Intelligent Obstacle Avoidance Research Articles

Intelligent Obstacle Avoidance Controller for QBot2

This paper describes the design process and the steps involved in the implementation of a fuzzy logic-based controller for autonomous navigation of an educational robot called Qbot2. The differential-driven robot is equipped with three ultrasonic sensors (SRF-04) mounted on the front bumper and DC motors with built-in encoders and current sensors. The controller takes the inputs from three ultrasonic sensors and generates the speed commands to avoid any obstacle in its path. PCB is designed to process all the data from the sensors and direct the signal to the motors via motor driver circuits. The low-level implementation of the hurdle avoidance controller is implemented using an inexpensive Arduino UNO in real-time. The controller performance is validated through an experimental run. The designed platform can be used to implement various fuzzy inference systems in real-time and hence can be utilized as laboratory work for testing soft computing algorithms on mobile robotics.

Auto-reversing intelligent obstacle avoidance system based on optical fibre sensor

Auto-reversing intelligent obstacle avoidance system based on optical fibre sensor

Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection.

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.

An Intelligent Obstacle Avoidance Method in Unmanned Driving

This paper proposes an intelligent obstacle avoidance method in unmanned driving. This method first establishes a simplified model for the obstacle avoidance problem in unmanned driving, and constrains the car from kinematics, physics, travelable area, and critical collision. Then list the equations and uses the direct collocation method to discrete the equation. The problem is turned into a nonlinear programming problem.

Intelligent obstacle avoidance algorithms for autonomous underwater vehicle

Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.

Lidar Based Intelligent Obstacle Avoidance System for Autonomous Ground Vehicles

Autonomous ground vehicles (AGVs) started occupying our day-to-day life. AGVs can be programmed to be smart with the current technological advancements. In doing so, we can apply them to assist humans in many aspects like reducing road accidents, enabling us to use cars without driving knowledge, autonomous patrolling in dangerous zones, and autonomous farming. For AGVs to operate at this level of automation, it must be equipped with sensory perception devices to be aware of its surroundings, and also, a way to perceives this data is crucial. As a first step towards this, researchers have developed a vast number of camera vision-based efficient neural network algorithms for detecting and avoiding obstacles. Unfortunately, an AGV cannot survive only with computer vision as it suffers from several effects like night driving and erroneous estimation of distance information. Camera vision and lidar vision together is suitable for AGVs to operate in all conditions like day, night, and fog. We propose a novel neural network model, which transforms the lidar sensor data into obstacle avoidance decisions, which is integrated into the hybrid vision of any AGV. Existing lidar sensor-based obstacle detection and avoidance systems like 2D collision cone approaches are not suitable for real-time applications, as they lag in providing accurate and quick responses, which leads to collisions. The proposed intelligent Field of View (FOV) mechanism replaces classical mathematical approaches, which accurately mimics the behavior of human drivers. The model quickly takes decisions with a high level of accuracy to command the AGV upon being obstructed with obstacles in the trajectory. This makes the AGV drive in obstacle rich environments without manual maneuvering autonomously.

Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment

In a current multi-obstacle environment, mobile robots are needed to control obstacle avoidance ability intelligently. Therefore, a method of mobile robot obstacle avoidance control in multi-obstacle environment is proposed based on the minimum risk index. This method first segments the path of obstacle environment according to certain rules. Then, robots are constrained through the constrained time according to requirements of robot obstacle avoidance. Therefore, calculating motion obtains necessary and sufficient conditions with no collision avoidance relationship. Then, impact danger level of each movement stage for the robot is evaluated by considering the spatial distance, inertia, and relative movement speed of multiple obstacle environments. The global safety path is planned, the intelligent avoidance function is calculated, and intelligent avoidance control is implemented. Finally, simulation results show that our proposed method is a common method in intelligent obstacle avoidance, timely tracking of moving target, and makes full use of space.

Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment

Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment

Navigation behavioural decision-making of MASS based on deep reinforcement learning and artificial potential field

To realize intelligent obstacle avoidance and local path decisions for maritime autonomous surface ships (MASS) in uncertain environments, a navigation behavioural decision-making model based on deep reinforcement learning (DRL) algorithm improved by artificial potential field (APF) is proposed. Based on the analysis of navigation decision system and perception principle, the action space, reward function, motion search strategy and action value function are designed respectively for the purpose of steering to collision avoidance. The navigation behavioural decision-making model for MASS is improved by adding the prior information, the gravitational potential field and the obstacle repulsion potential field to update the initial action state value function and search path. Python and Pygame modules are used to build a simulation chart. Effectiveness of the algorithm is verified, with Tianjin Xingang port as a study case. The simulation results show that the APF-DRL algorithm is better than the DRL algorithm in training iteration time and piloting decision path, which improves the self-learning ability of MASS, and can meet the requirements of MASS path decision and adaptive obstacle avoidance.

Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning.

This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance.

Method for intelligent obstacle avoidance decision-making of unmanned vessel in unknown waters

Method for intelligent obstacle avoidance decision-making of unmanned vessel in unknown waters

Path Optimization Method of Autonomous Intelligent Obstacle Avoidance for Multi-joint Submarine Robot

ABSTRACT An, P., 2018. Path optimization method of autonomous intelligent obstacle avoidance for multi-joint submarine robot. In: Ashraf, M.A. and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Respon...

Optimizing emergency rescue and evacuation planning with intelligent obstacle avoidance in a chemical industrial park

A major chemical accident has the characteristics of being destructive, and potentially provoking a great loss of lives and property damage in any Chemical Industrial Park (CIP). Emergency rescue and evacuation are essential parts of emergency decision-making for enhancing the capacity and effectiveness of emergency handling and reducing the potential loss of accidents. Most of current literature concentrates on one-way route planning of emergency rescue and evacuation, and applies different models, optimization objectives and algorithms. However, when applying the one-way route planning model in a CIP, a road conflict is possible due to the inherent weak traffic capacity. Therefore, a new method of two-way route planning of emergency rescue and emergency evacuation which considers intelligent obstacle avoidance, is proposed in the paper. The method we developed integrates three modeling components: (i) a dynamic grid environment model to simulate the interaction between the road network and the time-varying location of emergency rescue and evacuation. (ii) a two-way route planning model to simultaneously optimize routes of emergency rescue and routes of emergency evacuation. (iii) an intelligent obstacle avoidance model to prevent potential road conflicts. The results illustrate that the proposed model is able to generate a set of two-way optimum routes and overcomes possible road conflicts successfully.

The Design and Implementation of a Mobile Car Intelligent Toys

The purpose of the subject is to design a car based on 51 single chip microcomputer control of nuclear intelligence toy car. Design is mainly divided into four modules respectively control module, test module, drive module, display module. Detection module by infrared receiving tube as tracking and obstacle avoidance of the detector to external environment for testing, testing mileage with hall element as the detector, test results of the test modules to control module, control module processed signal into the driver module to control the car turn left, turn right, forward, backward, at the same time control module and analysis processing state of marching into the 1602 LCD liquid crystal display status display. After many trials completed hardware design, the control core programming and sensor debug, finally realize the automatic tracking of toy cars, intelligent obstacle avoidance, mileage of display design requirements, and increase the car alarm, lights flashing remind and additional features such as voice control.

Path planning and Obstacle avoidance approaches for Mobile robot

A new path planning method for Mobile Robots (MR) has been developed and implemented. On the one hand, based on the shortest path from the start point to the goal point, this path planner can choose the best moving directions of the MR, which helps to reach the target point as soon as possible. On the other hand, with an intelligent obstacle avoidance, our method can find the target point with the near-shortest path length while avoiding some infinite loop traps of several obstacles in unknown environments. The combination of two approaches helps the MR to reach the target point with a very reliable algorithm. Moreover, by continuous updates of the on-board sensors information, this approach can generate the MRs trajectory both in static and dynamic environments. A large number of simulations in some similar studies environments demonstrate the power of the proposed path planning algorithm.

Comparison of Hardware Design Complexity to Extract Depth Map from Stereo Images

Computer vision technology has been researched in the field of mobile robots, intelligent vehicle navigation aids, obstacle avoidance and many others. We implement fast and accurate census transform algorithm in software and hardware for computer vision to extract depth map from different two images. We have designed in C language and Verilog hardware description language, verified by Modelsim, and implemented on FPGA for real-time processing capability. We have converted the census transform algorithm to hardware structure to compute depth map for two images in parallel processing. The resultant system extracts the depth information within search-range of 30 from 240 x 320 image using the window size of 3by3, 5by5 and 7by7, respectively.

Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation

Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.

Outdoor Mobile Robot Navigation System Based on BCI and Dual Laser Radar

A machine intelligence assistant BCI fusion navigation method of an outdoor mobile robot is put forward in view of the problem of BCI’s low signal-to-noise ratio, bad accuracy and long time delay. A vehicle navigation system based on BCI and dual laser radar is designed and implemented. First, improved angle potential field method based on dual laser radar is used for local path planning, then with navigation intention from BCI system, control commands are generated by fusion decision and used for driving a electric vehicle with modified mechanical system. Experiments show that the system can realize intelligent obstacle avoidance and human-machine collaborative navigation based on environmental obstacle information and brain machine interface control intention and it has higher accuracy, fault tolerance and robustness

Design of Intelligent Obstacle Avoidance Car Based on FPGA

This design uses the Cyclone series of FPGA chip as the core of the control unit of the intelligent car. Besides, the system also includes power supply, DC electric machineries, steering, ultrasonic sensors, wireless data transceiver module and other circuits. According to the movement direction and movement status of the intelligent car to realize the function of monitoring and remote control, what is more, the FPGA will achieving the measurement data to process, in order to implement the function of automatic avoidance of road blocks. Meanwhile, the LED real-time display the ultrasonic distance measurement.

Application of Fuzzy Control Theory in Obstacle Avoidance Simulation of Intelligent Wheelchair

This paper introduces application of the fuzzy control theory in obstacle avoidance simulation of intelligent wheelchair. Aiming at the path planning problem of intelligent wheelchair, with information fuzzy theory, based on the information acquisition of ultrasonic sensors installed in the intelligent wheelchair, processed by fuzzy logic algorithm, then the fuzzy controller of fuzzy control theory was constructed in Matlab, so intelligent wheelchair obstacle avoidance simulation was conducted. The results show that this method can effectively avoid the obstacles, realize the continuous movement of intelligent wheelchair.