Real-time Obstacle Avoidance Research Articles

A Real-Time Hydrodynamic-Based Obstacle Avoidance System for Non-holonomic Mobile Robots with Curvature Constraints

The harmonic potential field of an incompressible nonviscous fluid governed by the Laplace’s Equation has shown its potential for being beneficial to autonomous unmanned vehicles to generate smooth, natural-looking, and predictable paths for obstacle avoidance. The streamlines generated by the boundary value problem of the Laplace’s Equation have explicit, easily computable, or analytic vector fields as the path tangent or robot heading specification without the waypoints and higher order path characteristics. We implemented an obstacle avoidance approach with a focus on curvature constraint for a non-holonomic mobile robot regarded as a particle using curvature-constrained streamlines and streamline changing via pure pursuit. First, we use the potential flow field around a circle to derive three primitive curvature-constrained paths to avoid single obstacles. Furthermore, the pure pursuit controller is implemented to achieve a smooth transition between the streamline paths in the environment with multiple obstacles. In addition to comparative simulations, a proof of concept experiment implemented on a two-wheel driving mobile robot with range sensors validates the practical usefulness of the integrated system that is able to navigate smoothly and safely among multiple cylinder obstacles. The computational requirement of the obstacle avoidance system takes advantage of an a priori selection of fast computing primitive streamline paths, thus, making the system able to generate online a feasible path with a lower maximum curvature that does not violate the curvature constraint.

Real-time obstacle avoidance of mobile robots using state-dependent Riccati equation approach

In this paper, state-dependent Riccati equation (SDRE) method-based optimal control technique is applied to a robot. In recent years, issues associated with the robotics have become one of the developing fields of research. Accordingly, intelligent robots have been embraced greatly; however, control and navigation of those robots are not easy tasks as collision avoidance of stationary obstacles to doing a safe routing has to be taken care of. A moving robot in a certain time has to reach the specified goals. The robot in each time step needs to identify criteria such as velocity, safety, environment, and distance in respect to defined goals and then calculate the proper control strategy. Moreover, getting information associated with the environment to avoid obstacles, do the optimal routing, and identify the environment is necessary. The robot must intelligently perceive and act using adequate algorithms to manage required control and navigation issues. In this paper, smart navigation of a mobile robot in an environment with certain stationary obstacles (known to the robot) and optimal routing through Riccati equation depending on SDRE is considered. This approach enables the robot to do the optimal path planning in static environments. In the end, the answer SDRE controller with the answer linear quadratic controller will be compared. The results show that the proposed SDRE strategy leads to an efficient control law by which the robot avoids obstacles and moves from an arbitrary initial point × 0 to a target point. The robust performance of SDRE method for a robot to avoid obstacles and reach the target is demonstrated via simulations and experiments. Simulations are done using MATLAB software.

Path planning in uncertain environment by using firefly algorithm

Abstract Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm (FA) for Mobile Robot Navigation (MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.

User-centered design of a depth data based obstacle detection and avoidance system for the visually impaired

The development of a novel depth-data based real-time obstacle detection and avoidance application for visually impaired (VI) individuals to assist them in navigating independently in indoors environments is presented in this paper. The application utilizes a mainstream, computationally efficient mobile device as the development platform in order to create a solution which not only is aesthetically appealing, cost-effective, lightweight and portable but also provides real-time performance and freedom from network connectivity constraints. To alleviate usability problems, a user-centered design approach has been adopted wherein semi-structured interviews with VI individuals in the local context were conducted to understand their micro-navigation practices, challenges and needs. The invaluable insights gained from these interviews have not only informed the design of our system but would also benefit other researchers developing similar applications. The resulting system design along with a detailed description of its obstacle detection and unique multimodal feedback generation modules has been provided. We plan to iteratively develop and test the initial prototype of the system with the end users to resolve any usability issues and better adapt it to their needs.

Real-time CUDA-based stereo matching using Cyclops2 algorithm

This paper presents a novel stereo matching algorithm Cyclops2. The algorithm produces a disparity image, provided two rectified grayscale images. The matching is based on the concept of minimising a weight function calculated using the absolute difference of pixel intensities. We present three simple and easily parallelizable weight functions. Each presented function gives a different trade-off between algorithm processing time and reconstructed depth image accuracy. Detailed description of the algorithm implementation in CUDA is provided. The implementation was specifically optimised for embedded NVIDIA Jetson platform. NVIDIA Jetson TK1 and TX1 boards have been used to evaluate the algorithms. We evaluated seven algorithm variations with different parameter values. Each variation results in a different speed accuracy trade-off, demonstrating that our algorithm can be used in various situations. The presented algorithm achieves up to 70 FPS processing time on lower resolution images (750 × 500 pixels) and up to 23 FPS on high-resolution images (1500 × 1000 pixels). The use of optional post-processing stage (median filter) has also been investigated. We conclude that despite its limitations, our algorithm is relevant in the field of real-time obstacle avoidance.

A New Efficient Obstacle Avoidance Control Method for Cars Based on Big Data and Just-in-Time Modeling

This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.

COLREGS Based Path Planning and Bearing Only Obstacle Avoidance for Autonomous Unmanned Surface Vehicles

Obstacle avoidance for autonomous unmanned surface vehicles (USV) is a very critical issue and must be in accordance with international maritime rules such as Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). Realtime obstacle avoidance for USV’s can be achieved with distance and bearing information obtained by lidar (light detection and ranging), radar (radio detection and ranging) etc. sensors. But if these sensors fail while USV’s are at sea, alternative strategies are needed to execute local obstacle avoidance for maintaining the safety of life and property at sea. In this paper, we present COLREGS based obstacle avoidance and path planning using Fast Marching Square algorithm for multiple USV’s and effectiveness of visual guidance aided bearing only navigation in case of distance measuring sensor failure.

Reinforcement Learning Strategy for Automatic Control of Real-time Obstacle Avoidance based on Vehicle Dynamics

Reinforcement Learning Strategy for Automatic Control of Real-time Obstacle Avoidance based on Vehicle Dynamics

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs.

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.

HYBRID COMMUNICATION NETWORK OF MOBILE ROBOT AND QUAD-COPTER

This paper introduces the design and development of QMRS (Quadcopter Mobile Robotic System). QMRS is a real-time obstacle avoidance capability in Belarus-132N mobile robot with the cooperation of quadcopter Phantom-4. The function of QMRS consists of GPS used by Mobile Robot and image vision and image processing system from both robot and quad-copter and by using effective searching algorithm embedded inside the robot. Having the capacity to navigate accurately is one of the major abilities of a mobile robot to effectively execute a variety of jobs including manipulation, docking, and transportation. To achieve the desired navigation accuracy, mobile robots are typically equipped with on-board sensors to observe persistent features in the environment, to estimate their pose from these observations, and to adjust their motion accordingly. Quadcopter takes off from Mobile Robot, surveys the terrain and transmits the processed Image terrestrial robot. The main objective of research paper is to focus on the full coordination between robot and quadcopter by designing an efficient wireless communication using WIFI. In addition, it identify the method involving the use of vision and image processing system from both robot and quadcopter; analyzing path in real-time and avoiding obstacles based-on the computational algorithm embedded inside the robot. QMRS increases the efficiency and reliability of the whole system especially in robot navigation, image processing and obstacle avoidance due to the help and connection among the different parts of the system.

Real-time Obstacle Avoidance of Non-holonomic Mobile Robots Using Expanded Guide Circle Method

Real-time Obstacle Avoidance of Non-holonomic Mobile Robots Using Expanded Guide Circle Method

NAVIGATION OF MOBILE ROBOT- ALGORITHM FOR PATH PLANNING & COLLISION AVOIDANCE- A REVIEW

The field of autonomous mobile robotics has recently gained many researchers’ interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective of our project is applications based mobile robot systems, especially in navigation, designing real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective behind using the obstacle avoidance approach is to obtain a collision-free trajectory from the starting point to the target in monitoring environments. The ability of the robot to follow a path, detects obstacles, and navigates around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Motion planning that allows the robot to reach its target without colliding with any obstacles that may exist in its path.
 To avoid collision in the mobile robot environment, providing a path planning& line following approach. Line following, path planning, collision avoidance, back propagation, improved memory, detecting long distance obstacles. Cheap and economical than the former one. Also work with back propagation technique.

Real-Time Path Generation and Obstacle Avoidance for Multirotors: A Novel Approach

Multirotors, among all aerial vehicles, are fundamental instruments in many situations, i.e. video recording of sport events, leisure, environmental monitoring before or after a disaster. In particular, in the context of environmental monitoring, the possibility of following a predetermined path while avoiding obstacles is extremely relevant. In this work, we propose a novel method for path definition in presence of obstacles, which describes a curve as the intersection of two surfaces. The planner, based on that path definition along with a Cascaded control architecture and utilizing a nonlinear control technique for both control loops (position and attitude), creates a framework to manipulate the multicopters’ behaviors. The method is demonstrated to be able to generate a safe path taking into account obstacles perceived in real-time and avoids collisions. These algorithms are embedded in a software package to control the flight of a fully autonomous AscTec Firefly hexacopter with two cameras and onboard processing capabilities.

Obstacle Avoidance in Real Time With Nonlinear Model Predictive Control of Autonomous Vehicles

A Nonlinear model predictive control (NMPC) for trajectory tracking with the obstacle avoidance of autonomous road vehicles traveling at realistic speeds is presented in this paper, with a focus on the performance of those controllers with respect to the look-ahead horizon of the NMPC. Two different methods of obstacle avoidance are compared and then the NMPC is tested in several simulated but realistic tracking scenarios involving static obstacles on constrained roadways. In order to simplify the vehicle dynamics, a bicycle model is used for the prediction of future vehicle states in the NMPC framework. However, a high-fidelity, nonlinear CarSim vehicle model is used to evaluate the vehicle performance and test the controllers in the simulation results. The CPU time is also analyzed to evaluate these schemes for real-time applications. The results show that the NMPC controller provides satisfactory online tracking performance in a realistic scenario at normal road speeds while still satisfying the real-time constraints. In addition, it is shown that the longer prediction horizons allow for better responses of the controllers, which reduce the deviations while avoiding the obstacles, as compared with shorter horizons.

Study on Autonomous Obstacle Avoidance Based On Guidance

DJI Guidance is an entirely new visual sensor navigation system, which has a function of real-time transmission of the depth of field images and other data. It is a robust optical sensing platform which significantly promotes the development of unmanned aerial vehicle (hereinafter referred to as UAV). On this basis, we have further studied and developed Guidance in application to the field of UAVs to also cut a striking figure, by applying the threshold and binaryzation to the single frame of a static image generated from Guidance. Utilizing OpenCV’s contour detection algorithm, we find all the closed contour information and re-plan the feasible region. Also, we specially re-project the depth information to achieve three-dimensional reconstruction of the image. Then combining the re-planed feasible domain with three-dimensional reconstruction, we can perform real-time obstacle avoidance and mapping. The results demonstrate that our improvement can efficiently complete the obstacle avoidance and real-time mapping in the specific circumstances.

Obstacle avoidance with translational and efficient rotational motion control considering movable gaps and footprint for autonomous mobile robot

This paper presents a sensor-based real-time obstacle avoidance method for an autonomous omnidirectional mobile robot based on simultaneous control of translational and efficient rotational motion considering movable gaps and the footprint. Autonomous mobile service robots that have been developed in recent years have arms that work and execute tasks. Depending on the task using moving parts, the shape of the robot (i.e., the footprint) changes. In this study, to improve the safety and possibility of reaching a goal even through a narrow gap with unknown obstacles, a sensor-based real-time obstacle avoidance method with simultaneous control of translational and efficient rotational motion (without unnecessary rotational motion) based on the evaluation of movable gaps and the footprint is proposed. To take account of the anisotropy footprint of the robot, multiple-circle robot model is proposed. In this paper, a novel control method based on fuzzy set theory is presented. To verify the effectiveness of the proposed method, several simulations and experiments are carried out.

Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics System

The field of autonomous mobile robotics has recently gained many researchers’ interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. Therefore, an efficient collision avoidance and path following methodology is needed to develop an intelligent and effective autonomous mobile robot system. This paper introduces a new technique for line following and collision avoidance in the mobile robotic systems. The proposed technique relies on the use of low-cost infrared sensors, and involves a reasonable level of calculations, so that it can be easily used in real-time control applications. The simulation setup is implemented on multiple scenarios to show the ability of the robot to follow a path, detect obstacles, and navigate around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Webots simulator was used to validate the effectiveness of the proposed technique.

Probabilistic fuzzy controller based robotics path decision theory

Purpose This paper aims to comprise the new ideas for the efficient implementation to autonomous mobile robot navigation over the complex environment in presence of static obstacles. Design/methodology/approach The fuzzy decision via probability and its distribution is applied for the generating objective function subject to the robotics path and obstacle avoidance. The present objective function is formed to achieve high level of significance for the real-time obstacle avoidance and the efficiency. Findings The proposed controller makes a robot take its decision effectively in complex environment in a feasible time. In comparison with other AI approaches the proposed controller reflects that the proposed method outperforms in terms of optimal path and quality of solution. The experimental and simulation results are nearly same. Originality/value It has been tested in a complex crowded environment to find shorter path than existed approaches. For the validation, the experimental and simulation result using Matlab Software (R2008) has provided at the end of the paper.

Real-time dynamic system to path tracking and collision avoidance for redundant robotic arms

An dynamic system for real-time obstacle avoidance path planning of redundant robots is constructed in this paper. Firstly, the inter-frame difference method is used to identify the moving target and to calculate the target area, then on the basis of color features and gradient features extracted from the target area, the feature fusion Cam-Shift mean shift algorithm is used to track target, improving the robustness of the tracking algorithm. Secondly, a parallel two-channel target identification and location method based on binocular vision is proposed, updating the target's three-dimensional information in real time. Then, a dynamic collision-free path planning method is implemented: the safety rods are removed through the intersection test, and the minimum distance is derived directly by using the coordinate values of the target in the local coordinate system of the rod. On this basis, the obstacle avoidance gain and escape velocity related to the minimum distance is established, and obstacle avoidance path planning is implemented by using the zero space mapping matrix of redundant robot. Experiments are performed to study the efficiency of the proposed system.

Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.