Collision Avoidance Technology Research Articles

Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement.

Technology roadmap of risk identification and collision avoidance decision-making in autonomous vehicles for domestic animals

At present, the multi-targets autonomous vehicles identify are mainly various road users such as vehicles and pedestrians in cities. However, in recent years, there are increasing vehicle-animal accidents in pastoral areas of China, and there is a lack of corresponding practical intelligent safety warning measurements. In order to realise the identification and collision avoidance of typical animals, this study obtains the basic characteristics of collisions and the animals’ behavioural characteristics from the pastoral accidents investigation. Firstly, an attempt to focus on the use of the algorithm of the binocular infra-red equipment is made, with the main targets of identification being domestic animals at night or under low brightness conditions. Secondly, the algorithm technology roadmap from the identification and the risk assessment to collision avoidance decision-making is designed. Further, based on the technical route, a feasible solution and overall technical program is presented, which outlines the five aspects of the proposed engineering-ready scheme’s architecture. The methodology used involves transfer learning and the PSMNet network model. The paper concludes that the system can predict the animal’s movement and make proper decisions by utilising binocular infra-red equipment, integrating its algorithms and the support of its behavioural risk assessment. The research results will contribute to the animal collision avoidance technology and the traffic safety in pastoral areas in the future.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Design and application of automatic driving emergency collision avoidance control algorithm based on artificial intelligence technology

The topic of car intelligence has seen a surge in research interest in automobile active collision avoidance systems in recent years. When there is an obstruction in front of the vehicle, active braking or active steering can be used to prevent a collision, which effectively increases driving safety. Research on intelligent automotive active collision avoidance technology is valuable both academically and practically. This paper aims to enhance the adaptability of active collision avoidance systems to various working conditions. To this end, we propose a design scheme that synchronizes the steering and braking collision avoidance strategies. Additionally, we derive a risk assessment model that takes into account both vehicle dynamics instability factors and vehicle collision factors at the same time. This approach effectively addresses the challenge of effectively assessing the risk of self-driving cars in emergency situations and helps to quantify the risk associated with them. Lastly, a simulation and experimental analysis are conducted on the active collision avoidance strategy that is suggested in this study. The findings demonstrate that the active collision avoidance control system for intelligent vehicles, both longitudinal and transverse, developed in this paper can accurately assess collision risk, make decisions, and manage the collision avoidance process. It can also further decrease the frequency of collision accidents.

잠수함-민간선 충돌 사고 사례 분석 및 충돌 방지 기술 조사

잠수함-민간선 충돌 사고 사례 분석 및 충돌 방지 기술 조사

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

A review of rule-based collision avoidance technology for autonomous UAV

A review of rule-based collision avoidance technology for autonomous UAV

Intelligent ship collision avoidance model integrating human thinking experience

With the maturation of intelligent ship collision avoidance technology, the acceptability of this technology has gained attention and the ship collision avoidance model is confronting interpretability and trustworthiness difficulties. Therefore, this paper integrates human thinking experience into actual collision avoidance decision-making to propose an intelligent collision avoidance model that is more in line with the operator ‘s understanding, thus improving trustworthiness and autonomy. The general architecture of collision avoidance model is divided into two modules: a basic model and an analytical model, by adopting from human thinking patterns of fast and slow. The basic model considers the COLREGS and navigation experience to solve the encounter situation of two ships. The analytical model addresses the collision avoidance issues in complex scenarios with multi-ships, by introducing the repulsive force and the driving force parts of the social force model, which are used to implement course mapping adjustment and gentle course fluctuation respectively. Additionally, a field theory-based effect evaluation model is designed to blend human experience with algorithmic benefits for making the optimal decision in complex scenarios. The results show that when the basic model and the analytical model work together, this architecture performs effectively in a variety of scenarios. It is worth mentioning that the collision avoidance model can potentially be developed to advance collision-free and human-like process trajectory navigation in multi-ship situations.

Planning and control of drifting-based collision avoidance strategy under emergency driving conditions

Automated emergency collision avoidance (CA) technology has made tremendous progresses in recent years. However, the existing CA approaches, either braking or steering based, cannot handle well the extremely emergency CA scenarios with limited space and time. This is a research gap to be bridged before the zero-fatality vision of road traffic, namely Vision Zero, can be realized. To further extend the safety limits of vehicles, especially that of automated vehicles in future, we propose a drifting-based CA approach that adopts an aggressive, but controllable, over-steering maneuver. To this end, first the trajectory planning of drifting is formulated and solved as a constrained optimal control problem, which considers vehicle dynamics, actuator limits and collision constraints. Then the knowledge of the three CA approaches’ capability limits is obtained and used for CA decision. Given a planned trajectory, a feedback linearization based controller is further designed to complete the drifting maneuver. Finally, using a real vehicle with by-wire actuators, a series of simulations and road tests are carried out. Results show that our proposed drifting-based algorithm can successfully fulfill agile CA tasks in extreme conditions. With further improvements, this preliminary and conceptual work may contribute as a promising complement to current active safety technologies.

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

AbstractIn recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions. The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non‐cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non‐cooperative flying objects. In this article, we review the topic of non‐cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non‐cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

LIGHT PAYLOAD QUADCOPTER DESIGN FOR THE TRANSPORTATION OF ESSENTIAL GOODS TO PEOPLE IN SELF-ISOLATION

A quadcopter, also called a quadrotor helicopter or quadrotor is a multirotor helicopter that is lifted and propelled by four rotors. In case of lockdown or self-isolation, companies as well as governments can make use of unmanned aerial vehicles equipped with obstacle detection and collision avoidance technology. It is paramount to understand the importance of flight safety with unmanned aerial vehicles. If rules and restrictions are not followed because the user with the controller made an error or was unconscious, a sophisticated system will detect possible conflict and save the drone before collision. In this paper, a digital model was created using MatLab software, in order to analyze and understand how a quadcopter works in its environment. Using the trajectory tools, a well-defined trajectory is defined for a quadcopter in order to better represent its behavior during a light parcel transport assignment. The results obtained can be used to design a drone for the delivery of small packages containing essential goods for people in self-isolation.

Computer Vision Algorithms, Remote Sensing Data Fusion Techniques, and Mapping and Navigation Tools in the Industry 4.0-Based Slovak Automotive Sector

The objectives of this paper, and the novelty brought to the topic of the Industry 4.0 manufacturing systems, are related to the integration of computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Slovak automotive sector. We conducted a thorough examination of Industry 4.0-based value and supply chains, clarifying how cyber-physical production systems operate in relation to collision avoidance technologies, environment mapping algorithms, and mobility simulation tools in network connectivity systems through vehicle navigation data. The Citroen C3 and Peugeot 208 automobiles are two examples of high-tech products whose worldwide value and supply chain development trends were examined in this study by determining countries and their contributions to production. The fundamental components of the research—statistical analysis and visual analysis—were utilized in conjunction with a variety of syntheses, comparisons, and analytical methodologies. A case study was developed using PSA Group SVK data. The graphical analysis revealed that Slovakia offers the second-highest added value to the chosen items, but it also highlighted the country’s slow-growing research and development (R&D) infrastructure, which could lead to a subsequent loss of investment and business as usual. Slovakia can generate better export added value by optimizing Industry 4.0-based manufacturing systems in the automotive sector.

Frequency Sweep Keying CDMA for Reducing Ultrasonic Crosstalk

Various sensors are embedded in automobiles to implement intelligent safety technologies such as autonomous driving and front–rear collision avoidance technology. In particular, ultrasonic sensors have been used in the past because they have an accuracy of centimeters to sub-centimeters in air despite their low cost and low hardware complexity. Recently, the crosstalk problem between ultrasonic sensors has been raised because the number of ultrasonic sensors in the unit space has increased as the number of vehicles increases. Various studies have been conducted to solve the crosstalk, but a demodulation error occurs when signals overlap. Therefore, in this paper, we propose a method that is robust to ultrasonic signal overlap, is robust even at shorter code length, and has reduced time of flight (TOF) error compared to the existing method by applying frequency sweep keying modulation based on code division multiple access (CDMA). As a result of the experiment, the code was detected accurately regardless of the overlap ratio of the two signals, and it was robust even in situations where the power of the two signals was different. In addition, it shows an accurate TOF estimation even if the ID code length is shorter than the existing on–off-keying, frequency shift keying, and phase shift keying methods.

A Novel Reinforcement Learning Collision Avoidance Algorithm for USVs Based on Maneuvering Characteristics and COLREGs

Autonomous collision avoidance technology provides an intelligent method for unmanned surface vehicles’ (USVs) safe and efficient navigation. In this paper, the USV collision avoidance problem under the constraint of the international regulations for preventing collisions at sea (COLREGs) was studied. Here, a reinforcement learning collision avoidance (RLCA) algorithm is proposed that complies with USV maneuverability. Notably, the reinforcement learning agent does not require any prior knowledge about USV collision avoidance from humans to learn collision avoidance motions well. The double-DQN method was used to reduce the overestimation of the action-value function. A dueling network architecture was adopted to clearly distinguish the difference between a great state and an excellent action. Aiming at the problem of agent exploration, a method based on the characteristics of USV collision avoidance, the category-based exploration method, can improve the exploration ability of the USV. Because a large number of turning behaviors in the early steps may affect the training, a method to discard some of the transitions was designed, which can improve the effectiveness of the algorithm. A finite Markov decision process (MDP) that conforms to the USVs’ maneuverability and COLREGs was used for the agent training. The RLCA algorithm was tested in a marine simulation environment in many different USV encounters, which showed a higher average reward. The RLCA algorithm bridged the divide between USV navigation status information and collision avoidance behavior, resulting in successfully planning a safe and economical path to the terminal.

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Highlight Experience Replay</i> and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages.

Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems

Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems

Collision avoidance control for Unmanned Autonomous Vehicles (UAV): Recent advancements and future prospects

The recent advances in collision avoidance technologies for unmanned vehicles such as UAVs, AUVs, AGVs, and USVs have greatly advanced the industry. Their lower cost and acceptability of high-risk missions have enabled the development of collision avoidance controllers for autonomous vehicles. These low-maintenance gadgets are also portable, need low maintenance, and enable continuous monitoring to occur near real-time. This may be said; however it would be incorrect, because collision avoidance controllers have been related with compromises that affect data dependability. Research on collision avoidance controls is quickly developing; therefore it is distributed throughout multiple papers, projects, and grey literature. This report critically reviews the recent relevant research on creating collision avoidance systems for autonomous vehicles. Typically, the assessment measures are dependent on the algorithm's use case and the platform's capabilities. The full evaluation of the benefits and drawbacks of the most prevalent approaches in the present state of the art is provided based on 7 metrics which are complexity, communication dependence, pre-mission planning, robustness, 3D compatibility, real-time performance and escape trajectories.

Intelligent collision avoidance algorithms for USVs via deep reinforcement learning under COLREGs

In the field of unmanned surface vehicles, intelligent collision avoidance technology is essential to ensure the safety of navigating. In this paper, the problem of avoiding moving boats for USVs under the constraints of COLREGs is studied. A COLREGs intelligent collision avoidance (CICA) algorithm based on deep reinforcement learning is proposed, which can automatically extract state features by using powerful deep neural networks. The reward function is designed, which ensures that the USV navigates to the target while obeying COLREGs to avoid dynamic obstacles. A method is proposed to track the current network weight to update the target network weight, which improves the stability of the algorithm in learning the optimal strategy. It is shown that the CICA algorithm converges with fewer training times through ε-greedy with both decaying ε and reward threshold than other three strategies. By comparing the CICA algorithm with the artificial potential field method and the velocity obstacle method, it is concluded that the CICA algorithm is superior to the other two algorithms.

Adequacy of Manitoba concrete bridge rail during truck platoon impacts and associated occupant risks

Platooning is an extension of Cooperative Adaptive Cruise Control (CACC) and forward collision avoidance technology; it allows lateral and longitudinal control of vehicles while they move in tight formations. Platooning is ideal for trucks as they usually travel long distances in highways as a group. It is necessary to understand if the existing roadside safety devices are adequate to resist the consecutive impacts at high speed that can occur due to errant truck platoons. It is also important to analyze the associated occupant risks during such impact events. A detailed analysis of the capacity and adequacy of Manitoba Concrete Bridge Rail using computer simulations, under Manual for Assessing Safety Hardware (MASH) Test Level 5 (TL5) criteria, is presented in this study. The study briefly discusses the methodology to run multiple impact computer simulations in LS-DYNA. The impact of the leading truck is validated using the known data from a full-scale crash test; then, the impact events involving the following trucks are simulated to understand the additional capacity of the roadside barrier. To assess the occupant risk, the flail space model (FSM) concept is utilised as per MASH criteria. Simulation results imply that catastrophic barrier failure and major injuries to the occupants are unlikely to occur during the truck platoon impact.

Drones become even more insect-like.

Mosquitoes' exceptional sensitivity to sound and airflow inspires new collision avoidance technology