations planning.

Collision Detection for Underwater ROV Manipulator Systems.

Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations.

Post-CCA and Reinforcement Learning Based Bandwidth Adaptation in 802.11ac Networks

The new 802.11ac standard aims at achieving Gbps data throughput for individual users by exploiting enhanced physical-layer features, such as higher modulation levels, Multiple Input Multiple Output (MIMO), and wider bandwidths. However, the heterogeneity of bandwidth in a network can cause asymmetric interferences in which certain transmissions cannot be sensed by some other nodes. As a result, the conventional Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) may not work well in 802.11ac networks. We call this the Hidden Channel (HC) problem, which is shown to be real via experiments with USRP and WARP boards. To solve this problem, we propose bandwidth adaptation based on post-CCA , which is a clear channel assessment (CCA) procedure performed after completing a transmission. Post-CCA in wireless networks helps mimic the CSMA with Collision Detection (CSMA/CD) mechanism in the wired Ethernet, thus enhancing channel assessment capability. Using post-CCA, we propose Post-CCA based Bandwidth Adaptation (PoBA) that alters bandwidth and channel configuration dynamically by applying a reinforcement learning mechanism. Post-CCA and PoBA do not require any hardware modification and are also compliant with the 802.11 standards. PoBA is shown via simulation to increase network-wide throughput, channel utilization and fairness, and also lower packet error probability.

Faster Information Gathering in Ad-Hoc Radio Tree Networks

We study information gathering in ad-hoc radio networks. Initially, each node of the network has a piece of information called a rumor, and the overall objective is to gather all these rumors in the designated target node. The ad-hoc property refers to the fact that the topology of the network is unknown when the computation starts. Aggregation of rumors is not allowed, which means that each node may transmit at most one rumor in one step. We focus on networks with tree topologies, that is we assume that the network is a tree with all edges directed towards the root, but, being ad-hoc, its actual topology is not known. We provide two deterministic algorithms for this problem. For the model that does not assume any collision detection nor acknowledgement mechanisms, we give an $$O(n\log \log n)$$ -time algorithm, improving the previous upper bound of $$O(n\log n)$$ . We then show that this running time can be reduced to O(n), which is asymptotically optimal, if the model allows for acknowledgements of successful transmissions.

Lightweight particle-based real-time fluid simulation for mobile environment

This paper presents a real-time lightweight fluid simulation based on a particle fluid technique developed for mobile environment . The Bullet physics engine and smoothed particle hydrodynamic (SPH) fluid algorithm will be used for our lightweight fluid simulation. First, we describe an advanced collision detection mechanism that will be used. By using this method, less computational resources are required. Secondly, we present a simplified SPH algorithm where nearby particles are grouped together to minimize the number of calculations. By decreasing the number of particles, an improved computational performance is expected. Finally, the ARM NEON based parallel computing technique was enabled to reduce execution time by lowering the number of arithmetic instructions. Several experiments are carried out where the experimental results indicate the first technique led to a 50% improvement in performance. The second technique provided a 17% overall improvement. The third technique delivered a performance improvement within the range of 26%–40%. Overall, the experimental results show that the proposed techniques provided an accumulative performance improvement of approximately 120% for all applied methodologies.

An improved CSMA/CA algorithm based on WSNs of the drug control system

A new improved CSMA/CA algorithm for wireless sensor networks (WSNs) is proposed in this paper to save energy and prolong the life cycle of WSNs. This algorithm is combined with the artificial neural network and Bayesian algorithm according to the practical applications of the drug control system of the Internet of Things. The algorithm is divided into two parts: first, the artificial neural network algorithm is used to estimate the data of WSNs, the results are the reference for the conversion of routing node frequency; second, by using the Bayesian formula, valuation method, and the CSMA/CA’s collision detection mechanism, the algorithm adjusts the frequency of the routing node and the relevant node frequency to establish the normal communication of packets sent by nodes and the aggregation node packets. In this way, it will reduce the collision detection and the back off time and avoid data packet duplication. The simulation tool-NS2 is used to configure an appropriate simulation scene for the experiment, which analyses and compares the received packet rate, the overall energy consumption of the network, and so on. The results demonstrate that the proposed algorithm ensures high energy efficiency and balanced energy consumption. Therefore the results show that the improved algorithm increases the efficiency, so that the network has the function of intelligent learning.

Utility optimization in heterogeneous networks via CSMA-based algorithms

We study algorithms for carrier and rate allocation in cellular systems with distributed components such as a heterogeneous LTE system with macrocells and femtocells. Existing work on LTE systems often involves centralized techniques or requires significant signaling, and is therefore not always applicable in the presence of femtocells. More distributed CSMA-based algorithms (carrier-sense multiple access) were developed in the context of 802.11 systems and have been proven to be utility optimal. However, the proof typically assumes a single transmission rate on each carrier. Further, it relies on the CSMA collision detection mechanisms to know whether a transmission is feasible. In this paper we present a framework for LTE scheduling that is based on CSMA techniques. In particular we first prove that CSMA-based algorithms can be generalized to handle multiple transmission rates in a multi-carrier setting while maintaining utility optimality. We then show how such an algorithm can be implemented in a heterogeneous LTE system where the existing Channel Quality Indication (CQI) mechanism is used to decide transmission feasibility.

Analysis of TCP-unfairness from MAC Layer Perspective in Wireless Ad-hoc Networks

In Ad-hoc networks every single node or station takes the responsibility to forward the data packets from the nodes that lie in the range with them. This additional routing responsibility might lead to performance degradation in some cases. Moreover, it is also reported that TCP which is designed for wired networks exhibit poor performance in terms of throughput and fairness in ad-hoc networks. Attempts to alleviate this issue included solutions from both TCP and MAC layer protocol modifications. This work aims at in-depth analysis of MAC level solutions (basic 802.11, 802.11 with RTS/CTS and Collision Detection Mechanism Based-MAC) to address TCP unfairness problem in 802.11-based multi-hop networks. A set of simulation based experiments are conducted and their observations are analyzed using metrics like throughput, goodput and loss probability. The metric loss probability is taken to investigate the protocols both from the perspective of congestion and collision i.e. the impact of congestion window and contention procedure on the end-to-end performance. The results obtained from the simulation revealed the inefficiency of the CDMB-MAC in the scenarios where hidden and exposed terminals do not exist. Further when TCP congestion window is set to 1 the basic 802.11 MAC is found sufficient to provide higher throughput with better fairness among the TCP connections but when TCP congestion window size is increased to 32 the 802.11 MAC with RTS/CTS scheme gives better performance. Added to this all the three MAC protocols fail in the scenario where the source and destination of a connection lie in the transmission ranges of destination and source of other connections. Therefore it can be concluded that it is indispensable to design an adaptive MAC protocol that could provide Fairness among TCP connections in all scenarios.

Collision Detection in Wireless Sensor Networks Through Pseudo-Coded ON-OFF Pilot Periods per Packet: A Novel Low-Complexity and Low-Power Design Technique

Sensor nodes in Wireless Sensor Networks (WSNs) operate with limited power resources such as small batterieswhich are difficult to be either recharged or replaced in some environments when depleted. Power consumptionrepresents one of the most constraints impact the design of WSNs, leading to various protocols and algorithmsaimed at minimizing the power consumption and extending batteries' lifetime. Sensor nodes in WSNs transmittheir periodic packets continuously to central nodes (receivers) which are responsible for decoding packets andtransmitting them to other communication networks. In addition, sensors usually follow various MAC strategieswhich allow accessing to wireless communication channels. However, sensors may attempt to access thewireless channels at the same time, potentially, leading to collisions among multiple nodes. In fact, central nodesin WSNs most often consume a large amount of power due to the necessity to decode every received packetregardless of the fact that the transmission may suffer from packets collision which impede the networkperformance. Therefore, in the receiver side of WSNs current collision detection mechanisms have largely beenrevolving around direct demodulation and decoding of received packets and deciding on a collision based onsome form of parity bits in each packet for error control. From information theoretic prospective full decoding ofreceived packets with error control bits at central nodes can achieve an efficient usage of network capacity,however, such an approach represents a major burden on power-constrained sensors. This drawback comes fromthe need to expend a significant amount of energy and processing complexity at sink nodes in order tofully-decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a morepractical power saving approaches which achieve a significant power saving with low-complexity at the expenseof low throughput losses. Based on studying the statistics of received packets, central nodes can make a fastdecision to detect a collision without the need for full-decoding of the whole received packets. Our novelapproaches not only reduces processing complexity and hence power consumption, but it also reduces the delayincurred to detect a collision since it operates on only a small number of IQ samples in the beginning of areceived packet. In such a paradigm, our approaches operate directly at the output of the receiver’sAnalog-to-Digital-Converter (ADC) and eliminate the need to pass the corrupted packets through the entiredemodulator/decoder line-up. The performance gain of our proposed approach is illustrated through thecomparison between the computational complexity of our Statistical Discrimination (SD) approaches and someexisting Full Decoding (FD) algorithms(note 1). Our results show that the SD approaches has significant powersavings and low computational complexities over existing FD algorithms with low False-Alarm and Missprobabilities, which qualify our SD approaches to be considered as reliable collision detection mechanisms inWSNs. We also show how to tune various design parameters in order to allow a system designer multipledegrees of freedom for design trade-offs and optimization.