Collision Avoidance Control Research Articles

Collision avoidance control of an automated guided vehicle based on hierarchical trajectory planning and fixed-time prescribed adaptive sliding mode

In this paper, a hybrid collision avoidance control (CAC) approach including trajectory planner and controller is proposed for an automated guided vehicle (AGV). Firstly, by combing the improved A* and timed-elastic-band method, a hierarchical trajectory planner is developed to realize the flexible collision avoidance trajectory planning of AGV. Subsequently, the trajectory controller is constructed to achieve the accurate tracking of the planned trajectory. Since the controller is developed based on a faster fixed-time stable system and a prescribed performance function, both the fixed-time and prescribed bounded convergences of the trajectory tracking system can be realized. In addition, a newly designed sliding mode filter-based nested adaptive law is adopted in the controller to update the control gain, thereby releasing the prior bound information of AGV unknown disturbance and minimizing control chattering. The global stability and prescribed convergence of the system are proved by Lyapunov theory. Finally, the effectiveness and merits of the presented CAC approach in flexible trajectory planning and accurate tracking are demonstrated by experimental validations on an AGV.

Fuzzy Inference-Based Adaptive Sonar Control for Collision Avoidance in Autonomous Underwater Vehicles

Abstract This article discusses the use of adaptive control in the sonar scanning sector within an obstacle detection system, to improve the effectiveness of collision avoidance for autonomous underwater vehicles (AUVs). An adaptive network-based fuzzy inference system (ANFIS) was used for dynamic calculations of the sonar scanning sector. Based on 100 simulation scenarios containing various trajectories created by the mission planner, with various shapes, dimensions and arrangements of static obstacles, and various arrangements and displacement vectors of dynamic obstacles, the effectiveness of the proposed system was tested in comparison with other classical approaches such as a single echosounder and sonar with a fixed scanning sector width. The above sensor configurations were evaluated in terms of the percentage of collision-free trials, the average percentage of trajectory completion, and the average number of activations of the collision avoidance system. Simulations conducted based on the mathematical model of the AUV confirmed that the proposed approach increased the effectiveness of collision avoidance systems for AUVs compared to classical echosounder and sonar-based systems.

Obstacle-aware path following of omni-wheeled robots using fuzzy inference approach

This study proposes a real-time LiDAR-based approach for efficient path following in omni-wheeled robots (OWRs) capable of dynamically adapting to surrounding obstacles. To achieve this, two fuzzy control schemes are developed: Fuzzy Sliding-Mode Tracking Control (FTC) for precise path following and Fuzzy Obstacle-avoidance Control (FOC) for real-time collision avoidance using LiDAR data. FTC utilizes fuzzified sliding surfaces, derived from tracking errors, to follow a planned path. In the presence of obstacles along the path, FOC, employing LiDAR measurements, assesses collision distance and direction, ensuring safe navigation. The outputs of FTC and FOC are seamlessly merged using a smooth switching factor to generate voltage inputs for the actuators. The effectiveness of the proposed approach is demonstrated through extensive simulations and real-world experiments under various conditions, including wheel disturbances, diverse trajectories, and varying initial positions.

Study of hydrogen fuel cell unmanned surface vehicle crane collision avoidance control by using an improved artificial potential field

ABSTRACT In this paper, a collision avoidance control strategy based on an enhanced Artificial Potential Field (APF) is proposed to overcome stationary and dynamic surface obstacles for Unmanned Surface Vehicle Crane (USVC) powered by hydrogen fuel cells. The proposed collision avoidance control relies on the gravitational and repulsive potential field functions. An angle limitation and speed optimisation design strategies are derived to enhance the path planning performance and guarantee the safety of the system. Finally, the simulation and experiment of USVC platform equipped with hydrogen fuel cells are developed to confirmed the effectiveness and reliability of the collision avoidance control strategy based on a novel APF under several obstacles.

Leader-Following Connectivity Preservation and Collision Avoidance Control for Multiple Spacecraft with Bounded Actuation

This paper investigates the distributed formation control of a group of leader-following spacecraft with bounded actuation and limited communication ranges. In particular, connectivity-preserving and collision-avoidance controllers are proposed for the leader with constant or time-varying velocity, respectively. The communication graph between the spacecraft is modeled via a distance-induced proximity graph. By designing a virtual proxy for each spacecraft, the spacecraft–proxy couplings address the actuator saturation constraints. The inter-proxy dynamics incorporated with a bounded artificial potential function fulfill the coordination of all proxies. In addition, the bounded potential function can simultaneously tackle connectivity preservation and collision avoidance problems. The distributed formation controllers are proposed for multiple spacecraft with constant or time-varying velocities relative to the leader. A sliding mode control approach and the proxies’ dynamics are used in the design of a distributed cooperative controller for spacecraft to address the cooperative problem between the followers and the leader. Numerical simulations confirm the effectiveness of the anti-saturation distributed connectivity preservation controller.

Recent Developments and Future Prospects in Collision Avoidance Control for Unmanned Aerial Vehicles (UAVS): A Review

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories.

Reactive Collision Avoidance Control for Nonholonomic Vehicles and Obstacles of Arbitrary Shape

Abstract Potential field-based collision avoidance algorithms for mobile robots frequently assume vehicles and obstacles to have circular or spherical shapes. This assumption not only simplifies the analysis but also limits the mobility of agents in confined spaces, particularly for vehicles with elongated or irregular shapes. To increase mobility, this letter presents a decentralized collision avoidance framework for nonholonomic systems of unicycle type that considers the non-circular shape and relative orientation of vehicles and obstacles. The framework builds on the concepts of potential field and avoidance functions. However, it proposes using a non-constant minimum safe distance radius that changes based on the shape, relative position, and relative orientation of agents. The control framework is proven to guarantee collision avoidance at all times and is shown, via simulation, to increase the ability of agents to navigate through narrow spaces safely.

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.

Design of Automotive Collision Avoidance Lower Position Controller Based on GA Optimization

Abstract To improve the control stability of the lower controller in the vehicle collision avoidance system, and solve the problem of traditional fuzzy PID not providing good dynamic performance under large parameter changes, a lower controller for vehicle collision avoidance based on genetic algorithm optimized fuzzy proportional-integral-derivative (PID) control was studied. The vehicle collision avoidance system collects real-time motion information of the preceding vehicle through millimeter-wave radar and calculates the safety distance. The safety distance model and upper controller of the system calculate the required acceleration of the vehicle. Afterwards, the expected acceleration of the vehicle is controlled by the lower controller of fuzzy PID. At the same time, the improved GA is used to adjust the PID parameters, solving the problem of poor robustness of the lower controller. A simulation platform was built to simulate the longitudinal vehicle collision avoidance control system jointly. The simulation results show that the lower controller designed based on this control algorithm has better stability and meets the collision avoidance requirements, providing an important basis for the development of vehicle control systems.

Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety

The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance.

Combined Steering and Braking Collision Avoidance Control Method Based on Model Predictive Control

Combined Steering and Braking Collision Avoidance Control Method Based on Model Predictive Control

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

Collision avoidance control for limited perception unmanned surface vehicle swarm based on proximal policy optimization

In order to ensure the safe and coordinated operation of unmanned surface vehicle (USV) swarm in complex marine environments, the primary problem is collision avoidance control (CAC). However, the limited perception, environmental uncertainty and multi-source complexity bring significant challenges to the efficient collaboration and CAC of the USV swarm. To overcome the above challenges, this paper aims to propose a distributed CAC method for USVs based on the proximal policy optimization (PPO). This method does not necessitate precise system models and is capable of autonomous learning, effectively adapting to unknown environments. In terms of CAC, unlike designing reward functions based solely on the distance from obstacles, we additionally consider the velocity of obstacles, and combine optimal reciprocal collision avoidance (ORCA) to design a reward function. We further consider the limited perception range of USVs and construct a bidirectional gated recurrent unit (BiGRU) network to extract features of variable length observation data, effectively overcoming the problem of dimensionality in observable data. Moreover, we construct a high-quality USV swarm simulation environment using the Gazebo 3D physics engine, which is used for testing the generalization capability of collision avoidance policy. Finally, to verify the effectiveness of the policy learning and optimization, a series of experiments are conducted in various scenarios, network architectures, and control methods. The experimental results indicate that our approach has remarkable superiority in terms of travel time, average velocity, average reward, and success rate.

Design of Automated Bascule Bridge and Collision Avoidance with Water Traffic

Abstract The development of movable bridges is a modern technological advancement that requires the integration of multi-disciplinary concepts such as control, automation, and design. In order to demonstrate how a collision avoidance system works, this research offers the design of an automated double-leaf bascule bridge with a pulley-rope moving mechanism and Pratt truss. The bridge is created where a settled railway or roadway intersection of a navigable stream cannot attain a steep profile. The design took into account the length, height, and width of the bridge as well as automatic ship identification and scaffold leaf movement activity for ship crossing. For the safe passage of cars on the bridge and for stopping vehicles during bridge movement, the system includes a collision avoidance control system and an automated road barrier system. After sensor adjustment, the model bridge’s performance under test produced results that were adequate, with a success rate of 100%.

Integrated Four-Wheel Steering and Direct Yaw-Moment Control for Autonomous Collision Avoidance on Curved Road

<div>An automatic collision avoidance control method integrating optimal four-wheel steering (4WS) and direct yaw-moment control (DYC) for autonomous vehicles on curved road is proposed in this study. Optimal four-wheel steering is used to track a predetermined trajectory, and DYC is adopted for vehicle stability. Two single lane change collision avoidance scenarios, i.e., a stationary obstacle in front and a moving obstacle at a lower speed in the same lane, are constructed to verify the proposed control method. The main contributions of this article include (1) a quintic polynomial lane change trajectory for collision avoidance on curved road is proposed and (2) four different kinds of control method for autonomous collision avoidance, namely 2WS, 2WS+DYC, 4WS, and 4WS+DYC, are compared. In the design of DYC controller, two different feedback control methods are adopted for comparison, i.e., sideslip angle feedback and yaw rate feedback. The simulation results demonstrate significant improvements in the path tracking performance and stability of the 4WS+DYC control system compared to other control systems. Furthermore, the performance of the DYC control system with yaw rate feedback outperforms that of the DYC control system with sideslip angle feedback.</div>

Steering collision avoidance and lateral stability coordinated control based on vehicle lateral stability region

The balance between vehicle lateral stabilization and collision avoidance is critical for steering collision avoidance in emergency situations. On the one hand, emergency steering may cause a vehicle to lose its lateral stability. On the other hand, the overly conservative stability controller may compress the safety margin of vehicle collision avoidance, leading to failure of collision avoidance. Therefore, steering collision avoidance and lateral stability coordinated control (SCALSC) based on the vehicle stability region is proposed. The Lyapunov’s Second Method is used to obtain the lateral stability region instead of the linear two-degree-of-freedom (2-DOF) vehicle states as the stability tracking target to ensure that the vehicle states are in the stability region. The SCALSC includes an active steering controller and a direct-yaw-moment controller (DYC). An active steering controller is used for collision avoidance in emergency conditions, while DYC is used for stability control. An intervention criterion for the DYC system is proposed by using the Hurwitz criterion. Finally, a simulation test was carried out based on MATLAB/Simulink. The simulation results show that the proposed coordinated control method ensures stability, improves the safety margin of collision avoidance, and realizes multiobjective coordinated control of collision avoidance and autonomous vehicle stability control in emergency situations.

A Bio-inspired Model for Bee Simulations.

As eusocial creatures, bees display unique macro collective behavior and local body dynamics that hold potential applications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual cues for foraging and predator avoidance, exhibiting distinctive local body oscillations, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges for realistic bee simulations in practical animation applications. In this paper, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee's local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee's local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees' dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees' innate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms.

Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance

Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance

Research on Active Collision Avoidance Control Strategy for Intelligent Vehicle

Research on Active Collision Avoidance Control Strategy for Intelligent Vehicle

Adaptive collision-free control for UAVs with discrete-time system based on reinforcement learning

A flexible reinforcement learning (RL) optimal collision-avoidance control formulation for unmanned aerial vehicles (UAVs) with discrete-time frameworks is revealed in this work. By utilizing the neural network (NN) estimating capacity and the actor-critic control scheme of the RL technique, an adaptive RL optimal collision-free controller with a minimal learning parameter (MLP) is formulated, which is based on a novel strategic utility function. The optimal collision-avoidance control issue, which couldn’t be addressed in the prior literature, can be resolved by the suggested approaches. Furthermore, the proposed MPL adaptive optimal control formulation allows for a reduction in the quantity of adaptive laws, leading to reduced computational complexity. Additionally, a rigorous stability analysis is provided, demonstrating that the uniform ultimate boundedness (UUB) of all signals in the closed-loop system is ensured by the proposed adaptive RL. Finally, the simulation outcomes illustrate the effectiveness of the proposed optimal RL control approaches.