Collision Avoidance Scheme Research Articles

Multisensor Multitarget Tracking and Collision Avoidance Based on the Labeled RFS Void Probability and KL-PaC Cost Function

Existing sensor control methods for multi-sensor and multi-target tracking seldom consider comprehensive control strategy and collision avoidance between sensors and targets or between sensors together. In this paper, we first propose a cost function for multi-sensor control strategy, which includes Kullback-Leibler (K-L) divergence, detection probability and sensor usage cost (KL-PaC cost function). Second, in order to solve collision avoidance between sensors and targets, we propose a collision avoidance strategy based on the labeled random finite set (RFS) void probability. The strategy is essentially to set up a safe region around the sensor or target, and then combine the void probability and cost function in the safe region as the evaluation function. The final simulation and experimental results verify the effectiveness of the proposed approach.

FREE-RDW: A Multi-user Redirected Walking Method for Supporting Non-forward Steps.

Multi-user redirected walking (RDW) is widely used in large-scale virtual scenes because it allows more users to move synchronously in both virtual and physical environments. To ensure the freedom of virtual roaming, which can be used in various situations, some redirected algorithms have been dedicated to non-forward movements, such as vertical movement and jumping. However, the existing RDW methods still mainly focus on forward steps, ignoring sideward and backward steps, which are also common and necessary in virtual reality. RDW algorithms for non-forward steps can enrich the movement direction of users' virtual roaming and improve the realism of VR roaming. In addition, the non-forward motions have a larger curvature gain, which can be used to better reduce resets in RDW. Therefore, this paper presents a new method of multi-user redirected walking for supporting non-forward steps (FREE-RDW), which adds the options of sideward and backward steps to extend the VR locomotion. Our method adopts a user collision avoidance strategy based on optimal reciprocal collision avoidance (ORCA) and optimizes it into a linear programming problem to obtain the optimal velocity for users. Furthermore, our method uses APF to expose the user to repulsive forces from other users and walls, thus further reducing potential collisions and improving the utilization of physical space. The experiments show that our method performs well in virtual scenes with forward and non-forward steps. In addition, our method can significantly reduce the number of resets compared with reactive RDW algorithms such as DDB-RDW and APF-RDW in multi-user forward-step virtual scenes.

A Novel Collision Avoidance Strategy with D2D Communications for UAV Systems

In recent years, safety operation issues related to the autonomous flight of unmanned aerial vehicles (UAVs) have become popular research and development topics worldwide. Among all UAV applications, multiple UAV-related applications are emerging due to the integration of UAVs into 6G networks, which is an important topic for next-generation wireless communication systems. For multiple UAV applications, flight safety among UAVs is the most significant issue. Therefore, collision avoidance for UAVs has become an emerging topic in UAV-related research. In the past, although many UAV collision avoidance methods have been proposed, there is still a probability of other problems, such as no possible avoidance route and unmanaged UAVs that are without centralized control, which both result in an unpredictable risk of collisions. In this study, we investigate the current existing methods and propose novel collision avoidance methods based on the elastic collision principle. To verify the performance of the proposed methods, we also conduct simulations in this paper to demonstrate their effectiveness. From the simulation results, it can be seen that the proposed methods can effectively perform collision avoidance for multiple UAVs. Specifically, using the proposed methods, all UAVs can reach their destination points within reasonable time resources without any collision, validating the effectiveness of the proposed methods.

Study on Collision Avoidance Strategies Based on Social Force Model Considering Stochastic Motion of Pedestrians in Mixed Traffic Scenario

In typical traffic scenarios where there are no clear separations between the traffic participants, such as mixed traffic or shared space, vehicles and pedestrians are usually moving in the same time so that ego vehicle may need to face with multiple pedestrians in a relatively short interaction distance. Considering the stochastic motion of pedestrians and to balance the time consumption and safety during passing process, this paper proposes two strategies of collision avoidance (CA) for ego vehicle, which are based on model predictive control (MPC) and social force model (SFM). Besides, a modified SFM-based pedestrian model that considers the stochastic motion is given to evaluate the effectiveness of the proposed strategies. For MPC-based CA strategy, considering the unpredictable motion of the pedestrians, a novel speed re-planning layer combined with collision probability estimation, which is used to calculate an acceptable maximum safe speed for ego vehicle, is proposed. On the other hand, parameters associated with the SFM-based vehicle model are re-calibrated by particle swarm optimization (PSO) and the calibration process has been analyzed physically in details. The recommended values based on different initial interaction speed and distance of vehicle and pedestrians are also determined for further reference as useful findings from the analysis.

Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review

Autonomous decision-making for ships to avoid collision is core to the autonomous navigation of intelligent ships. In recent years, related research has shown explosive growth. However, owing to the complex constraints of navigation environments, the Convention of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs), and the underactuated characteristics of ships, it is extremely challenging to design a decision-making algorithm for autonomous collision avoidance (CA) that is practically useful. Based on the investigation of many studies, current decision-making algorithms can be attributed to three strategies: alteration of course alone, alteration of speed alone, and alteration of both course and speed. This study discusses the implementation methods of each strategy in detail and compares the specific ways, applicable scenes, and limiting conditions of these methods to achieve alteration of course and/or speed to avoid collision, especially their advantages and disadvantages. Additionally, this study quantitatively analyzes the coupling mechanisms of alterations of course and speed for autonomous CA decision-making under different encounter situations, supplementing and optimizing the decision-making theory for ship autonomous CA. Finally, several feasible algorithms and improvement schemes for autonomous CA decision-making, combined with course and speed alterations, are discussed.

3D collision avoidance strategy and performance evaluation for human–robot collaborative systems

Collaborative robots (cobots) are one of the newest technologies helpful to improve the performance of the systems. This is because of their ability to combine the high-level productivity, typical of automatic machines, with the flexibility, typical of manual systems. Moreover, they can work directly with human operators, in the same work area; however, the shared workspace is both the greatest advantage and limit of collaborative robots. In collaborative applications it is important to understand how this interaction between different resources can affect the performance of the system since an emergency stop can be generated due to interference between them, reducing so the efficiency of the system. Indeed, a collision avoidance strategy can be introduced in order to promote the prosecution of the movement, but safely. Therefore, a 3D collision avoidance strategy is presented in this work, which is experimentally validated to prove the goodness of the proposed method. Finally, the performance of the systems is investigated through simulation and experimental tests to comprehend the effects of the dimension of the collaboration area with respect to the workspace. From the results, it is possible to understand that the reduction in performance, because of an increase in the collaboration area, is mitigated by the introduction of the collision avoidance strategy, since fewer emergency stops are required.

Research on MASS Collision Avoidance in Complex Waters Based on Deep Reinforcement Learning

The research on decision-making models of ship collision avoidance is confronted with numerous challenges. These challenges encompass inadequate consideration of complex factors, including but not limited to open water scenarios, the absence of static obstacle considerations, and insufficient attention given to avoiding collisions between manned ships and MASSs. A decision model for MASS collision avoidance is proposed to overcome these limitations by integrating the strengths of model-based and model-free methods in reinforcement learning. This model incorporates S-57 chart information, AIS data, and the Dyna framework to improve effectiveness. (1) When the MASS’s navigation task is known, a static navigation environment is built based on S-57 chart information, and the Voronoi diagram and improved A* algorithm are used to obtain the energy-saving optimal static path as the planned sea route. (2) Given the small main dimensions of an MASS, which is easily affected by wind and current factors, the motion model of an MASS is established based on the MMG model considering wind and current factors. At the same time, AIS data are used to extract the target ship (manned ship) data. (3) According to the characteristics of the actual navigation of ships at sea, the state space, action space, and reward function of the reinforcement learning algorithm are designed. The MASS collision avoidance decision model based on the Dyna-DQN model is established. Based on the DQN algorithm, the agent (MASS) and the environment interact continuously, and the actual interaction data generated are used for the iterative update of the collision avoidance strategy and the training of the environment model. Then, the environment model is used to generate a series of simulated empirical data to promote the iterative update of the strategy. Using the waters near the South China Sea as the research object for simulation verification, the navigation tasks are divided into three categories: only considering static obstacles, following the planned sea route considering static obstacles, and following the planned sea route considering both static and dynamic obstacles. The results show that through repeated simulation experiments, an MASS can complete the navigation task without colliding with static and dynamic obstacles. Therefore, the proposed method can be used in the intelligent collision avoidance module of MASSs and is an effective MASS collision avoidance method.

A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks

With the increasing attention to front-edge vehicular communication applications, distributed resource allocation is beneficial to the direct communications between vehicle nodes. However, in highly dynamic distributed vehicular networks, quality of service (QoS) of the systems would degrade dramatically because of serious packet collisions in the absence of sufficient link knowledge. Focusing on the fairness optimization, a Q-learning-based collision avoidance (QCA) scheme, which is characterized by an ingenious bidirectional backoff reward model R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QCA</inf> corresponding to arbitrary backoff stage transitions, has been proposed in an intelligent distributed media access control protocol. In QCA, an intelligent bidirectional backoff agent based on the Markov decision process model can actively motivate each vehicle agent to update itself toward an optimal backoff sub-intervel BSI <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> through either positive or negative bidirectional transition individually, resulting in the distinct fair communication with a proper balance of the resource allocation. According to the reinforcement learning theory, the problem of goodness evaluation on the backoff stage self-selection policy is equal to the problem of maximizing Q function of the vehicle in the current environment. The final decision on BSI <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> related to an optimal contention window range was solved through maximizing the Q value or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q_{\max}$</tex> . The ε-qreedy algorithm was used to keep a reasonable convergence of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q_{\max}$</tex> solution. For the fairness evaluation of QCA, four kinds of dynamic impacts on the vehicular networks were investigated: mobility, density, payload size, and data rate with a network simulator NS2. Consequently, QCA can achieve fair communication efficiently and robustly, with advantages of superior Jain's fairness index, relatively high packet delivery ratio, and low time delay.

Time-varying formation prescribed performance control with collision avoidance for multi-agent systems subject to mismatched disturbances

The time-varying formation problem is addressed for second-order multi-agent systems with matched and mismatched disturbances. A time-varying formation prescribed performance control method is presented with improved collision avoidance and connectivity maintenance scheme. Specifically, more accurate judgment mechanisms of collision risks and connectivity interruption risks are proposed to reduce conservatism of the existing results, by making use of the velocity information and analyzing the maneuvering process of agents. Then, a nonlinear disturbance observer is adopted to estimate those disturbances, and an adaptive prescribed performance controller based on a novel performance function is developed to obtain the predesigned transient performance and steady-state performance. Finally, theoretical analysis, numerical simulation, and actual experiments are given to verify the proposed method.

Trajectory planning for satellite cluster reconfigurations with sequential convex programming method

This paper presents trajectory planning algorithms for large-scale satellite clusters reconfiguration based on sequential convex programming, and these algorithms consider fuel consumption and collision avoidance. Firstly, the trajectory planning problem is formulated as a nonconvex optimal control problem with nonlinear dynamics and nonconvex path constraints. Secondly, the original nonlinear continuous optimal control problem is transformed into a discrete convex optimization subproblem through linearization and discretization. Collision avoidance strategy between discrete points and obstacle avoidance constraints are considered in the convex subproblem to ensure that the trajectories are collision-free. Thirdly, coupled and decoupled sequential convex programming methods are proposed for rapidly generating collision-free and fuel-efficient trajectories. Finally, comparative numerical simulations are presented to demonstrate that as the number of satellites increases, 94 to 99 percent performance improvement over the pseudo-spectral method is achieved in terms of computational efficiency.

A collaborative collision avoidance strategy for autonomous ships under mixed scenarios

AbstractShip collision avoidance has always been one of the classic topics in the field of marine research. In traditional encounter situations, officers on watch (OOWs) usually use a very high frequency (VHF) radio to coordinate each other. In recent years, with the continuous development of autonomous ships, there will be a mixed situation where ships of different levels of autonomy coexist at the same time. Under such a scenario, different decision makers have different perceptions of the current scene and different decision-making logic, so conventional collision avoidance methods may not be applicable. Therefore, this paper proposes a collaborative collision avoidance strategy for multi-ship collision avoidance under mixed scenarios. It builds a multi-ship cooperative network to determine cooperative objects and timing, at the same time. Based on a cooperative game model, a global collision avoidance responsibility distribution that satisfies group rationality and individual rationality is realised, and finally achieves a collaborative strategy according to the generalised reciprocal velocity obstacle (GRVO) algorithm. Case studies show that the strategy proposed in this paper can make all ships pass each other clearly and safely.

Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN–LSTM fusion network

This paper is concerned with the autonomous effective collision avoidance strategy for multiple unmanned aerial vehicles (multi-UAV) in limited airspace under the framework of proximal policy optimization (PPO) algorithm. An end-to-end deep reinforcement learning (DRL) control strategy and a potential-based reward function are designed. Next, the CNN-LSTM (CL) fusion network is constructed by fusing the convolutional neural network (CNN) and the long short-term memory network (LSTM), which realizes the feature interaction among the information of multi-UAV. Then, a generalized integral compensator (GIC) is introduced into the actor-critic structure, and the CLPPO-GIC algorithm is proposed by combining CL and GIC. Finally, we validate the learned policy in various simulation environments by performance evaluation. The simulation results show that the introduction of the LSTM network and GIC can further improve the efficiency of collision avoidance, and the robustness and accuracy of the algorithm are verified in different environments.

Path optimization for mass emergency evacuation based on an integrated model

The current study proposes an evacuation optimization model (IEO model) for mass emergency evacuation, which aims to minimize the average completion time, avoid traffic congestion, optimize the network utilization rate, and balance the exit loads. Pedestrians are divided into free evacuation pedestrians and organized evacuation pedestrians. The K-means algorithm and improved parameter adaptive DBSCAN algorithm are adopted to divide organized evacuation pedestrians into appropriate groups. Combined with an improved ant colony algorithm and the collision avoidance strategy, route scheduling for organized evacuation groups is carried out. A representative case is employed to benchmark the performance of the proposed model. The universality of the proposed model targeting different failure time of key exit and evacuation scale are analyzed. The developed model shows obvious advantages in mass evacuation compared with traditional statistical methods.

Automatic collision avoidance algorithm based on route-plan-guided artificial potential field method

With respect to navigation practices and real-ship applications, this study provided a stable and reliable technical program for automatic ship navigation. First, an improved route-plan-guided artificial potential field (APF) method was presented by balancing the attractive and repulsive forces, resolving the irrationality and swaying of the collision avoidance (CA) strategy, and extending the applicability of the International Regulations for Preventing Collisions at Sea (COLREGS). Second, a fast local path planning (FLPP) method was proposed based on the high-precision ship motion model to obtain the expected CA results in advance, and to promote its acceptance and application by the navigators. Third, a dynamic goal-guided APF method was proposed to navigate a ship to return to its route plan upon completing the CA procedure. A series of CA experiments for different ships and encountering situations were conduct in a simulated environment. Although the varying maneuverability influenced the CA procedure and results, the CA processes and trends were identical, and the track zone width was within 0.10 nm in restricted waters. The real-time CA track sufficiently aligned with the FLPP results, and the ship was able to rapidly return to its route plan and remained within the cross track limit (XTL) according to the set return parameters.

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

Reinforcement learning (RL) has shown superior performance in solving sequential decision problems. In recent years, RL is gradually being used to solve unmanned driving collision avoidance decision-making problems in complex scenarios. However, ships encounter many scenarios, and the differences in scenarios will seriously hinder the application of RL in collision avoidance at sea. Moreover, the iterative speed of trial-and-error learning for RL in multi-ship encounter scenarios is slow. To solve this problem, this study develops a novel intelligent collision avoidance algorithm based on approximate representation reinforcement learning (AR-RL) to realize the collision avoidance of maritime autonomous surface ships (MASS) in a continuous state space environment involving interactive learning capability like a crew in navigation situation. The new algorithm uses an approximate representation model to deal with the optimization of collision avoidance strategies in a dynamic target encounter situation. The model is combined with prior knowledge and International Regulations for Preventing Collisions at Sea (COLREGs) for optimal performance. This is followed by a design of an online solution to a value function approximation model based on gradient descent. This approach can solve the problem of large-scale collision avoidance policy learning in static-dynamic obstacles mixed environment. Finally, algorithm tests were constructed though two scenarios (i.e., the coastal static obstacle environment and the static-dynamic obstacles mixed environment) using Tianjin Port as an example and compared with multiple groups of algorithms. The results show that the algorithm can improve the large-scale learning efficiency of continuous state space of dynamic obstacle environment by approximate representation. At the same time, the MASS can efficiently and safely avoid obstacles enroute to reaching its target destination. It therefore makes significant contributions to ensuring safety at sea in a mixed traffic involving both manned and MASS in near future.

Optimal reinforcement learning and probabilistic-risk-based path planning and following of autonomous vehicles with obstacle avoidance

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

Active Braking Strategy Considering VRU Motion States in Curved Road Conditions

Currently, the active braking system is primarily used to avoid collisions between vehicles and vulnerable road users (VRUs) traversing the road at a constant speed in straight road conditions. Research on the collision avoidance strategy has been conducted to enhance the protective effects of the active braking system for VRUs traversing the road in various motion states in curved road conditions. Firstly, the spatial position relationships between VRUs and turn-taking vehicles are established when VRUs traverse the road in various motion states from the outside or the inside of the curved road; a mathematical model is established to identify whether VRUs are in a dangerous condition. Secondly, the safe distance model is established to formulate the vehicle collision avoidance strategy. Thirdly, the active braking controller is designed based on the upper sliding mode control and the lower discrete PID control. Finally, the six collision test scenarios on the curved road are constructed by utilizing the Prescan and Matlab/Simulink software. The results show that the active braking system can avoid the collision between the vehicle and VRUs traversing the curved road in various motion states.

Spherical Wrist Manipulator Local Planner for Redundant Tasks in Collaborative Environments

Standard industrial robotic manipulators use well-established high performing technologies. However, such manipulators do not guarantee a safe Human-Robot Interaction (HRI), limiting their usage in industrial and medical applications. This paper proposes a novel local path planner for spherical wrist manipulators to control the execution of tasks where the manipulator number of joints is redundant. Such redundancy is used to optimize robot motion and dexterity. We present an intuitive parametrization of the end-effector (EE) angular motion, which decouples the rotation of the third joint of the wrist from the rest of the angular motions. Manipulator EE motion is controlled through a decentralized linear system with closed-loop architecture. The local planner integrates a novel collision avoidance strategy based on a potential repulsive vector applied to the EE. Contrary to classic potential field approaches, the collision avoidance algorithm considers the entire manipulator surface, enhancing human safety. The local path planner is simulated in three generic scenarios: (i) following a periodic reference, (ii) a random sequence of step signal references, and (iii) avoiding instantly introduced obstacles. Time and frequency domain analysis demonstrated that the developed planner, aside from better parametrizing redundant tasks, is capable of successfully executing the simulated paths (max error = 0.25°) and avoiding obstacles.

A Data-Driven Approach to Motion Planning and Optimal Control of Medical Nanorobots with Dynamic Window Approach⋆

Targeted drug delivery via nanorobots has become an area of prominent research. However, since such robots are microns in size (and in the future, nanometers in size), implementing control laws to make sure the nanorobot does not deviate from a desired trajectory becomes exceedingly difficult. Building upon the existing nonlinear dynamic models of a nanorobot, this work proposes a data-driven method for optimal path planning and control of nanorobots. Specifically, the collision avoidance strategy, Dynamic Window Approach (DWA) implemented with linear quadratic regulator (LQR) control, is employed to simulate a randomly chosen nanorobot moving through the pulmonary artery against blood flow. The nanorobot is simulated as though it is being guided from the initial site to the target site via an induced magnetic field gradient generated by an MRI. It is shown that when full-state feedback control is implemented for nanorobot control, the LQR controller is able to quickly bring the nanorobot to desired states.