Collision Avoidance Method Research Articles

A game-based decision-making method for multi-ship collaborative collision avoidance reflecting risk attitudes in open waters

To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.

Navigation of a Quadrotor Based on Voronoi Division Calculated from Local Information

This study applies a successful collision-avoidance method using buffered Voronoi cells (BVC) to control quadrotors. In particular, we consider the case of dealing with stationary obstacles using only the local information obtained from sensors, which has not been discussed in previous studies. In this study, we assume an unknown environment with grid-shaped obstacles. We demonstrate that four quadrotors can compute the information required for the BVC-based collision avoidance algorithm and move in the same environment without communicating with each other or receiving information from a central controller, using the local information obtained from the mounted 3D LiDAR. Simulations indicate that the system can handle static obstacles using point-cloud data obtained using 3D LiDAR as Voronoi seeds. We also demonstrate that the BVC-based collision avoidance algorithm can be applied to quadrotors controlled by a simple PD position controller without any modification to the controller. These findings show that the BVC-based collision avoidance can be easily implemented using existing commercial drones.

A novel collaborative collision avoidance decision method for multi-ship encounters in complex waterways

With the continuous growth of maritime traffic volume, multi-ship collision avoidance has gradually become one of the most crucial issues in ensuring the safety of ship navigation. This study proposes a multi-ship collaborative collision avoidance decision model (MCCA) to address the issue of multi-ship encounters in complex waterways. The model takes into account the ship spatio-temporal collision risk, relative motion trends, as well as the safety and economic considerations of ship navigation. A comprehensive collision avoidance method is designed based on the velocity obstacle method, enabling simultaneous adjustments both course and speed, with the aim of minimizing economic losses incurred by ships due to avoidance operations while ensuring ship navigation safety. Meanwhile, the NSGA-II algorithm, which has faster convergence and better effect, is used to calculate the optimal collaborative collision avoidance decision of multiple ships. Besides, both simulation and example validation are used to test the validity of the model. The results indicate that the proposed model can effectively achieve multi-ship collaborative collision avoidance in the region. Simultaneously, it holds significant reference value for guaranteeing the safety of ship navigation, enhancing supervision efficiency, and promoting the development of intelligent ship navigation systems.

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

Autonomous collision avoidance decision-making method for USV based on ATL-TD3 algorithm

To address the issue of decision-making for ship collision avoidance in complex encounter situations, an Autonomous Collision Avoidance Decision-making (ACAD) model based on Attention Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (ATL-TD3) algorithm for Unmanned Surface Vessel (USV) is proposed in this study. The novelty of this method is: (1) Incorporating LSTM and multi-head self-attention mechanism into the existing TD3 algorithm network enhances its focus on pertinent historical state information. Furthermore, the generalization ability of the trained model is further enhanced through optimization of the accumulated experience. (2) A comprehensive reward mechanism is designed, incorporating ship domain, arena, yaw, and other relevant factors, which solve the encounters defined in clauses 13–17 in Chapter 2 of COLREGs and immediate danger. The experiment involved a comparative analysis of the average reward curve and success rate during training under different environments. Additionally, 20 cases of Imazu classic encounters and real navigation data from the Suez Canal were utilized to validate the proposed algorithm. It is highlight that the ACAD model trained by the ATL-TD3 algorithm exhibits remarkable performance in the generalization verification, surpassing conventional collision avoidance methods.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

Collision Avoidance Path Planning and Tracking Control for Autonomous Vehicles Based on Model Predictive Control.

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm.

Intelligent sequential multi-impulse collision avoidance method for non-cooperative spacecraft based on an improved search tree algorithm

The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making collision avoidance significantly more challenging than that for space debris. Much existing research focuses on the continuous thrust model, whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions. Additionally, it is important to minimize the impact on the original mission while avoiding non-cooperative targets. On the other hand, the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer, posing challenges for practical engineering applications. To conquer these difficulties, this paper makes the following key contributions: (A) a turn-based (sequential decision-making) limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time; (B) a novel Selection Probability Learning Adaptive Search-depth Search Tree (SPL-ASST) algorithm is proposed for non-cooperative target avoidance, which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search (MCTS). Numerical simulations confirm the effectiveness and efficiency of the proposed method.

Collision Avoidance and Give Way of Heterogeneous and Variable-Sized Multiple Mobile Robots Based on Glued Nodes

Multiple mobile robot systems (MMRSs) play an important role in workshops, storage and other scenarios. A complex MMRS can contain heterogeneous robots, and even the robot sizes are not fixed, making it more difficult to avoid collisions. As a widely studied method, the zone-controlled method is not accurate enough for a system with heterogeneous and variable-sized robots, and it increases the difficulty of applying MMRS. Based on the concept of glued nodes, this paper proposes a more accurate calculation method than the zone-controlled method to avoid collisions among heterogeneous and variable-sized robots. In order to improve the efficiency of calculating glued nodes, this paper proposes a time-to-space method, which has a constant complexity after the system runs long enough. In addition to collision avoidance, this paper also studies the problem of giving way of idle robots. This paper proposes a method which can detect those idle robots blocking the working robots and plan paths for the idle robots to find avoidance nodes to give way to the working robots. Simulation experiments are carried out based on a real automated production line scenario, and the experimental results prove the effectiveness and efficiency of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In a complex MMRS, the size of different robots can be different, and even the size of a robot is different when loaded and unloaded. One of the motivations of this paper is to avoid collisions in such a complex MMRS. A collision avoidance method is proposed to avoid collisions among robots by allocating nodes in real time. At the same time, this paper also addresses the problem of idle robots blocking working robots. If an idle robot blocks some working robots, an avoidance node will be found for the idle robot to give way to the working robots. The methods proposed in this paper can be applied to the roadmap of any structure. This means that in a scenario, regardless of the structure of roadmap and the different sizes of robots, the methods can be applied directly. The methods have been applied to many practical industrial projects in warehousing, manufacturing and other scenarios, and greatly reduces the difficulty of applying MMRS. In future research, there is a need to improve the efficiency of the methods so that they can be applied to a large-scale system.

Dynamic navigation: Integrating GL-STGCNN and MPC for collision avoidance with future Awareness

Existing ship dynamic collision avoidance methods mostly rely on the instantaneous motion information of surrounding ships to make decisions. This makes it difficult to adapt to changes in the motion states of surrounding ships, which may lead to collisions between ships. To improve the safety of dynamic collision avoidance methods, this paper combines the multi-ship trajectory prediction model GL-STGCNN with model predictive control for ship dynamic collision avoidance tasks. Firstly, the interaction between ships is extracted through GL-STGCNN to predict the future trajectories of surrounding ships. Then, the objective function based on the artificial potential field method and the velocity obstacle method is optimized to control the ship to complete the dynamic collision avoidance task. The performance of the dynamic collision avoidance method is verified and analyzed in the ship navigation scenario simulated by AIS data. The experiments show that the new ship dynamic collision avoidance method not only complies with the COLREGs, but also can flexibly select the collision avoidance method according to different scenarios. In addition, the theoretical collision avoidance threshold distance based on the MPC objective function shows a high degree of fit with the actual collision avoidance trigger distance observed in the simulation verification.

A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

Collision avoidance is critical in unmanned surface vehicles (USVs) and is especially challenging in scenarios involving ships of different intelligence levels. To address this problem, inspired by risk analysis and collision avoidance habits of seafarers, this paper proposes a human-like collision avoidance method based on deep reinforcement learning (DRL) and velocity obstacle (VO). It first introduces a navigation impact factor (NIF) calculation module based on fuzzy theory to simulate human beings’ attention mechanisms when facing multiple ships. To be compatible with human beings’ regulations, the convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is first to be integrated into the calculation of the NIF calculation module, and the VO algorithm is incorporated into the reward function, which can reduce collision risks in paths and improve safety requirements. In addition, a series of reward functions are carefully designed to balance safety, smoothness, and driving operation. To validate the performance, experiments are conducted on our virtual simulation platform. The results show that our algorithm can accurately assess the impact of target ships (TS) on own ship (OS) in complex environments and can obey the COLREGs. Collision avoidance can be achieved effectively and the path is smoother.

Solar Operated Car Enabled with AI

The convergence of renewable energy and artificial intelligence (AI) has spurred a transportation revolution, exemplified by the rise of solar-powered cars equipped with advanced AI capabilities. This abstract digs into the integration of solar energy and AI technology in autos, examining their synergistic potential to transform the automotive industry and pave the way for a more sustainable future.The paper presents the concept of solar-powered cars and discusses their importance in reducing environmental concerns connected with regular fossil-fuelled vehicles. Solar energy, captured by solar panels built into the vehicle's construction, provides a clean and abundant power source, minimizing reliance on non-renewable resources and lowering greenhouse gas emissions. Furthermore, advances in solar panel efficiency and energy storage technology have increased the practicality of solar-powered vehicles for widespread use.. Furthermore, AI-powered autonomous driving capabilities improve safety by reducing human error and allowing for proactive collision avoidance methods. The paper highlights how AI-enabled solar-powered vehicles have the potential to completely change the automotive sector and hasten the shift to a sustainable transportation network

Collision avoidance decision-making strategy for multiple USVs based on Deep Reinforcement Learning algorithm

—To address the issue of flexible decision-making for Unmanned Surface Vehicles (USVs) in emergency collision avoidance scenarios, this paper proposes a Collision Avoidance Decision-making Strategy (CADMS) for multiple USVs based on Deep Reinforcement Learning (DRL). The novelty of this method is: (1) The Preferential Experience Replay Mechanism (PERM) and the Gate Recurrent Unit (GRU) network are employed to enhance the Deep Deterministic Policy Gradient (DDPG) algorithm. The PERM facilitates faster convergence by increasing the sampling probability, thereby aiding in data assimilation. Meanwhile, the GRU enables self-assessment of sensor input significance, leading to more accurate action prediction. (2) In the design of the reward function, the departure from COLREGs has been fully considered, including the two-ship encounter situations and the multi-ship encounter situations. This study also provides a comprehensive analysis of neural network training and generalization verification. The experimental results demonstrate the effectiveness of the proposed approach in effectively avoiding collisions with multiple USVs in complex water environments. It is noteworthy that in comparison to conventional collision avoidance methods, the proposed CADMS model exhibits a superior collision avoidance success rate and enables safer and more efficient decision-making behaviors when confronted with intricate encounter situations.

Collision avoidance for formation of autonomous ships using separation and recovery mechanism combine artificial potential field method

ABSTRACT It is essential to investigate the path planning and tracking problem for maritime autonomous surface ships (MASS). This study proposed an automatic collision avoidance method based on the separation and recovery mechanism with formation of MASS (F-MASS). First, the marine environment model utilized S-57 electric navigation chart (ENC) data for construction. The Scan-searching method was proposed for multipath and path-optimized redistribution method was developed to address MASS congestion. The formation pattern transformation was settled by separation and recovery mechanism. Considered the ‘unmanned–manned’ encounter situation, formulated avoidance strategies for both the MASS formation and manned ships encounters. Finally, the simulation experiment was carried out in the sea near Zhoushan port to verify the effectiveness of the algorithm. The results indicated that the method can satisfy the integrated operation of collision avoidance in a complex environment and can offer effective collision avoidance strategies for F-MASSs navigating in complex marine environments.

Coordinating Obstacle Avoidance of a Redundant Dual-Arm Nursing-Care Robot.

Collision safety is an essential issue for dual-arm nursing-care robots. However, for coordinating operations, there is no suitable method to synchronously avoid collisions between two arms (self-collision) and collisions between an arm and the environment (environment-collision). Therefore, based on the self-motion characteristics of the dual-arm robot's redundant arms, an improved motion controlling algorithm is proposed. This study introduces several key improvements to existing methods. Firstly, the volume of the robotic arms was modeled using a capsule-enveloping method to more accurately reflect their actual structure. Secondly, the gradient projection method was applied in the kinematic analysis to calculate the shortest distances between the left arm, right arm, and the environment, ensuring effective avoidance of the self-collision and environment-collision. Additionally, distance thresholds were introduced to evaluate collision risks, and a velocity weight was used to control the smooth coordinating arm motion. After that, experiments of coordinating obstacle avoidance showed that when the redundant dual-arm robot is holding an object, the coordinating operation was completed while avoiding self-collision and environment-collision. The collision-avoidance method could provide potential benefits for various scenarios, such as medical robots and rehabilitating robots.

Personalized driving behavior oriented autonomous vehicle control for typical traffic situations

autonomous driving systems not only provide services for human drivers, but also need to consider the personalized driving requirements of human beings. In current road traffic environments, the driving behaviors of drivers differ significantly. This suggests that the various needs of different drivers cannot be met by a single behavior mode in an autonomous driving decision-making system. This paper looks at the personalized characteristics of various drivers and considers the implications of their differences. First, a Proportional Integral Differential (PID) feedback channel is introduced in a traditional Model Predictive Control (MPC) to improve the performance of the controller, and comprehensively considering the collision risk, motion hysteresis and rule constraints, referring to the MPC idea, a collision avoidance method based on Q-ABSAS optimization is proposed. Then based on the Chance Constrained Programming, the control constraint is combined with driver personalization to reflect a variety of driving personality characteristics. Finally, the proposed method is tested using Hardware-in-the-loop (HIL) experiments. The experiment results demonstrate that the proposed method can successfully implement vehicle tracking control and make the vehicle's state of movement match the driver's expectations, which can increase driver comfort and driving safety.

AN EFFECTIVE FRAMEWORK TO IMPROVE PERFORMANCE AND SECURITY IN INTELLECTUAL VANET

Vehicular Ad Hoc Networks, also known as VANETs, are an essential component in the development of intelligent transportation systems. They hold the promise of improving commuters' safety, efficiency, and convenience. The dynamic and decentralised character of VANETs, on the other hand, presents a number of issues, notably with regard to performance and security. a complete framework that is aimed to enhance the operation of VANETs and address the difficulties that have been identified. Intelligent routing algorithms, collaborationbased collision avoidance methods, and robust security protocols are the three primary components that make up the system. In the first place, intelligent routing algorithms make use of machine learning techniques in order to dynamically adapt to shifting traffic circumstances. This increases the efficiency of data transmission while simultaneously reducing delays. Whendetermining how to properly route data packets over the network, these algorithms take into consideration a variety of parameters, including the density of vehicles, traffic patterns, and congestion on the network.

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

Formation control and collision avoidance of unmanned water surface vehicles in maritime environments

The formation control of a team of unmanned water surface vehicles (USVs) experiencing maritime environmental disturbances is investigated. These perturbations include water waves, ocean current, wind gusts, communication and measurement errors, and other unmodeled dynamics that are captured through stochastic noise. A distributed control scheme leveraging local communication among team members is proposed to drive the group towards a set of waypoints while simultaneously achieving a desired spatial configuration and collision avoidance. The exponential mean square convergence of the proposed control scheme is shown under a version of the Lyapunov stability theory adapted to stochastic systems. Closed form expressions are proposed to estimate model parameters from experimental data collected in operating environments. The effectiveness of the proposed formation control scheme and collision avoidance method is assessed through numerical simulations demonstrating the interplay between the control parameters and the intensity of the noise. It also highlight the crucial role of the stochastic noise in resolving potential singularities or deadlocks in the vehicles’ motion , which originate from the collision avoidance forces.

MCCA: A Decentralized Method for Collision and Deadlock Avoidance With Nonholonomic Robots

MCCA: A Decentralized Method for Collision and Deadlock Avoidance With Nonholonomic Robots