Potential Collision Research Articles

Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

The robotic manipulation of deformable linear objects is a frontier problem with many potential applications in diverse industries. However, most existing research in this area focuses on shape control for a provided explicit goal and does not consider physical constraints, which limits its applicability in many real‐world scenarios. In this study, a self‐supervised planning and control approach are proposed to address the challenge of rearranging deformable linear objects for implicit goals. Specifically, the context of making both ends of the object reachable (inside the robotic access range) and graspable (outside potential collision regions) by dual‐arm robots is considered. Firstly, the object is described with sequential keypoints and the correspondence‐based action is parameterized. Secondly, a generator capable of producing multiple explicit targets is developed, which adhere to implicit conditions. Thirdly, value models are learnt to assign the most promising explicit target as guidance and determine the goal‐conditioned action. All models within the policy are trained in a self‐supervised manner based on data collected from simulations. Importantly, the learned policy can be directly applied to real‐world settings since we do not rely on accurate dynamic models. The performance of the new method is validated with simulations and real‐world experiments.

Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces.

Artificial potential field-empowered dynamic holographic optical tweezers for particle-array assembly and transformation

Owing to the ability to parallel manipulate micro-objects, dynamic holographic optical tweezers (HOTs) are widely used for assembly and patterning of particles or cells. However, for simultaneous control of large-scale targets, potential collisions could lead to defects in the formed patterns. Herein we introduce the artificial potential field (APF) to develop dynamic HOTs that enable collision-avoidance micro-manipulation. By eliminating collision risks among particles, this method can maximize the degree of parallelism in multi-particle transport, and it permits the implementation of the Hungarian algorithm for matching the particles with their target sites in a minimal pathway. In proof-of-concept experiments, we employ APF-empowered dynamic HOTs to achieve direct assembly of a defect-free 8 × 8 array of microbeads, which starts from random initial positions. We further demonstrate successive flexible transformations of a 7 × 7 microbead array, by regulating its tilt angle and inter-particle spacing distances with a minimalist path. We anticipate that the proposed method will become a versatile tool to open up new possibilities for parallel optical micromanipulation tasks in a variety of fields.

A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.

Dynamic responses of thin-walled FRP-concrete-steel tubular wind turbine tower under horizontal impact loading: Experimental study and FE modelling

Thin-walled FRP-concrete-steel tubular towers (TW-FCSTs), composed of an outer FRP tube, an inner hollow steel tube, and an interlayer of concrete, represent a novel type of wind turbine tower with promising application prospects. Given their potential risks to potential vehicle or vessel collisions, this study investigates the dynamic responses of five cantilevered TW-FCSTs using a horizontal impact device. These TW-FCSTs possess significantly large void ratios (i.e., φ=0.73or0.82) and practical large column sizes (i.e., Dc = 300 mm, Hc = 1500 mm). The experimental programme of this paper investigates several key parameters, including FRP thickness, steel thickness, and void ratio of TW-FCSTs, as well as the velocity and numbers of impact loading. Experimental findings demonstrate the following insights: (1) cantilevered TW-FCSTs exhibit an overall flexural failure mode after the horizontal impact loading; (2) the increase in steel thickness or FRP thickness contributes to greater energy dissipation and reduced localized dent deformation; (3) the higher void ratio results in more pronounced localized dent deformation and less overall deformation in specimens; (4) the proportion of energy dissipation caused by localized dent deformation increased with impact velocity, signifying more severe localized dent deformation at higher impact velocities. The utilization of FE models in LS-DYNA effectively simulates the dynamic performance of cantilevered TW-FCSTs, offering reliable predictions. Parametric studies were conducted to analyze influences of impact mass, impact height, concrete strength, and steel yield strength.

Analysis of Potential Collisions of Elements of Therapeutic Accelerator on the Basis of the System of Virtual Visualization of Radiation Therapy

Analysis of Potential Collisions of Elements of Therapeutic Accelerator on the Basis of the System of Virtual Visualization of Radiation Therapy

Real-time assessment of ship collision risk using image processing techniques

The poor quality or the miss of Automatic Identification System (AIS) data may cause erroneous judgement of the potential navigational risk. Therefore, this study proposes a real-time framework for assessing ship collision risk using onboard video data in order to improve the risk perception ability of navigators. Firstly, the Squeeze-and-Excitation (SE) attention mechanism and the K-means algorithm are simultaneously utilized for the framework to enhance the multi-scale ship detection capability. The Deep-SORT is employed to complete multi-ship feature matching. Secondly, the distances between two ships and their speeds are measured using the pinhole imaging principle based on the ship visual feature extraction results. Moreover, the ship distance-speed correction method is designed to improve the reliability of estimated results. Finally, the effectiveness of the framework is validated using naturalistic driving data from the “He Hua Hai” ship. The results show that the proposed framework could demonstrate an excellent performance in assessing ship collision risk using the onboard video data. The proposed framework could help precisely detect and promptly provide warnings about potential ship collision risks. This could help prevent catastrophic accidents that pose a threat to oceans and coasts, particularly in situations when AIS data proves to be unreliable or ineffective.

Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

In this article, we propose an optimized version of the Hard Decision Decoder based on Hash and Syndrome Decoding (HSDec) decoder, named Reduced Memory Space of HSDec (RMS-HSDec), which uses less memory space. In this article, we aim to reduce the spatial complexity of the HSDec decoding algorithm while preserving its error correction capabilities. Our methodology involves allocating only the essential memory space for correctable error patterns and optimizing the hashing mechanism to effectively handle potential collisions. While maintaining the integrity of error correction, this new method guarantees memory reduction rates of over 96 % for the BCH(63, 39, 9) code and over 84 % for the QR(47, 24, 11) code compared to HSDec. Simulations were conducted to evaluate the performance of RMS-HSDec on various BCH and QR codes over AWGN and Rayleigh channels. The results demonstrated significant memory reduction rates and coding gains ranging from 0.8 dB to 2.8 dB over the AWGN channel and from 14 dB to 32 dB over the Rayleigh channel, confirming the robustness of the algorithm under different channel conditions. Comparative analyses showed that RMS-HSDec maintains competitive performance with existing decoders while offering effective error correction. These findings confirm the robustness of the RMS-HSDec algorithm under different channel conditions. Overall, the proposed decoder proves to be an effective solution, optimizing memory usage without compromising error correction capabilities, making it ideal for high-density data applications and environments with limited memory resources.

A Data-driven Search For Mid-infrared Excesses Among Five Million Main-sequence FGK Stars

Stellar infrared excesses can indicate various phenomena of interest, from protoplanetary disks to debris disks, or (more speculatively) techno-signatures along the lines of Dyson spheres. In this paper, we conduct a large search for “extreme” infrared excesses, designed as a data-driven contextual anomaly detection pipeline. We focus our search on FGK stars close to the main sequence to favor nonyoung host stars. We look for excess in the mid-infrared, unlocking a large sample to search in while favoring extreme IR excess akin to the ones produced by extreme debris disks (EDDs) and/or planetary collision events. We combine observations from ESA Gaia Data Release 3, the Two Micron All-Sky Survey, and the unWISE version of NASA’s Wide-field Infrared Survey Explorer (WISE), and create a catalog of 4,898,812 stars with G < 16 mag. We consider a star to have an excess if it is substantially brighter in the W1 and W2 bands than what is predicted from an ensemble of machine learning models trained on the data, taking optical and near-infrared information as input features. We apply a set of additional cuts (derived from the machine learning models and the objects’ astronomical features) to avoid false positives and identify a set of 53 objects, including one previously identified EDD candidate. The typical infrared-excess fractional luminosities we find are in the range 0.005–0.1, consistent with previous EDD candidates and potential planetary collision events.

Full duplex based collision detection to enhance the V2X sidelink autonomous mode

The Third Generation Partnership Project (3GPP) recently introduced the fifth-generation (5G) new radio (NR) sidelink to enable vehicle-to-everything (V2X) communications supporting advanced safety services. Nevertheless, improvements over the previous generation still pose challenges to meet the reliability and latency requirements of V2X communications, particularly in the allocation of distributed resources, i.e., Mode 2. In Mode 2, vehicles autonomously select radio resources for their message transmissions and can maintain the selected resources for a given reservation period to efficiently handle periodic data traffic. However, potential collisions during this period may remain undetected due to half-duplex communications and unacknowledged broadcast transmissions, resulting in persistent message losses and posing a threat to road safety. This paper aims to improve the 5G NR-V2X sidelink for systems beyond 5G-Advanced by exploiting full-duplex transceivers. We propose a novel medium access control (MAC) scheme where vehicles can detect collisions while transmitting, dynamically adapt the collision detection threshold according to the measured channel load, and react to detected collisions through appropriate resource reselection and retransmission procedures. Extensive simulations conducted under various settings show that this MAC scheme brings substantial performance gains in terms of reliability and latency, compared to the current legacy Mode 2 procedure and a benchmark full-duplex scheme from the literature.

Driver Behavior in Response to Forward Collision Warnings Considering Driving Context

Forward collision warnings (FCW) are designed to warn drivers of potential collisions, but their effectiveness may vary based on driving conditions and driver responses. This study investigated driver behavior in response to real-world FCW alerts across various driving contexts with the aims of evaluating the prevalence and characteristics of FCW events, examining how often these events occur, their severity, and the contexts in which they happen. The current analysis considered driver responses to FCW events, including changes in vehicle kinematics, visual attention, and hand-on-wheel (HOW) behavior. Findings suggest that lower-severity FCWs are more common in local road contexts and associated with adjacent vehicles, while higher-severity FCWs were more frequent on highways and in the presence of lead vehicles. The study highlights the potential value of integrating additional contextual information into FCW systems to inform drivers better and reduce unnecessary alerts.

An Assessment of Explosive and Fire Risks from Fighter Jet Collision with Aircraft Carrier

AbstractThis paper presents a framework for evaluating the risk of fire and explosion resulting from potential collisions between fighter jets and aircraft carriers. An event tree is a chronological order of risk issues that may arise from an initial event. This study creates an initial crash scenario based on actual accident cases and risk issues, which are event tree items. Using these scenarios, collision analysis is conducted utilizing finite element analysis to investigate the structural response. It allows us to identify potential fuel leakage and assess whether the collision impact is potent enough to activate the fuze of any onboard weaponry. Consequently, a series of fire analyses pertinent to the respective situations is executed. These analyses aim to discern whether hazardous conditions such as secondary fires or explosions will likely ensue post-collision. By simulating a possible collision scenario between a fighter jet and a flight deck, the proposed method systematically explores and understands the probability and consequences of fire and explosion events following such collisions.

Obstacle Avoidance Path Planning Strategy for Autonomous Vehicles Based on Genetic Algorithm

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Dynamic behaviors of bridge with corroded RC piers under barge collisions

Reinforced concrete (RC) bridge piers located in navigable waterways are susceptible to chloride-induced corrosion, which may cause severe damage or even entire collapse of bridge under potential barge collisions. The present study aims to examine the failure patterns and dynamic responses of typical simply supported girder bridge with corroded RC piers under barge collisions. Firstly, nine RC column specimens with three designed corrosion degrees and two corrosion strategies were prepared using the impressed anodic current technique. Secondly, the drop hammer impact and axial compression tests were successively performed to examine the residual axial load bearing capacities of both unimpacted and impacted specimens. Furthermore, by comprehensively considering the effects of corrosion-induced deteriorations on the effective cross-sectional area and yield strength of reinforcements, concrete strength, as well as the rebar-concrete bond-slip relationship, the refined finite element (FE) models of specimens were established and validated. Finally, the dynamic behaviors of a prototype simply supported girder bridge with corroded RC piers under barge collisions at different service lives, i.e., 0–60 years, were numerically assessed. It indicates that: (i) under drop hammer impact, six specimens all exhibit global flexure failure, and relatively severe concrete cracking and spalling occur in the specimens with 15 % designed corrosion degree; (ii) the axial load bearing capacities of specimens with 5 % and 15 % designed corrosion degrees are reduced by 15.46 % and 34.86 % compared to the unimpacted non-corroded specimen, and 11.29 % and 43.30 % compared to the impacted non-corroded specimen; (iii) the transverse impact resulted in 25.93 %, 22.27 % and 35.53 % reductions in axial load bearing capacities for 0 %, 5 % and 15 % designed corrosion degree, respectively; (iv) for the prototype barge-bridge collision scenario, the failure patterns vary from minor bending cracks to remarkable flexural failure as the service live increases from 0 to 60 years. The corrosion-induced degradations of bridge performance should be concerned for the vessel collision-resistant design of bridge.

Risk Assessment of Bird Collisions with a Wind Turbine Based on Flight Parameters

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Vehicle Dynamic Maneuverability through Data-driven Six Sigma Analysis for Enhanced Driver Assistance

This paper presents the concept of a vision-based system to provide intelligent driving assistance for steering, braking, and acceleration maneuvers. The system utilizes vision systems to detect the vehicle's surroundings, including the road, other vehicles, pedestrians, and obstacles. Based on this environmental awareness, the system can intervene to assist the driver to enhance the safety and efficiency of the driving experience by maneuverability access or alert to driver in steering, brake, and acceleration. The steering assist function analyzes the road geometry and trajectory and provides subtle steering adjustments alerts to keep the vehicle centered in the lane thereby smoothly navigate curves. The braking assist function detects potential collision threats and can assist driver on application status of brakes to avoid or mitigate impacts. Similarly, the acceleration assist function monitors the driving conditions and traffic flow to optimize throttle input for improved fuel economy, traffic flow thereby reducing the impact to dynamic parts of vehicles. A detailed six sigma analysis is done by DMAIC method to prove the proposed methodology significance compared to existing techniques.

Application of Technological Procedure Automated Guided Vehicles in the Production Hall of a Company Due to Increasing the Automation—Case Study

This paper discusses the possibility of deploying automated guided vehicle (AGV) technology in a company’s production hall. Chronometric measurements of material handling and people handling were performed. The handling processes were divided into five sub-processes, which were analysed in terms of production line operation, distance, and carrying capacity of the automated guided vehicles. Based on the measurements, we found that it was necessary to use four vehicles for handling in the foam production hall. The company is applying this automation project to foams, and after successful testing, plans to continue with automation for leather upholstery. To ensure the correct chronometric results, we measured the distances between each workstation on the production floor at the company. Based on an analysis of material handling on the production lines, we evaluated that there is a possibility of applying AGVs on the entire production floor. We analysed the volumes of material transfer and the possibilities of interconnection within the existing material handling in the enterprise. Complications arise when deploying AGVs in the plant because the handling to the production lines is not only provided by the parent company, but also by the component suppliers themselves. Based on this observation, we have identified potential collision points within the operation between automated guided vehicles and other material handling equipment or people. For this reason, we deployed one more AGV on the handling routes to prevent unwanted collisions. This means that a human would be able to perform even more processes than a machine and can circulate 80 times, where a machine under the same conditions would only perform the entire circulation 57 times. There is room here to use humans for other processes and to automate the handling of goods, although it introduces delays that can be eliminated by deploying additional automated guided vehicles. This opens room for further research questions on how these vehicles can be deployed, even in a small space, and how existing material handling can be automated.

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

Hybrid trajectory planning of two permanent magnets for medical robotic applications

Independent robotic manipulation of two large permanent magnets, in the form of the dual External Permanent Magnet (dEPM) system has demonstrated the possibility for enhanced magnetic control by allowing for actuation up to eight magnetic degrees of freedom (DOFs) at clinically relevant scales. This precise off-board control has facilitated the use of magnetic agents as medical devices, including catheter-like soft continuum robots (SCRs). The use of multiple robotically actuated permanent magnets poses the risk of collision between the robotic arms, the environment, and the patient. Furthermore, unconstrained transitions between actuation inputs can lead to undesired spikes in magnetic fields potentially resulting in unsafe manipulator deformation. This paper presents a hybrid approach to trajectory planning for the dEPM platform. This is performed by splitting the planning problem in two: first finding a collision-free physical path for the two robotically actuated permanent magnets before combining this with a path in magnetic space, which permits for a smooth change in magnetic fields and gradients. This algorithm was characterized by actuating each of the eight magnetic DOFs sequentially, eliminating any potential collisions and reducing the maximum undesired actuation value by 203.7 mT for fields and by 418.7 mT/m for gradients. The effect of this planned magnetic field actuation on a SCR was then examined through two case studies. First, a tip-driven SCR was moved to set points within a confined area. Actuation using the proposed planner reduced movement outside the restricted area by an average of 41.3%. Lastly, the use of the proposed magnetic planner was shown to be essential in navigating a multi-segment magnetic SCR to the site of an aneurysm within a silicone brain phantom.

Trajectory planning with multiplatform spacetime RRT*

The article presents a method of planning the flight trajectory of a swarm of drones using a modified RRT (Rapidly-exploring Random Tree) algorithm. The version of the RRT algorithm presented in the article is called Multiplatform Spacetime RRT*. The proposed modifications make it possible to determine the flight trajectory of UAVs taking into account time constraints related to the area occupied by each platform. Additionally, the proposed algorithm ensures the avoidance of potential collisions of platforms in the air by using a collision avoidance algorithm used in practice based on geometric methods. Two designed and tested modifications of RRT were presented, based on the basic RRT* and Informed RRT* algorithms. The algorithm used in both tested versions guarantees the determination of the optimal flight path for unmanned platforms in a finite, small number of steps, which solely depends on the number of UAVs involved. This algorithm takes into account the dynamic model of the fixed-wing UAV. The simulation results presented by planning the flight trajectory of a swarm, consisting of three and four UAVs using the Multiplatform Spacetime RRT* algorithm, are significantly better than the algorithms that were compared to achieve these results.