Collision Risk Research Articles

Multi-condition hawser tension prediction of offshore offloading system based on long and short-term memory network and transfer learning

Real-time prediction of hawser tension of the side-by-side oil offloading system of Floating Production Storage and Offloading (FPSO) can provide early warnings for hawser breakages and ship collision risk, thus improving structural, property, and environmental security. Although offline numerical models and Long Short-Term Memory (LSTM) could offer substantial precision, they confront intensive time costs in recalibrating or retraining models. This paper proposes an integration method of LSTM networks and transfer learning for real-time tension prediction considering inputs of actual remote wave elevations. We use short-term environmental data and numerical simulation data of correlated hawser tensions during offloading operations to train a pre-trained benchmark model for transfer learning. Then, a highly generalized and efficient transferred model is constructed by using a small sample to realize short-term tension predictions in time-varying environments. The results show the occurrence time and value of extreme tensions predicted by transfer learning nearly match the reference data, and their maximum errors are 3 s and 0.11, respectively, superior to LSTM direct training. Therefore, it provides sufficient demand for real-time prediction and early collision risk prevention in dynamically changing ocean environment. The research results could provide an alternative framework for intelligent monitoring of large-scale marine structures.

Single- and Multinozzle Plume Impingement to Detumble Space Debris

The management of space debris is increasingly crucial for ensuring the safety of space missions by mitigating collision risks. Most proposed debris management systems involve the capture and deorbiting of debris. However, it is also essential to eliminate any residual rotation and nutation of the debris to perform capture without accidents. The current work examines the effectiveness of thruster plume impingement for despinning space debris. A proof-of-concept study of plume impingement dynamics is conducted on a rectangular prism target using single- and multiple-nozzle arrangements for different target offsets and orientations. The three-dimensional simulation is carried out using the open-source Direct Simulation Monte Carlo code SPARTA. Staging and dynamic grid adaptation techniques are incorporated to obtain high-resolution results for the multiscale problem. It is observed that the plume–plume interaction in the multinozzle configuration leads to the beneficial formation of a relatively narrow beam of flow interacting with the target; that is, a multinozzle plume exerts a more concentrated directed force, which imparts greater countertorque across a broader spectrum of target orientations than a single-nozzle plume.

Application of simulated AIS data to study the collision risk system of ships

Abstract In previous research, several computational methods have been proposed to analyse the navigation, transportation safety and collision risks of maritime vessels. The objective of this study is to use Automatic Identification System (AIS) data to assess the collision risk between two vessels before an actual collision occurs. We introduce the concept of an angle interval in the model to enable real-time response to vessel collision risks. When predicting collision risks, we consider factors such as relative distance, relative velocity and phase between the vessels. Lastly, the collision risk is divided into different regions and represented by different colours. The green region represents a low-risk area, the yellow region serves as a cautionary zone and the red region indicates a high-alert zone. If a signal enters the red region, the vessel's control system will automatically intervene and initiate evasive manoeuvres. This reactive mechanism enhances the safety of vessel operations, ensuring the implementation of effective collision avoidance measures.

Long‐term data reveals increase in vehicle collisions of endangered birds in Hokkaido, Japan

AbstractWildlife‐vehicle collisions have significant consequences for both humans and animals, including injuries, deaths, and vehicle damage. Therefore, analysis of accident data is important for planning countermeasures and appropriate wildlife management. In this research field, roadkill incidents have been extensively studied in many taxa, while railway accidents have received less attention despite their obvious impact on wildlife. Here we applied a Bayesian state‐space model to 31 years of collision data, both on railways and on roads, collected by the Ministry of the Environment in Hokkaido prefecture, Japan, to reveal the spatiotemporal dynamics of accidents for white‐tailed eagles, Steller's sea eagles, and red‐crowned cranes, for which over hundred accidents were reported in the data. Our analysis suggested that the mean annual number of individuals collected per collision site across Hokkaido increased 47,377‐fold in the white‐tailed eagle, 40,277‐fold in the Steller's sea eagle, and 50,584‐fold in the red‐crowned crane between 1991 and 2021. There have been concerns about the impact of traffic accidents on the population dynamics of these endangered birds, but no formal analyses have been conducted. Our analysis showed numerically that the negative impact has been increasing annually. These results suggest that long‐term data accumulation over large spatial scales allows us to understand the dynamics of accidents and predict potential factors underlying collision risks.

Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth

Driving is integral to many people’s daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver’s physiological state (e.g., fatigue, anger, or stress) can negatively affect their driving performance. This is especially true for young drivers who have limited driving experience. This research focuses on examining the connection between emotional arousal and aggressive driving behavior in young drivers, using predictive analysis based on electrodermal activity (EDA) data through neural networks. The study involved 20 participants aged 18 to 30, who completed 84 driving sessions. During these sessions, their EDA signals and driving behaviors, including acceleration and braking, were monitored using an Empatica E4 wristband and a telematics device. This study conducted two key analyses using neural networks. The first analysis used a comprehensive set of EDA features to predict emotional arousal, achieving an accuracy of 65%. The second analysis concentrated on predicting aggressive driving behaviors by leveraging the top 10 most significant EDA features identified from the arousal prediction model. Initially, the arousal prediction was performed using the complete set of EDA features, from which feature importance was assessed. The top 10 features with the highest importance were then selected to predict aggressive driving behaviors. Another aggressive driving behavior prediction with a refined set of difference features, representing the changes from baseline EDA values, was also utilized in this analysis to enhance the prediction of aggressive driving events. Despite moderate accuracy, these findings suggest that EDA data, particularly difference features, can be valuable in predicting emotional states and aggressive driving, with future research needed to incorporate additional physiological measures for enhanced predictive performance.

Oblique or short incisions reduce the risk of saphenous nerve damage during hamstrings harvesting. A model for mapping nerve pathways at the harvest site.

INTRODUCTIONHamstring autografts are frequently used for ligament reconstruction surgery. Twelve to 84% of patients describe hypo or dysaesthesia secondary to injury of the saphenous nerve or one of its branches after hamstring harvesting. Type of incision (orientation and length) is subject of much debate to limit the risk of nerve damage. A cadaveric study was performed to determine which type of incision limits the risk of injury to the saphenous nerve or one of its branches, based on an anatomic model for mapping nerve pathways at the harvest site. METHODSAn anatomical study was performed on 20 knees from 12 embalmed bodies. Distance between saphenous nerve branches and 4 points of interest along the tibial crest was measured. Based on these measurements, a digital model of the saphenous nerve and its branches was created. A model of three common types of incision (vertical, horizontal and oblique) was overlaid. Each incision was modelled for three lengths (2, 3 and 4cm). Percentage of collision between nerve course model and incision model was then calculated to determine the risk of nerve damage for each type of incision. Based on the nerve course model, a “low collision risk” safe zone was identified. RESULTSNerve damage risk after an oblique incision was significantly lower than for a horizontal or vertical incision, for incision lengths of 3 and 4cm (p<0.05). For a specific incision orientation, the length of the incision did not affect the risk of nerve damage. A trapezoidal space close to the tibial crest and distal to the anterior tibial tuberosity appears to reduce risk of nerve damage. CONCLUSIONThis cadaveric study suggests that during hamstring harvesting, incisions shorter than 2cm reduce the risk of saphenous nerve’s branches injuries. For incisions longer than 2cm, using an oblique incision may reduce the risk compared to vertical or horizontal incisions. LEVEL OF EVIDENCELevel of evidence not applicable: Laboratory experiments

Aircraft Collision Risk Analysis Based on EVENT Improved Modeling

Aircraft Collision Risk Analysis Based on EVENT Improved Modeling

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

Safety-Critical Fixed-Time Formation Control of Quadrotor UAVs with Disturbance Based on Robust Control Barrier Functions

This paper focuses on the safety-critical fixed-time formation control of quadrotor UAVs with disturbance and obstacle collision risk. The control scheme is organized in a distributed manner, with the leader’s position and velocity being estimated simultaneously by a fixed-time distributed observer. Meanwhile, a disturbance observer that combines fixed-time control theory and sliding mode control is designed to estimate the external disturbance. Based on these techniques, we design a nominal control law to drive UAVs to track the desired formation in a fixed time. Regarding obstacle avoidance, we first construct safety constraints using control barrier functions (CBFs). Then, obstacle avoidance can be achieved by solving an optimization problem with these safety constraints, thus minimally affecting tracking performance. The main contributions of this process are twofold. First, an exponential CBF is provided to deal with the UAV model with a high relative degree. Moreover, a robust exponential CBF is designed for UAVs with disturbance, which provides robust safety constraints to ensure obstacle avoidance despite disturbance. Finally, simulation results show the validity of the proposed method.

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

Enhancing Tram Safety in Mixed-Traffic Systems: A Case Study of Melbournes Tram Collision Factors and Improvement Strategies

Abstract: Trams, a vital component of public transportation, operate within diverse environments ranging from fully segregated tracks to mixed-traffic systems. This study investigated tram collisions in Melbournea city renowned for its extensive and historic tram networkand explored both systemic and behavioral factors contributing to these incidents. The analysis identified key factors such as the arrangement of tram platforms, intersection design, right-of-way (ROW) regulations, tram driver behavior, and the actions of other road users. The study revealed that inadequate tram platform arrangements and suboptimal intersection designs are major contributors to tram collisions. Recommendations for improvement include implementing elevated, standalone tram platforms with protective barriers and accessible ramps, expanding the use of Hook Turn intersections, and integrating intelligent transportation systems to enhance traffic management. Additionally, addressing behavioral factors is crucial. Enhanced training for tram drivers and educational campaigns for pedestrians and other road users are proposed to improve situational awareness and adherence to safety norms. By addressing both objective systemic issues and subjective behavioral factors, this study aimed to enhance the safety of tram operations in mixed-traffic environments, ultimately reducing collision risks and improving public confidence in tram systems.

Graph-driven multi-vessel long-term trajectories prediction for route planning under complex waters

Current collision avoidance algorithms used in autonomous navigation vessels are hindered by limited long-term visibility of dynamic obstacles, resulting in overly cautious and frequent avoidance maneuvers that can actually increase the risk of collisions in complex waterways. Therefore, this paper proposed a novel graph-driven multi-vessel long-term trajectory prediction method (termed GMLTP), which extends the length of real-time and accurate prediction of other vessel trajectories. GMLTP can enhance collision avoidance algorithms by enabling more anticipatory trajectory prediction, ultimately supporting real-time route planning in creating safer, more efficient, and optimized routes. Specifically, GMLTP consists of a multi-graph spatial convolution (MGSC) layer and a probability sparse (ProbSparse) self-attention Transformer. MGSC is a finer spatial interactions extractor, which supports the modeling of spatial dependency evolution on longer sequences and thus improves the perception of long-term trends, while ProbSparse self-attention Transformer is used to reduce the computation of learning long-term global time dependencies and thus extending the predictable length. Extensive experimental results indicate that GMLTP exhibits better accuracy and generalization in prediction tasks beyond 48-timesteps (each time step is 10 s), which can provide better assistance to current real-time route planning tasks.

Research on Safety Separation Distance in Paired Approach Based on Monte Carlo Simulation

Faced with the increasing number of flights, efficient and orderly operations have become a critical issue for busy airports. The closely spaced parallel runway paired approach technology has been proven to be an effective solution for increasing runway capacity and operational efficiency. This research focuses on determining the safety separation between paired aircraft, particularly the collision safety limit. This study refines the kinematic modeling process by constructing a three-dimensional kinematic model and advancing the starting point of paired approach, making the simulation process more aligned with reality. Subsequently, the Monte Carlo simulation method is applied, integrating the kinematic model and considering collision risks to simulate the entire paired approach process. Multiple scenarios are created, and simulation experiments are conducted to determine the safety separation between the two paired aircraft. Finally, by adjusting parameter settings and taking the example of the two closely spaced parallel runways at Chongqing Jiangbei International Airport, the correlation between various influencing factors and the safety separation is analyzed. Different scenarios are simulated 100,000 times each, and the results show that the speed difference between the two paired aircraft has the most significant impact on the safety separation, followed by the paired approach mode and runway centerline spacing. The wake turbulence category matching has a minimal effect on the safety separation. The results provide a theoretical basis for airports with closely spaced parallel runways to further reduce the separation between paired aircraft, thereby enhancing airport capacity and runway operational efficiency.

Safety evaluation for mixed traffic flow of CAVs with different automation and connection levels

With the development of connected communication and automated driving technologies, connected automated vehicles (CAVs) are expected to gradually replace human-driven vehicles (HDVs), owing to their significant safety advantages. However, widespread adoption of CAVs will still require a considerable amount of time due to constraints such as technological development, legal regulations, and social acceptance. During the transitional period of CAVs’ proliferation, road traffic will comprise vehicles with different levels of connection and automation. These vehicles will differ in technology regarding communication, perception, decision-making, and other aspects, potentially posing severe challenges to mixed traffic flow safety. To address this issue, this paper proposes a mixed traffic flow model from both automation and connection perspectives to consider time delay and communication degradation. Four safety evaluation metrics are employed, and three-speed scenarios are set to investigate the influence of connection levels, automation levels, and their combined effects on mixed traffic flow safety. The result shows that (1) low automation enhances traffic safety in a non-connected environment, whereas medium and high automation decreases traffic safety. Conversely, in a connected environment, the role of automation levels exhibits the opposite pattern. (2) non-connected vehicles enhance traffic safety in a low-automated environment, while connected vehicles diminish it. However, connection level effects are reversed in medium and highly automated environments. (3) In connected and automated environments, traffic safety is increased by non-connected and low-automated vehicles, connected and medium automated vehicles, and connected and highly automated vehicles. However, other vehicle configurations decrease traffic safety. (4) Across various scenarios, the integration of connection and high automation proves beneficial for reducing traffic collision risks and improving traffic safety. In summary, this study provides theoretical support for managing and controlling mixed traffic flow with different automation and connection levels in the future.

Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users' behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: vehicle speed, Pedestrian Safety Margins (PSM), and Predicted Collision Risk (PCR) level. The comprehensive analysis covers a spectrum of behaviors, from simple to complex, assessing the policy's impact in urban environments. The findings reveal several important insights. First, the policy enforcement resulted in a positive impact on vehicle speeds, with average speeds aligning with posted speed limits. Second, an increase was observed in yielding behaviors, particularly in school zones. However, much of this behavior appeared to be cosmetic, emphasizing the need for more safety-oriented yielding practices. Finally, the policy enforcement had a mixed impact on PCR levels, with a reduction in danger levels in school zones, yet an unexpected increase in danger levels in non-school zones. This study provides valuable insights into the effectiveness of strengthened penalty policies in school zones, particularly the development of a safe and sustainable urban environment.

Avoidance and attraction responses of kittiwakes to three offshore wind farms in the North Sea

AbstractSeabird collision risk is a key concern in relation to the environmental impacts associated with offshore wind farms (OWFs). Understanding how species respond both to the wind farm itself, and individual turbines within the wind farm, is key to enabling better quantification and management of collision risk. Collision risk is of particular concern for the black-legged kittiwake, Rissa tridactyla, where modelling predicts unsustainable population level impacts. In this study 20 adult breeding kittiwakes, were tracked with GPS from Whinnyfold, Scotland (57°23′07″N, 001°52′11″W) during the breeding season in 2021. An Avoidance-Attraction Index (AAI) was estimated at several bands within macro- and meso-scales (0–4 km from outer boundary and 0–400 m from turbines, respectively), and the Avoidance Rate (AR; used in environmental impact assessments) at macro-scale to estimate avoidance behaviour to three operational OWFs within their foraging range. One offshore wind farm and its buffer zone (0–4 km from outer boundary) was visited more frequently by the majority of tracked individuals (19/20 birds), despite being twice as far as the closest OWF (17.3 and 31.9 km respectively), whilst 10 or less individuals used the remaining two OWFs. At the most frequented OWF we found macro-scale attraction to the closest band (0–1 km) trending towards avoidance in the furthest band (3–4 km). At the meso-scale we found avoidance of areas below the rotor height range (RHR, a.k.a. rotor swept area/zone) up to 120 m from individual turbines, which decreased to 60 m when within the RHR. Our results indicate that kittiwakes may be slightly attracted to the area around OWFs or aggregate here due to displacement but avoid individual turbines. Increased productivity in the OWF area may potentially be drawing birds into the general area, with aversion to individual turbines being responsible for meso-scale observations.

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce the reliance on trial-and-error learning in real-world scenarios, thereby mitigating the risks associated with dynamic driving conditions and enhancing vehicle foresight. This framework integrates the predictive capabilities of world models with the constrained Markov decision process (CMDP) and safety reinforcement learning to accurately anticipate future environmental changes. This ensures the reliability of autonomous driving routes, thereby improving both safety and efficiency. Furthermore, to reduce trial-and-error costs in real-world applications, this study employs PAC-Bayesian methods to derive generalization error bounds between simulations and reality, enabling a more effective transfer of knowledge and experience from simulations to real-world scenarios. Validation experiments in simulated and real environments showed that Safedrive Dreamer significantly outperformed existing autonomous driving solutions by 3.8% in key safety metrics, excelling in collision avoidance and risk reduction. This study provides new insights into the integration of world models into decision-making processes to enhance decision-making capabilities in safety–critical applications, thereby contributing significantly to the improvement of autonomous driving safety and reliability.

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.

Not all maps are equal: Evaluating approaches for mapping vessel collision risk to large baleen whales

Abstract A growing and increasingly globalised human population, requiring the movement of goods and commodities, is placing increasing demands on the maritime industry, resulting in a concurrent increase in global shipping activities. This has consequences for the marine environment, particularly for species vulnerable to the impacts of vessel traffic. For example, vessel collisions can result in sub‐lethal or fatal injuries for marine mammals, whilst vessel noise can cause acoustic masking that effectively reduces an animal's listening space, potentially impacting their communication, navigation and foraging capacity. While a number of parallel approaches to mapping collision risk to large whales have arisen, these methods vary in their focus, usually on either co‐occurrence, collision probability, or probability of mortality. However, little attention has been given to the implications of methodological choice and data selection on subsequent risk predictions. To assess differences between these approaches, we used a standardised input dataset comprised of telemetry‐point data from tagged bowhead whales, and satellite‐based Automated Identification System (AIS) data of spatial vessel movements covering the Davis‐Baffin Arctic Marine Area. We applied this data to eight different, previously published analyses for deriving areas of vessel risk. We found that the choice of risk mapping approach affected the location, and total area, identified as ‘high risk’, and that more computationally complex approaches did not necessarily equate to different predictions. There was considerable variation in the total area of ‘high risk’ predicted within each map (range = 20–42,246 km2). Synthesis and Applications. The results underscore the importance of methodological transparency, informed data selection and careful interpretation when predicting collision risk. We provide practical recommendations for enhancing transparency when predicting risk, and discuss choice of approach suitable for different situations or management applications. It is critical that managers and policy makers are aware of the implications of applying different approaches when interpreting risk evaluation outputs.

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.