Obstacle Avoidance Control Algorithm Research Articles

Cooperative autonomous obstacle avoidance control of spacecraft cluster via event-triggered reference governors

This paper conducts research on the design of a cooperative obstacle avoidance control mechanism for spacecraft cluster. It proposes an event-triggered reverse command regulator to address the current unidirectional obstacle avoidance mechanism, which only goes from the cluster reference command to the individual execution of spacecraft. The proposed mechanism enhances the survivability of spacecraft cluster in complex environments. By employing the designed event-triggered command regulator, the spacecraft can autonomously decide whether to execute a leader-following task based on whether the tracking error satisfies the performance requirements of the cluster closed-loop system. This allows the spacecraft to dynamically choose between task execution and temporarily “forgetting” the task to focus on collision avoidance, thereby increasing the collaborative flexibility of the spacecraft cluster. Furthermore, based on Lyapunov stability theory, the convergence of the proposed cooperative obstacle avoidance control algorithm is theoretically proven. Ultimately, simulation results demonstrate the real-time effectiveness of the designed cooperative obstacle avoidance solution.

Grouping Formation and Obstacle Avoidance Control of UAV Swarm Based on Synchronous DMPC

This paper focuses on the grouping formation control problem of unmanned aerial vehicle (UAV) swarms in obstacle environments. A grouping formation and obstacle avoidance control algorithm based on synchronous distributed model predictive control (DMPC) is proposed. First, the UAV swarm is divided into several groups horizontally and into a leader layer and a follower layer vertically. Second, tracking is regarded as the objective, and collision avoidance and obstacle avoidance are considered as constraints. By combining the velocity obstacle method with synchronous DMPC and providing corresponding terminal components, a leader layer control law is designed. The control law can enable the UAV swarm to track the target while avoiding collisions and dynamic obstacles. Then, considering the formation maintenance term, based on different priorities, member-level obstacle avoidance and group-level obstacle avoidance strategies are proposed, and the corresponding follower layer control laws are provided. Furthermore, the stability of the UAV swarm system under the control algorithm is demonstrated based on the Lyapunov theory. Finally, the effectiveness of the designed algorithm and its superiority in obstacle avoidance are verified through simulations.

Detection and Control Framework for Unpiloted Ground Support Equipment within the Aircraft Stand.

The rapid advancement in Unpiloted Robotic Vehicle technology has significantly influenced ground support operations at airports, marking a critical shift towards future development. This study presents a novel Unpiloted Ground Support Equipment (GSE) detection and control framework, comprising virtual channel delineation, boundary line detection, object detection, and navigation and docking control, to facilitate automated aircraft docking within the aircraft stand. Firstly, we developed a bespoke virtual channel layout for Unpiloted GSE, aligning with operational regulations and accommodating a wide spectrum of aircraft types. This layout employs turning induction markers to define essential navigation points, thereby streamlining GSE movement. Secondly, we integrated cameras and Lidar sensors to enable rapid and precise pose adjustments during docking. The introduction of a boundary line detection system, along with an optimized, lightweight YOLO algorithm, ensures swift and accurate identification of boundaries, obstacles, and docking sites. Finally, we formulated a unique control algorithm for effective obstacle avoidance and docking in varied apron conditions, guaranteeing meticulous management of vehicle pose and speed. Our experimental findings reveal an 89% detection accuracy for the virtual channel boundary line, a 95% accuracy for guiding markers, and an F1-Score of 0.845 for the YOLO object detection algorithm. The GSE achieved an average docking error of less than 3 cm and an angular deviation under 5 degrees, corroborating the efficacy and advanced nature of our proposed approach in Unpiloted GSE detection and aircraft docking.

Fixed-time event-triggered control for multi-agent systems with input delay.

An in-depth study on the fixed-time event-triggered obstacle avoidance consensus control in heterogeneous USV-AUV systems with input delay and uncertain disturbances are conducted in this paper. When initial state of the system fails to achieve consensus, the desired heterogeneous USV-AUV formation can be achieved by fixed-time consensus control, within a fixed predetermined time, regardless of the initial states. Besides, an event-triggered communication strategy among the agents is introduced in the system, significantly reducing communication energy consumption. By employing the proposed control strategy, the Zeno behavior also can be avoided. Additionally, an obstacle avoidance control algorithm for the heterogeneous USV-AUV system based on improved artificial potential fields (IAPF) is designed, which helps in avoiding both static and dynamic obstacles. Compared to existing research, this algorithm reduces control input jitter, resulting in smoother obstacle avoidance paths. Through extensive simulation experiments and comparisons with other methods, effectiveness and superiority of the proposed algorithm is validated.

Collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle

In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view angle is added to the collaborative obstacle avoidance control algorithm of swarm robots, and a control model of collaborative obstacle avoidance of swarm robots with limited view angle is constructed. Then based on the Simulink platform, the simulation is carried out in the case of static obstacle to verify the correctness and stability of the algorithm.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Obstacle avoidance control algorithm of self-driving electric vehicle based on DRL

Facing the increasingly severe pressure of environmental pollution, road safety and traffic congestion, intelligent vehicle has become the development focus of modern automobile industry, and it is also the main direction and key competitive field of automobile technology development in the future. This trend puts forward higher requirements for vehicle control system. Under the background of the vigorous development of electric vehicle and automatic driving technology, through the overall planning of obstacle avoidance strategy of automatic driving electric vehicle, studying the correct control method and designing obstacle avoidance system under different working conditions, it has theoretical guiding significance and practical application value for the development of obstacle avoidance control system of automatic driving electric vehicle in the future. This chapter first introduces the basic theoretical knowledge of reinforcement learning, then introduces the DL theory and classical neural network, and finally introduces DQN and DDPG algorithms in DRL, so as to interact RL data. Combining DL with reinforcement learning, the feature representation of complex driving scene is established, which is used as input to train intelligent vehicle obstacle avoidance control strategies that can complete various driving tasks, and transplanted to practical applications, so that they have better performance in automatic driving obstacle avoidance control. To sum up, DRL has great research and application value for obstacle avoidance control of self-driving electric vehicles, which can greatly promote the level of intelligent transportation system.

Enhanced multi-agent systems formation and obstacle avoidance (EMAFOA) control algorithm

Most of the multi-agent formation and obstacle avoidance algorithms in the literature are computationally expensive and/or presents an ad hoc method for a specific type of systems. A general inexpensive, in term of computational complexity, multi-agent formation and obstacle avoidance algorithm is modeled and designed in this paper. The method builds on the leader-follower strategy for the simplicity and applicability for a wide range of the engineering systems. A novel artificial potential function (APF) is proposed. The proposed function has unique attributes which differentiate it superior to what is reported in the literature. The proposed algorithm can be applied for first order systems, second order systems, and non-holonomic systems. Also, the proposed method is free of local minima problem and oscillations. In addition, a novel multi-agent system formation control design is proposed in this work. The proposed design allows to embed any formation in the system paradigm and reduces complexity. Moreover, the stability of the proposed method is investigated in terms of Riccati equation and Lyapunov method. Furthermore, to show the effectiveness of the proposed method it applied and simulated for a second order leader and one follower system with specific formation. Then, a five second order agents with leader in a circular formation avoiding simple and complex obstacles are also introduced with different scenarios. Finally, twenty agents with square formation with two obstacles are simulated and investigated.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy (IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy. The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

Obstacle avoidance and model‐free tracking control for home automation using bio‐inspired approach

AbstractThis article proposes a control algorithm for obstacle avoidance and trajectory tracking for a redundant‐manipulator in smart‐homes. The redundancy provides dexterity and flexibility for the applications like picking, dropping, and transporting objects, tracking predefined paths while avoiding obstacles. The obstacle avoidance is one of the critical problems that need to be addressed. Our proposed algorithm, zeroing neural network with beetle antennae search (ZNNBAS), unifies these two problems into a single constrained optimization problem, which includes a penalty function to reward the optimizer on avoiding obstacles while tracking the reference trajectory. The penalty function is based on the principle of maximizing the minimum distance between the joints of the manipulator and the obstacles. Gilbert Johnson Keerthi (GJK) algorithm is used to calculate the distance between the manipulator and the obstacle as it uses the 3D geometry of the object and manipulator. To test the working of ZNNBAS, we simulated the model of the KUKA LBR robotic manipulator. We used two reference trajectories, that is, hypotrochoid and a character “M,” with an arbitrarily shaped obstacle. The simulation results show that ZNNBAS was able to trace the reference path while successfully avoiding the obstacle accurately.

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

This work is concerned with Model Predictive Control (MPC) algorithm for vehicle obstacle avoidance. The second objective of the algorithm is on-line minimization of fuel utilization. At first, the rudimentary nonlinear MPC optimization problem is formulated. Next, the constraints related to the predicted process state variables are formulated as soft ones to guarantee computational safety. Furthermore, in order to obtain a computationally simple procedure, the process dynamics and the fuel utilization model are linearized on-line and used for prediction in MPC. It leads to a quadratic programming MPC task, the necessity of nonlinear optimization performed in real-time is eliminated. In order to stress advantages of the discussed computationally uncomplicated MPC method it is compared with the basic scheme with on-line nonlinear optimization in terms of control quality and computational time. Additionally, effectiveness of the MPC algorithm is discussed in presence of modeling errors and measurement noise. Finally, additional constraints imposed on the rate of change of the manipulated variables are considered.

Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method

In order to study the adaptability of a multi-redundancy and multi-degree-of-freedom snake-like robot to underwater motion, a two-dimensional (2-D) obstacle avoidance control algorithm for a snake-like robot based on immersed boundary-lattice Boltzmann method (IB-LBM) and improved artificial potential field (APF) is proposed in this paper. Firstly, the non-linear flow field model is established under the framework of LBM, and the IB method is introduced to establish a fluid solid coupling of a 2-D soft snake-like robot. Then, the obstacle avoidance of a snake-like robot in a flow field is realized by optimizing the curvature equation of the serpentine curve and eliminating the local minimum in APF method. Finally, the effects by exerted different control parameters on a snake-like robot’s obstacle avoidance capability are analyzed via MATLAB simulation experiment, by which we can find the optimal parameter of the obstacle avoidance and testify the validity of the proposed control algorithm.

2D Underwater Obstacle Avoidance Control Algorithm Based on IB-LBM and APF Method for a Multi-Joint Snake-Like Robot

In order to study the rapidity and stability of multi-joint snake-like robots in underwater obstacle avoidance, a new underwater obstacle avoidance control algorithm for multi-joint snake-like robots based on the combination of IB-LBM (Immersed Boundary-Lattice Boltzmann Method) and APF (Artificial Potential Field) is proposed in this paper. Firstly, the IB-LBM is used to establish the non-linear flow field model and the fluid-structure coupling model of a multi-joint snake-like robot. Secondly, the 2-D Serpentine curve equation of motions of a multi-joint snake-like robot is improved, and APF method is added to each joint of the robot to control its motion direction, so as to achieve effective obstacle avoidance of the robot in the flow field. Then by MATLAB simulation experiment, influences of different control parameters on obstacle avoidance performance of a multi-joint snake-like robot are analyzed, and the best parameters suitable for obstacle avoidance of a robot are found to improve the rapidity and stability of the whole system. Finally, an underwater obstacle avoidance experiment of a multi-joint snake-like robot is carried out to verify the effectiveness of the proposed control algorithm in real environment.

Closed Loop Control Algorithm for Obstacle Avoidance Based on the Transformation of Master and Slave Tasks

Closed Loop Control Algorithm for Obstacle Avoidance Based on the Transformation of Master and Slave Tasks

Collision Free Control of Variable Length Hyper Redundant Robot Manipulator

Hyper-redundant robot (HRR) manipulators are useful at navigating convoluted paths, but conventionally complicated to control. The control of a hyper-redundant manipulator is complex due to its redundancy. In this paper, a simple but effective control algorithm for obstacle avoidance is proposed. The algorithm derives a collision free path around known obstacles so that the end-effector of a variable length hyper redundant robot (VHRR) is able to reach the target location following the path without hitting the obstacles. The algorithm can be grouped into two tasks to drive the end-effector along the collision free trajectories: first, solve the inverse kinematics without disregarding the existence of obstacles in the system; and second, fit the manipulator to the respective prescribe trajectories. This method has the capability to allow VHRR maneuver within its workspace without penetrating to the neighboring obstruction. Further, this method is very effective in the sense that it forms a nice coil profile avoiding zig-zag configuration, and thus eliminates sharp turn on the robot. The performance of a VHRR was tested through simulation to demonstrate the effectiveness of the proposed method. The approach succeeded in delivering the path that avoids obstacle.

Design of Robot Lawn Mower Based on Computer Vision

In this paper, we introduce a new research about mowing robot on obstacle avoidanceand path tracking controlling theoretically and practically, and propose the mowing control methodwhich based on computer vision. Lawn images collected by camera, through the extraction ofprogramming dealing with lawn, obstacles and boundary characteristics to achieve edge separation.Through special processing algorithm to determine boundary arises and obstacles location, size andspeed of the robot, with real-time capturing and processing. This paper gives the complete imagerecognition processing, and analyzes several methods of image filtering and edge detection, andproposes simple control algorithm for obstacle avoidance, and applies MATLAB for obstacleavoidance simulation. The results show that: the method can correctly identify the obstacles andboundaries, through the output about location, size of the obstacles and boundary, border slope andspeed of the robot to control robot mower. Most importantly, that computer vision applying in themowing robot itself is an innovation.

단순구조 퍼지논리시스템을 이용한 이동 로봇의 주행 제어기 설계

이동로봇에 관한 연구가 널리 진행되고 있다. 본 논문에서는 미지의 공간에서 효과적으로 장애물을 회피할 수 있는 SFLC(single-input fuzzy logic controller) 기반의 이동로봇의 주행 제어기 설계와 구현을 제안한다. 장애물의 위치와 거리 인식을 위해 초음파센서를 사용하였으며, 좌, 우측 바퀴의 각속도 출력 제어를 위하여 퍼지논리시스템 기반의 제어기를 설계하였다. 퍼지제어기의 퍼지화 방법은 싱글톤 방법, 추론법은 간략화된 Mamdani의 추론법, 비퍼지화 방법은 간략화된 무게중심법을 사용하였다. 제안한 퍼지제어기의 성능 및 실제 적용 가능성의 평가를 위해 이동로봇의 모델링에 근거한 컴퓨터 시뮬레이션을 수행하였다. 그 결과 이동로봇이 장애물을 피하면서 목표지점에 정확히 도착함을 확인하였다. 더욱이 기존의 2-입력 퍼지논리시스템 기반의 제어기로부터 단일 입력을 가지는 단순구조 퍼지논리제어기를 설계할 수 있음도 보였다. In this paper, we present an obstacle avoidance control algorithm for mobile robots based on SFLC (single-input fuzzy logic controller) with an efficient fuzzy logic look-up table to replace the traditional complicated operation. This method achieves better performance than traditional methods in terms of efficiency. The output of a SFLC leads the robot to the target automatically although many obstacles on the path. Our experiments show that the robot has good performance in the view of path tracking and other efficiency.

A Decentralized Control Algorithm for Swarm Behavior and Obstacle Avoidance in Unknown Environments

In this paper a decentralized control algorithm for swarm behavior and obstacle avoidance in unknown environment is presented. The proposed technique is based on the concept of Null Space Based behavioral approach to merge different swarm tasks. In particular, the algorithm focuses on the possibility of sharing sensors’ data in order to avoid obstacles that can be detected in the environment. The effectiveness of our approach is investigated by means of numerical simulations with different typology of environment.