Robot Obstacle Avoidance Research Articles

Path planning algorithm based on Improved Artificial Potential Field method

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

Reinforcement Learning-Based Approach to Robot Path Tracking in Nonlinear Dynamic Environments

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system’s environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

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.

Field Oriented Control of Brushless Direct Current Motor for Electric Vehicle Robot Obstacle Detection and Avoidance

This paper presents an approach to implement Field Oriented Control (FOC) brushless directcurrent motor using PID Controller for electric vehicle obstacle detection and avoidance. A state spacemodel of the BLDC motor with FOC was model and simulated using MATLAB/Simulink to validate thestability of the proposed system. The overall system was validated in real time implementation using microcontroller and Ultrasonic Sensor. The detection and recognition of objects, as well as making anautonomous decision to change its trajectory which is based on predetermined threshold distance of 5cmwere analyzed.

Rancang Bangun Robot Penghindar Halangan Dengan Metode PID

The development of increasingly advanced science and technology has produced many innovations in meeting the needs of mankind. One area that is developing comprehensively is robotic technology, one of which is obstacle avoidance robots which is an example of a device used to improve the safety and effectiveness of human work. This study aims to design and develop obstacle avoidance robots using the PID (Proportional, Integral, and Derivative) method and ultrasonic sensors. Robots are designed to automatically detect and avoid obstacles in their environment by using ultrasonic sensors to measure the distance between the robot and existing objects. The PID method is used to control the movement of the robot based on information received from the ultrasonic sensor with Arduino Uno as the main brain controlling the DC motor. Ultrasonic sensors are used to measure the distance between the robot and obstacles in front of it. The distance data obtained from the ultrasonic sensor is then processed using the PID method to produce a control signal that regulates the speed and direction of the robot's movement. The robot is equipped with an obstacle avoidance mechanism that allows it to change its direction of movement automatically when it detects an obstacle in front of it by testing it in various obstacle avoidance scenarios. The results of the tests that have been carried out show that the robot is able to detect different obstacles with high accuracy and avoid them successfully. The use of the PID method in controlling the robot's movement has proven to be effective in maintaining the stability and accuracy of the robot's movement. This research contributes to the development of robotics and automation technology by presenting an obstacle avoidance robot that can move automatically and avoid obstacles effectively.

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.

Improved Deep Deterministic Policy Gradient for Dynamic Obstacle Avoidance of Mobile Robot

When a mobile robot is required to perform tasks in the unknown and complex environment, it is critical to have the ability of dynamic obstacle avoidance. However, conventional deep deterministic policy gradient (DDPG) for collision-free navigation can only perceive a fixed number of dynamic obstacles, and thus it cannot adapt to the stochastic working scenario. To overcome the limitation, an improved DDPG algorithm is proposed in this study. It is an exploration to implement the DDPG with long short-term memory (LSTM) network-based encoder to achieve dynamic obstacle avoidance for the mobile robot in the stochastic working scenario, which can encode the variable number of obstacles into a fixed-length representation. Specifically, to facilitate the LSTM network-based encoder, one safe processing rule is designed to guarantee the entire information of the observable obstacles can be represented completely. The LSTM network-based encoder takes the latest environment information of observable obstacles by employing the safe processing rule and generates the fixed length state vector. In addition, continuous state space for mobile robot and obstacles, as well as reward function and action space are designed. Both simulations and experiments are conducted and the results verify that the improved DDPG algorithm can achieve collision-free trajectory with multiple dynamic obstacles well. It helps to reduce the path distance and motion time effectively.

Voice Controlled Robot

Abstract- The field of robotics has advanced significantly, and we are now seeing the rise of sophisticated, intelligent machines that have the potential to completely change the way we live. The Voice-Controlled and Obstacle Detection Robot, a cutting-edge invention that has captivated the world, is one such marvel.This highly developed robot is outfitted with a variety of features that set it apart from the competition. With the voice-controlled interface, users may engage with the robot in a simple and natural way, making technology interaction simpler than before. This robot can hear and carry out a variety of verbal orders thanks to its powerful speech recognition technology, which lets it. Also, this robot's Obstacle Detection system is a feat of engineering that makes it possible for it to easily navigate through challenging environments. This robot is the best option for hazardous areas since it can identify impediments in its route and make the required changes to avoid collisions. The Voice-Controlled and Obstacle Avoidance Robot is a game-changer in the field of robotics. It can explore uncharted territory, carry out difficult activities, or help people go about their daily lives. This robot is certain to have a significant impact on the technology industry and beyond because to its cutting-edge capabilities, user-friendly interface, and unparalleled adaptability.

Non-contact automatic COVID-19 nucleic acid detection robot

This product is mainly used to realise the contactless automatic COVID-19 nucleic acid detection robot, which can navigate to the workstation or location of the set personnel through the camera and radar and is mainly applied to the autonomous movement function of the existing automatic nucleic acid detection robot. The robot will conduct intelligent navigation according to the position of the nucleic acid person, avoid obstacles autonomously through radar and camera, and use clever navigation to plan the nearest route to the position part of the needed nucleic acid. This product carries a camera and laser radar based on SLAM technology for obstacle avoidance of autonomous mobile robots, through the experiment of advanced algorithms and the team constantly optimised. The algorithm makes its universality, compatibility, real-time and precision to achieve the desired effect. These autonomous mobile robots in the industrial production industry are essential to the application.

Path planning of robot in epidemic situation

With the development of information technology in the world, human life is becoming more and more intelligent, which leads to the robot path planning algorithm becoming more and more important. Combined with the environment of new coronal pneumonia in China, this paper first introduces the environment of the cabin mobile hospital, and then deduces the role that the automatic obstacle avoidance robot with path planning ability can play in the mobile cabin hospital, and introduces the construction of the whole system model in the virtual simulation platform. This paper discusses the improvement and application of breadth first search algorithm, including the explanation of algorithm reference formula, and the experiment and subsequent analysis on the webots platform, and successfully proves its feasibility. Finally, the existing problems are explained and the parts that can be improved are discussed. Then, the research on robot path planning under the environment of new coronal pneumonia is summarized and prospected.

Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks

This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously.

Ardubot - The Maze Runner

The Avoidance of Obstacles Robot using IoT is a robotic system that is guided by the software we develop and makes use of IoT technologies to avoid obstacles in its route. The robot utilizes sensors to identify obstructions, which it then transmits to a microcontroller, which analyses the information and commands the robot's motors to change course. This kind of robot can be beneficial for a number of tasks, including exploration, search and rescue, and surveillance. The main challenge in building mobile robots is the ability to identify and avoid obstacles. The robots now have the senses necessary to navigate unknown environments safely owing to this technology. This project involves designing an obstacle-avoiding robot that can navigate around obstacles without colliding with them. It is a robot that uses three ultrasonic distance sensors to find obstacles and runs on an Arduino microcontroller. The microcontroller platform chosen was the Arduino board, and the programming was done using Arduino Software, the board's software counterpart. Higher accuracy in spotting nearby impediments is provided by the incorporation of three ultrasonic distance sensors. Being a fully autonomous robot, it navigated new settings without colliding with anything. The project's hardware is widely accessible and reasonably priced, making it simple to replicate the robot

Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model

Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model

Simulation and experiment on obstacle avoidance control of concrete pump truck boom based on improved danger field and gradient projection method

Aiming at the shortcomings of the traditional robot obstacle avoidance algorithm applied directly to the leader–follower task transformation of concrete pump truck boom, an algorithm combined with improved danger field and improved gradient projection method for obstacle avoidance control of the boom is proposed. In the method, a joint limit avoidance function is used to avoid the angle overrun of the boom joints. And the danger field expression is modified to improve its engineering suitability. Moreover, a smoothing adjustment factor is introduced to improve the smoothness of the leader–follower task transformation of the boom. The influence of key control parameters on the safety and accuracy of boom movement is discussed through the simulation, and the reasonable ranges of the parameters are given. Furthermore, experiments are carried out to verify the effectiveness of the algorithm. The method provides a solution for obstacle avoidance control of construction machinery with multijoint series boom.

Potential field-based dual heuristic programming for path-following and obstacle avoidance of wheeled mobile robots

AbstractPath-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.

Obstacle avoidance of a wheeled robotic swarm using virtual spring-damper mesh

Swarm robotics, in which groups of robots coordinate to achieve a common goal, has the potential to revolutionize a wide range of industries. However, one of the key challenges in developing effective swarm systems is enabling the robots to navigate around obstacles while maintaining cohesion. In this article, the problem of trajectory tracking for a self-organized swarm of wheeled robots in the shape of a regular polygon operating in an environment with obstacles is addressed. The goal is for the swarm to follow a reference point by the geometric center of the swarm, while avoiding collision with obstacles and maintaining a given distance between neighboring robots and the reference point. To solve this problem, a method based on virtual forces that act on each individual robot in the swarm is proposed. These virtual forces are derived from virtual spring-damper connections acting from the obstacles, adjacent robots, and the reference point. The virtual forces allow the robots to dynamically adjust their trajectories to avoid obstacles and maintain the shape of the swarm. The proposed approach is evaluated through simulation and experiment with a swarm of two-wheeled robots. During the experiment the maximum deviation of inter-robot distances was equal to 0.08 m. With the set limit distance to obstacles equal to 0.2 m, from which virtual forces start to push the robot away from the edge of the obstacle, the shortest distance to obstacles was 0.11 m. The results demonstrate the effectiveness of the method in enabling the swarm to accurately follow the reference point while avoiding obstacles and maintaining the desired distances between robots. Overall, the approach represents a step forward in the development of virtual force-based algorithms for obstacle avoidance in swarm robotics.

Automatic Obstacle Avoidance Robot based on Artificial Intelligence Detection and Recognition

This paper designs an automatic obstacle avoidance robot based on artificial intelligence detection and recognition, including a shell, and a detection and obstacle avoidance mechanism is set inside the shell; the detection and obstacle avoidance mechanism includes a movable member, a screw, a first electric actuator, a first camera, a gear, a rack and a steering assembly; by setting the detection and obstacle avoidance mechanism, the first camera and the second camera can take pictures of the surrounding environment during the movement of the shell, and when the micro When the computer recognizes the obstacle, it controls the servo motor to run and makes the screw rotate, so that the movable part moves in the direction of no obstacle, and the rack at the bottom of the movable part moves together during the moving process of the movable part, and the gear will be driven to rotate the steering wheel during the moving process of the rack, so that the robot can perform obstacle avoidance operation, and the height of the camera can also be adjusted by adjusting the first electric pusher and the second electric pusher to adjust the By adjusting the first and second electric actuators, the height of the camera can be adjusted to adjust the detection range, making the robot more flexible.

Reinforcement learning with dynamic convex risk measures

AbstractWe develop an approach for solving time‐consistent risk‐sensitive stochastic optimization problems using model‐free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time‐consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules that aid in obtaining optimal policies. We further develop an actor–critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to three optimization problems: statistical arbitrage trading strategies, financial hedging, and obstacle avoidance robot control.

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.

Dynamic Obstacle Avoidance for Cable-Driven Parallel Robots With Mobile Bases via Sim-to-Real Reinforcement Learning

A Cable-Driven Parallel Robot (CDPR) with Mobile Bases (MBs) can modify its geometric architecture and is suitable for manipulation tasks in constrained environments. In manipulation tasks, a CDPR with MBs inevitably encounters obstacles, including dynamic obstacles. However, the high dimensional state space and a considerable number of constraints caused by multiple cables and MBs make the real-time dynamic obstacle avoidance of a CDPR with MBs challenging. This letter proposes a Reinforcement Learning (RL)-based dynamic obstacle avoidance method for a CDPR with MBs to deal with dynamic obstacles in real time. To explain the RL-based dynamic obstacle avoidance method, this letter focuses on a CDPR with four fixed-length cables connected to four MBs. An RL-based Obstacle Avoidance Controller (OAC) is developed and integrated into a trajectory tracking controller to address the dynamic obstacle avoidance problem of a CDPR with MBs tracking a target trajectory. To explain and evaluate the RL-based dynamic obstacle avoidance method further, an RL-based OAC is trained in a Mujoco simulator and transferred to a CDPR with four fixed-length cables connected to four MBs in the real world.