Obstacle Avoidance Function Research Articles

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.

A compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots

This paper presents a compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots working safely in high-radiation environments. Firstly, configurations and kinematics of the intelligent demolition robot are detailed to detect the possible obstacles in its workspace. A collision detection function based on the improved dual vector method is proposed to detect the different distances between the robot and obstacles in three cases: a point and a line segment, two line segments, and a line segment and a geometric shape. This function can also be applied to detect collisions with various obstacles of different shapes reasonably and efficiently. Furthermore, an obstacle avoidance function based on modified gradient projection method considering multi-task transformation is proposed. According to the different distances between the robot and the obstacle, it can be used in three situations: end obstacle avoidance task, end effector operation task, and end trajectory tracking task. This function can be applied to avoid obstacles both in the workspace and on the desired path. Finally, a simulation system is established to verify the collision detection and obstacle avoidance algorithms of the intelligent demolition robot. An experiment was conducted on the intelligent demolition robot. This robot can successfully achieve the expected trajectory with the method described in this article. Results of simulation and experiment demonstrate that obstacles both in workspace and on desired path can be detected and avoided properly.

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

ARUDINO BASED OBSTACLE AVOIDING ROBOT CAR P VAYUNANDA SAI KUMAR

This project involves the design and implementation of an intelligent obstacle-avoiding robot car. The objective of this project is to implement a robot car, which while moving should have the ability to detect obstacles in its path and change direction where obstacles are present without any form of external influence. The new direction to be taken to avoid collision is the direction that has the most distance between the obstacle and the sensor and this is determined by the robot based on sensor inputs. This implementation was done using an ultrasonic wave sensor, which measures distance by sending pulses. Also, the movement of the servo motor (for sensor movement) and the DC motors (for wheel movement) are controlled by the motor driver shield in order to enable the obstacle avoidance function. The commands are sent to the Arduino microcontroller chip which serves as the main control of the robot car, as it controls the sensor and car movement. The implemented robot car was able to successfully detect and avoid obstacles within the line of sight of the Ultrasonic sensor used. Keywords: Arduino UNO, ultrasonic sensor, DC motor, Servo Motor

Novel Extension Control Instrument for Power Wheelchair Based on Kalman Filter Head Motion Detection

People with upper limb disabilities or high quadriplegia have extremely high requirements for the maneuverability and functionality of power wheelchairs. Normal wheelchairs cannot meet travel tasks, while smart customized wheelchairs are expensive and cannot be popularized. Therefore, a novel extension control instrument for power wheelchairs with low cost, strong scalability, and convenient usage is proposed, which can realize the control of the wheelchair by sensing a change of head posture. The device is divided into a head motion sensing unit (HMSU) and a wheelchair assistance control unit (WACU). The mapping relationship between the head attitude and the subject’s motion intention is established. The inertial measurement module in the HMSU collects the head attitude data and uses the Kalman filtering method to obtain the accurate Euler angle. The WACU is fixed on the original controller of the wheelchair. The joystick is inserted into the extended control mechanism and controlled, instead of the hand, through a 2-degree-of-freedom servo system combined with the pinion and rack push rod structure, thus controlling the movement of the wheelchair. In proceeding, the system can also detect the distance of objects in the environment in real time through the three-direction (front, left, right) ultrasonic ranging sensors installed on the WACU, with a certain obstacle avoidance function. The prototype experiments prove that the extension control instrument developed in this paper based on the Kalman filter can quickly and accurately identify head motion and accurately control the movement of the wheelchair. It is easy to operate and has strong universality, which presents a new low-cost solution for the travel of patients with disabilities.

Experimental Evaluation of Manipulator Teleoperation System Based on Trajectory Planning for Obstacle Removal Task in Nuclear Plant Decommissioning

In the teleoperation system of a dual-arm manipulator developed to retrieve fuel debris and reactor interior structures at the Fukushima Daiichi Nuclear Power Plant, we used software in which an obstacle avoidance function (trajectory planning) was implemented to conduct a mock test simulating obstacle removal operations in narrow spaces. The test results confirmed the validity of the obstacle avoidance function, the executability of a series of necessary tasks, and the improved operability. In addition, issues were identified using the test data.

双向犁耕防过载自动避障机构的设计与试验

During the tillage of two-way ploughing equipment in rocky and barren soil, due to hard objects such as stones, the plough and the key working parts such as plough column are deformed and broken, thereby reducing the reliability and service life of machines and tools. As such, an anti-overload automatic obstacle avoidance mechanism for two-way ploughing is developed. The maximum obstacle avoidance height is 40 cm, and the obstacle avoidance angle α is designed. The rotation range is 4.6 ~51.5°, and the effective compression stroke of the spring after pre-tightening is 39.34 mm. The motion equation and quasi-static force equation of the mechanism are established. The plowing resistance in the equilibrium state is 9.74 KN, and the required spring preload is 9.75 KN. Under the safety factor of 1.3, different spring elastic coefficients change with the rotation angle of the mechanism. The virtual prototype simulation model of the anti-overload automatic obstacle avoidance mechanism is established. The simulation results show that the mechanism can effectively perform the obstacle avoidance action. Bench test verifies that the mechanism can avoid obstacles according to the predetermined load. The field test shows that the stability coefficient of the ploughing depth of the mechanism is less than 8%. The results can effectively realize the obstacle avoidance function and ensure the farming quality, and provide an efficient and reliable anti-overload obstacle avoidance structure and parameter basis for the rocky land.

EFFICIENT UAV FLIGHT PLANNING FOR LOD2 CITY MODEL IMPROVEMENT

Abstract. Geometric errors in LoD2 building models can be caused by the modeling algorithm but are often related to the quality of input data. One approach to tackling the modeling errors caused by the quality of input data is to collect additional data with a UAV and remodel the buildings. However, no flight planning approach exists specifically designed for efficient data recollection for model improvement. In this paper, we propose an innovative flight planning approach for this purpose. Contrary to the conventional method that recollects the data covering the entire building roof, our approach only collects the data over the erroneous region and uses it to improve the erroneous model part later. Our algorithm utilizes the existing LiDAR survey data to automatically detect model errors and design the camera networks by considering the roof geometry. We optimize the trajectory that connects the viewpoints with a genetic algorithm and develops an obstacle avoidance function with ray-casting to ensure a collision-free path. The proposed flight plan is implemented in a real-world scene. Our result shows an improved point cloud created through dense image matching with the collected UAV image data. The generated point cloud is successfully used for creating partial building models for improving the original models.

Intelligent Vehicle Systematic Design Based on Arduino and Raspberry Pi

The intelligent vehicle designed in this paper can realize functions, such as safety detection, visual identification, remote control and manipulator grasping, and so on. Arduino MEGA is used as the main control board to send signal messages to drive vehicles. Wi-Fi module is used to receive messages to remote control vehicles. The ultrasonic and infrared module is used to realize object detection around vehicles. To realize complex route movement, raspberry pie is used for visual recognition and path planning. Data is sent to Arduino for judgment in real time. Finally, it is verified that the design effectively improves the path-planning ability and obstacle-avoidance function in a sample vehicle.

Review on Advanced Obstacle Avoidance Techniques for Arduino-based Intelligent Vehicles

Abstract: Robotics is a rapidly expanding and fascinating field in the modern world. Because of its high level of intelligence, a smart car can utilize all of the available space. This thesis presents the design and execution of a self-driving, obstacle-avoiding Smart Car that uses an ultrasonic wave sensor. The goal of the project is to create a Smart Car that can move around obstacles by employing ultrasonic sensors. To carry out the needed procedure, an ATmega328 microcontroller is employed. A machine that can work automatically or with instruction is referred to as a smart car. The proposal suggests a smart car that can selfsteer when an impediment is in its way since it has intelligence built into it. A microcontroller from the AT mega 328 series was used to construct this Smart Car vehicle. Any obstruction in front of it is detected by an ultrasonic sensor, which then sends an instruction to the microcontroller. The microcontroller instructs the Intelligent Car to travel in a different direction based on the signal that it receives by activating the motors that are connected to it by a motor driver. The roadblock avoidance distance may be calculated by emitting pulses. We can simultaneously manage the steering to carry out the function of obstacle avoidance. The Smart Car has rear wheel drive and front axle steering. Two electric motors using gear reduction systems power two driving tires. Using an Arduino MCU microchip as the brains of the Smart Car's control system. We construct our Smart Car platform while achieving positive experimental results through the design for both the software and the hardware systems.

EMPMR berthing scheme: A novel event-triggered motion planning and motion replanning scheme for unmanned surface vessels

In this study, the issues related to approaching and berthing of unmanned surface vessels (USVs) are investigated. The automatic berthing task is divided into motion planning and motion replanning. First, a smooth path from the initial position to the berth is planned using A* algorithm and B-spline. Then, the segmented interpolation method is employed to let the USV velocities converge slowly to zero at the berth, and an initial berthing trajectory is obtained. Moreover, considering the kinetic constraints of the USV, the nonlinear model predictive control (NMPC) algorithm is employed in the offline motion planning and online motion replanning scheme. In this scheme, novel static, and COLREGs-based dynamic obstacle avoidance functions are proposed based on Gaussian functions. Furthermore, the event-triggered mechanism is adopted to improve the real-time performance of the NMPC algorithm. The proposed event-triggered motion planning and motion replanning (EMPMR) scheme and Han et al.’s scheme are tested in six scenarios. Simulation results show that the proposed EMPMR scheme can accomplish USV’s approaching and automatic berthing of USVs and has better obstacle avoidance and computational performance.

The underwater obstacle avoidance method based on ROS

To achieve real-time obstacle avoidance for underwater robots, the artificial potential field method can be selected as the obstacle avoidance path planning algorithm. However, this method applies a large attractive force to the underwater robot when it is far from the target point, causing it to collide with obstacles. Additionally, when the underwater robot just enters the repulsion influence zone, the repulsion force that it experiences undergoes an abrupt change, resulting in a non-smooth obstacle avoidance path. Furthermore, developing the obstacle avoidance function based on software platforms such as MATLAB has a long algorithm verification cycle, and it is difficult to transfer the algorithm model to practical scenarios. Therefore, this paper proposes an underwater robot obstacle avoidance method based on ROS. Firstly, a dynamic adjustment factor is introduced into the potential field function of the artificial potential field method to redefine the obstacle influence zone, and an improved artificial potential field method is designed as the obstacle avoidance path planning algorithm for underwater robots. The experiment is conducted on the ROS simulation platform, and the results show that the improved algorithm can solve the two problems existing in the artificial potential field method. Secondly, nodes required for underwater robot obstacle avoidance are established based on the ROS operating system, and the improved artificial potential field method is integrated into the obstacle avoidance path planning node. Finally, the obstacle avoidance experiment of the Bluerov2 underwater robot is conducted in a pool, and the experimental results demonstrate that the proposed method can achieve real-time obstacle avoidance for underwater robots.

Autonomous aerial obstacle avoidance using LiDAR sensor fusion.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

ROS-based Library Book Precise Positioning Guidance Robot

In order to solve the problem of book search and positioning in the library, this paper introduces a library book precise positioning guidance robot based on robot operating system (ROS). The robot has functions such as synchronous positioning and mapping, autonomous navigation and obstacle avoidance. First of all, the library book precision positioning guidance robot uses sensors such as lidar and cameras to realize the perception of the library environment, and at the same time uses image recognition technology to identify books. Secondly, the two-dimensional raster map of the library is drawn by using the SLAM algorithm, and the global and local optimal path planning and obstacle avoidance function are realized by using the Move-base function package, so as to realize the navigation and positioning tasks in the library.

Research on Autonomous Navigation of Showroom Service Robot Based on ROS and Laser SLAM

As an important tool for guiding and introducing visitors to the exhibition hall, the robot needs to confront the complex environment that may exist and the constantly moving visitors. First of all, it is needed to have the ability to enable accurate positioning and navigation as well as dynamic obstacle avoidance. In recent years, simultaneous localization and mapping (SLAM) technology has been widely used to address localization and navigation issues in unknown indoor environments. Based on the robot operating system (ROS), this study collects environmental information by 2D LiDAR, and then combines the service robot's Inertial measurement unit (IMU), odometer, and other sensors to map the exhibition hall environment by the Cartographer SLAM algorithm. Path planning and dynamic obstacle avoidance functions are also implemented, in path planning, the algorithm is selected for global path planning and the Dynamic Window Approach (DWA) algorithm for local path planning. The environment map construction, path planning and dynamic obstacle avoidance functions are verified by simulation and in real environment. The average position deviation and standard deviation (SD) of the robot from the target point were less than 7 cm and less than 3 cm, respectively, when the robot was set to move at 0.5 m/s for the positioning test. The average heading deviation was less than 9° and SD was less than 3°. The robot's positioning and navigation can well meet the working requirements of the pavilion service robot.

Design of Intelligent Tracking Car Based on Arduino Mega 2560

In certain special circumstances, human beings cannot enter to carry out expeditions or rescue missions. As a result, a smart tracked adventure vehicle equipped with multiple sensors has been created. In this study, an Arduino Mega 2560 is used as the main control board to design an intelligent trajectory adventure cart with automatic trajectory, metal detection, sound and light alarm, and obstacle avoidance functions. An intelligent cart system based on 2560 as the core, sensor information detection and fusion, audible alarm and LED display, and PID control [1] is built. The design has a simple structure, a full set of functions, and a certain degree of intelligence.

Research on SLAM Path Planning of ROS Robot based on Lidar

In order to improve the autonomous navigation performance of robot, an automatic mapping and path planning method based on ROS robot is proposed, which uses two-dimensional laser radar to collect the surrounding environment. Gmapping algorithm is used for automatic optimization and mapping, Class A algorithm is used for global path planning, and DWA method is used to realize the local obstacle avoidance function of the robot. Experiments show that this method can realize the automatic generation and automatic navigation of the robot.

Obstacle avoidance method based on double DQN for agricultural robots

In a complex farm environment, the lack of an intelligent obstacle avoidance function is a major barrier to the large-scale adoption of automatic navigation technology for agricultural vehicles. This paper introduces a high-performance obstacle avoidance control method based on reinforcement learning. The process of obstacle avoidance is modeled to define the state and action spaces in the Double Deep Q-Network (DQN) architecture, and a reward function is designed to evaluate and guide the model training. A neural network model is constructed and embedded in the Double DQN architecture to decide the output action according to the input state. To train models efficiently, three encounter situations (Confronted, Cross, and Overtaking) are established in a Multi-Joint dynamics with Contact (MuJoCo) simulation environment in which validation tests verify the stability and performance of the proposed obstacle avoidance controller. In field experiments, the averages of the shortest distance, trajectory length, and time of obstacle avoidance are 2.37 m, 0.53 m, and 2.7 s, respectively, which indicate the availability of the proposed controller. The proposed Double DQN-based controller shows a significant advantage over the traditional Risk Index-based control method in terms of both space utilization and time efficiency, and its performance facilitates automatic navigation in complex farmland.

Research on Obstacle Avoidance and Positioning Technology of UAVs in Substation

UAV obstacle avoidance technology is directly related to work safety in substations, and is an important topic in the current domestic power system. Therefore, the obstacle avoidance and positioning technology of UAVs in substations are deeply studied. Through camera imaging, matching image features are extracted. And camera imaging can be combined with ultrasonic obstacle avoidance to realize the overall obstacle avoidance technology of UAVs. The coordinates are established through parameter optimization, and the substation UAV positioning technology is realized after the positioning accuracy is clarified. This solution can effectively solve the contradiction between the workload of the substation system and human and material resources, detect risks in time, ensure safety, provide a complete and reliable guarantee for the unattended mode in the substation, and achieve the autonomy of UAVs in the substation and positioning and obstacle avoidance functions. CLC number: TM744 Document identification code: A.

Control system for automatic search and transportation of an object by a mobile robot with obstacle avoidance function

Control system for automatic search and transportation of an object by a mobile robot with obstacle avoidance function