Robot Obstacle Avoidance Research Articles

Robot target tracking control considering obstacle avoidance based on combination of deep reinforcement learning and PID

When simultaneously addressing the challenges of dynamic target tracking and obstacle avoidance for robots, conventional control and control only based on reinforcement learning cannot deal with the complex scenarios effectively. The purpose of this study is to design a robot control algorithm that combines deep reinforcement learning (Soft Actor-Critic, SAC) with PID to achieve real-time tracking of a moving object and effectively avoid single or multiple obstacles. The control of the robot is divided into two key components: initially, the first joint of the 6-degree-of-freedom robot is controlled by PID algorithm, which makes the working plane (the plane coincident with the axis of the first joint and parallel to the linkage) quickly approach the target until it overlaps. Subsequently, the task of reinforcement learning is simplified to control the planar robot to track the target projection in working plane while avoiding the obstacle projection, ultimately achieve target tracking and obstacle avoidance in 3D space. The simulation and experiment results show that the proposed method has good efficiency and convergence speed. The SAC-PID strategy effectively controls the Universal-Robots UR5 to complete dynamic target tracking while accomplishing obstacle avoidance in both virtual and real-world environments.

Trajectory optimization and obstacle avoidance of autonomous robot using Robust and Efficient Rapidly Exploring Random Tree.

One of the key challenges in robotics is the motion planning problem. This paper presents a local trajectory planning and obstacle avoidance strategy based on a novel sampling-based path-finding algorithm designed for autonomous vehicles navigating complex environments. Although sampling-based algorithms have been extensively employed for motion planning, they have notable limitations, such as sluggish convergence rate, significant search time volatility, a vast, dense sample space, and unsmooth search routes. To overcome the limitations, including slow convergence, high computational complexity, and unnecessary search while sampling the whole space, we have proposed the RE-RRT* (Robust and Efficient RRT*) algorithm. This algorithm adapts a new sampling-based path-finding algorithm based on sampling along the displacement from the initial point to the goal point. The sample space is constrained during each stage of the random tree's growth, reducing the number of redundant searches. The RE-RRT* algorithm can converge to a shorter path with fewer iterations. Furthermore, the Choose Parent and Rewire processes are used by RE-RRT* to improve the path in succeeding cycles continuously. Extensive experiments under diverse obstacle settings are performed to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach outperforms existing methods in terms of computational time, sampling space efficiency, speed, and stability.

Lidar-Based Obstacle Detection and Path Prediction for Unmanned Surface Vehicles

Abstract Unmanned Surface Vehicles (USVs) represent an advanced technology with a wide range of applications in maritime contexts, and they offer a promising alternative to manned systems, particularly for the provision of services such as riverbed mapping. Furthermore, the use of USVs in combination with other robotic systems, such as flying drones, remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs), opens up innovative ways to inspect and monitor maritime infrastructure. However, the use of these systems in dynamic environments, such as ports or shipping lanes, requires a high degree of adaptability and precision in navigation to manoeuvre safely between other traffic participants and obstacles to prevent collisions. To address this problem, USVs require sophisticated perception systems for the precise detection and classification of nearby objects. Leveraging Light Detection and Ranging (LiDAR) technology, which is renowned for its efficacy in obstacle avoidance in robotics, this paper outlines the adaptation of a LiDAR sensor for USVs. The proposed pipeline enables the detection of other moving vessels and the accurate determination of their positions and trajectories up to 150 m from the USV. This paper introduces an obstacle detection system and validates it within a port setting. A methodical approach to LiDAR data preprocessing, clustering, and point cloud extraction will be outlined. Furthermore, it explores the application of an Unscented Kalman Filter (UKF) for the projection of motion paths of identified entities. This advanced form of data analysis provides the ability to predict the future position and path of potential obstacles, optimize decision-making for autonomous navigation algorithms and significantly improve the safety of USV operations. The pipeline is tested experimentally in sea trials by enacting five different scenarios that USV may encounter in a port setting. The results of the pipeline’s tracking capabilities are compared to the ground truth data collected by the tracked vessel and plotted to visualize the error.

Mixed obstacle avoidance in mobile chaotic robots with directional keypads and its non-identical generalized synchronization

Mixed obstacle avoidance in mobile chaotic robots with directional keypads and its non-identical generalized synchronization

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

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.

Hybrid dynamical systems logic and its refinements

Hybrid dynamical systems describe the mixed discrete dynamics and continuous dynamics of cyber-physical systems such as aircraft, cars, trains, and robots. To justify correctness properties of the safety-critical control algorithms for their physical models, differential dynamic logic (▪) provides deductive specification and verification techniques implemented in the theorem prover ▪. The logic ▪ is useful for proving, e.g., that all runs of a hybrid dynamical system α satisfy safety property φ (i.e., ▪), or that there is a run of the hybrid dynamical system α ultimately reaching the desired goal φ (i.e., ▪). Logical combinations of ▪'s operators naturally represent safety, liveness, stability and other properties. Variations of ▪ serve additional purposes. Differential refinement logic (▪) adds an operator α≤β expressing that hybrid system α refines hybrid system β, which is useful, e.g., for relating concrete system implementations α to their abstract verification models β. Just like ▪, ▪ is a logic closed under all operators, which opens up systematic ways of simultaneously relating systems and their properties, of reducing system properties to system relations or, vice versa, reducing system relations to system properties. A second variant of ▪, differential game logic (▪), adds the ability of referring to winning strategies of players in hybrid games, which is useful for establishing correctness properties where the actions of different agents may interfere either because they literally compete with one another or because they may interact accidentally. In the theorem prover ▪, ▪ and its variations have been used for verifying ground robot obstacle avoidance, the Federal Aviation Administration's Next-Generation Airborne Collision Avoidance System ACAS X, and the Federal Railroad Administration's train control model.

Trajectory Tracking with Obstacle Avoidance for Nonholonomic Mobile Robots with Diamond-Shaped Velocity Constraints and Output Performance Specifications.

In this paper, we address the trajectory-/target-tracking and obstacle-avoidance problem for nonholonomic mobile robots subjected to diamond-shaped velocity constraints and predefined output performance specifications. The proposed scheme leverages the adaptive performance control to dynamically adjust the user-defined output performance specifications, ensuring compliance with input and safety constraints. A key feature of this approach is the integration of multiple constraints into a single adaptive performance function, governed by a simple adaptive law. Additionally, we introduce a robust velocity estimator with a priori-determined performance attributes to reconstruct the unmeasured trajectory/target velocity. Finally, we validate the effectiveness and robustness of the proposed control scheme, through extensive simulations and a real-world experiment.

Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation

Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots.

Research on mobile robot path planning in complex environment based on DRQN algorithm

A deep reinforcement Q learning algorithm (DRQN) based on radial neural network is proposed to achieve path planning and obstacle avoidance for mobile robots in complex ground environments with different types of obstacles, including static and dynamic obstacles. Firstly, the path planning problem is represented as a partially-observed Markov decision process. Steering angle, running characteristics, and other elements are introduced into the state-action decision space and the greedy factor is dynamically adjusted using a simulated annealing algorithm, which improves the mobile robot’s environment exploration and action selection accuracy. Secondly, the Q-learning algorithm is improved by replacing the Q-table structure with an RBF neural network to enhance the approximation ability of the algorithm’s function values, and the parameters of the implicit layer and the weights between the implicit and the output layer are trained using the dynamic clustering and least-mean methods respectively, which improves the convergence speed and enhances the ability of mobile robots to handle large-scale computation. Lastly, the double reward mechanism is set up to prevent the mobile robot from blind searching in unknown environments, which enhances the learning ability and improves path planning safety and flexibility at the same time. Different types of scenarios are set up for simulation experiments, and the results verified the superiority of the DQRN algorithm. Taking the 30 * 30 complex scene as an example, using the DQRN algorithm for path planning reduces the values of distance, turning angle, and planning time by 27.04%, 7.76%, and 28.05%, respectively, compared to the average values of Q-learning, optimized Q-learning, deep Q-learning, and DDPG algorithms, which can effectively improve the path planning efficiency for mobile robots in complex environments.

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.

Research on Methods and Development Trends of Mobile Robots in Obstacle Avoidance

In 2010s, the scale of mobile robots in the market is increasingly developing. Nowadays, China is becoming one of the most active regions in the world for the development of machine vision. Computer vision is the most crucial part of obstacle avoidance in mobile robots. Therefore, the research topic of this article is the intelligent obstacle avoidance methods and development trends of mobile robots based on machine vision. The research method of this article is as follows: Firstly, the working principle of machine vision, the application process of machine vision in mobile robot obstacle avoidance, and the development trend of machine vision are introduced. Secondly, the development and shortcomings of machine vision were discussed, and feasible solutions were proposed to address the potential problems in current machine vision. Finally, this article looks forward to the future research directions of machine vision and predicts that machine vision technology will become one of the most powerful detection technologies in science and technology. After this study, it was found that the machine vision of mobile robots has shortcomings in obstacle avoidance, such as difficulty in identifying multiple obstacles and limitations in processing large-scale data. Therefore, it is believed that machine vision still has room for development.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Line Follower Robot with Obstacle Avoidance

One of the most available robots in applications are the line followers. These robots normally follow either white or a black line. This paper introduces an Autonomous Emergency Indicating Line Follower and Obstacle Avoiding Robot. The robot moves on a specific path determined by the user and detects the obstacle that comes in its way. The vehicle robot stops and diverts its route and returns back to original path, once the obstacle has been overcome. Whenever the robot gets lost from the track, the robot automatically follow the predefined path. The robot is achieved through the implementation of many sensors interacting with the controllers. An experimental study of time taken by the robot for different length obstacle as well as for different speed of the robot is also performed. Key Words: Sensors, Obstacle Detection, Line Follower

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

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

Robot obstacle avoidance optimization by A* and DWA fusion algorithm.

The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning.

Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots

Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots

Non-parametric Gaussian process movement primitive with via-point constraint for effective and safe robot skill learning

Learning from demonstration (LFD) algorithms has been proven to be an effective way to encode basic human skills, such as probabilistic movement primitives (ProMPs). However, such algorithms are based on parametric methods, which means that more effort is required to expand to multi-dimensional and high reproduction accuracy. In this article, a novel non-parametric LFD algorithm is proposed, named Gaussian process movement primitive based on Gaussian mixture regression (GPGR), which has lower computational complexity and high accuracy. We encapsulate the variability of the demonstration set into the prior of Gaussian processes and propose a necessary and sufficient condition to ensure that the generated trajectory can pass the expected via point with 100% probability, which is different from existing ProMPs and their variants. In addition, we propose a novel trajectory obstacle avoidance method. The method allows efficient and safe robot obstacle avoidance by solving for a small number of via points. The effectiveness of the algorithm is validated through a series of experiments on the LASA dataset and the Franka Emika Panda robot.

Design and Development of Plant Watering Robot Using Arduino Microcontroller

With the growing demand for efficient and sustainable agricultural practices, there is an increasing need for innovative solutions to automate plant care processes. This project presents the design and development of a Plant Watering Robot utilizing Arduino microcontroller. The aim of this project is to create an intelligent and autonomous system capable of providing precise and timely watering to plants and also like a obstacle avoidance robot while it is spraying water & ensuring their optimal growth while minimizing water usage. An Arduino microcontroller serves as the brain of the system, enabling the integration of various sensors to collect environmental data, The combination of obstacle avoidance sensors ensures safe and efficient movement, preventing collisions with obstacles and potential damage to the robot or plants. The heart of the system lies in the intelligent decision-making algorithm implemented on the Arduino microcontroller. Keywords—IR sensors, L298N Controller driver, HC05 Bluetooth module, Arduino microcontroller, motors.