Obstacle Avoidance Technique Research Articles

Load Transportation by Quadrotors in Crowded Workspaces

Load transportation by unmanned aerial vehicles has many advantages in comparison with load transportation by unmanned ground vehicles. With the vehicle in the air obstacles in the ground and hard terrains are not a problem anymore, for instance. Moreover, higher speeds can be developed, allowing faster task accomplishment. The disadvantage is the possibility of load oscillations during the flight, which should be compensated through using suitable controllers. Furthermore, aiming at a real application, such as the delivery of medical kits for victims of disasters, the control system responsible for the navigation should embed an obstacle avoidance technique that works in harmony with the compensation of the load oscillations. In this work a load delivery task in an environment crowded with obstacles is addressed, in which the aerial vehicle should deliver the cargo at a specified target point. Euler-Lagrange equations are used to model the vehicle-load system, and a feedback-linearization controller is designed based on such a model, which is able to reject the load oscillations. The obstacle avoidance module embedded in the controller is also concerned to the efficiency in the task accomplishment, such that it avoids abrupt movements and reduction of the forward velocity of the vehicle. Several real experiments in which the proposed control system is adopted have been run, some of them presented here, whose results validate the proposed controller.

Coordination control and obstacle avoidance for a team of mobile robots in unknown environment

This paper presents a dual-level control structure for controlling a mobile robot and/or a team of mobile robots to navigate through an unknown (static or dynamic) environment. The higher-level controller operates in cooperation with robot's state estimation and mapping algorithm, extended Kalman filter - simultaneous localisation and mapping (EKF-SLAM), and the lower-level controller (PID) controls the motion of the robot when it encounters an obstacle and manoeuvres the robot around the obstacle/s. The higher-level controller jumps in as soon as the robot is out of the obstacle range and moves the robot to the goal. The obstacle avoidance technique involves a novel approach to calculate the rebound angle. The experimental results show that the proposed dual-level control structure can be effectively used to manoeuvre a mobile robot or a team of mobile robots to navigate through a dynamic environment.

Obstacle Recognition and Avoidance for UAVs Under Resource-Constrained Environments

Existing resource-intensive obstacle avoidance techniques are hard to be applied on the small-size Unmanned Aerial Vehicles (UAVs) that have limited sensing and computation capacity. Therefore, it is necessary to develop an obstacle recognition and avoidance scheme which works under resource-constrained environments. To this backdrop, this paper first presents an obstacle recognition model based on monocular vision feature points. Afterwards, an obstacle recognition algorithm is put forward, whose computational complexity is low. Then, an obstacle avoidance method is proposed to regulate the obstacle-avoidance path of a UAV until it arrives its destination. To evaluate the effectiveness of the presented algorithms, we design and implement a simulation platform on Objective Modular Network Testbed in C++ (OMNeT++), and conduct a series of experiments. Experimental results show that the proposed model and algorithms can effectively guide a micro UAV to its destination using only an embedded processor and 0.5kg extra load. Even in poor communication conditions, the UAV can independently avoid obstacles and reach the destination only by acquiring the destination coordinates from the ground station.

Coordination control and obstacle avoidance for a team of mobile robots in unknown environment

This paper presents a dual-level control structure for controlling a mobile robot and/or a team of mobile robots to navigate through an unknown (static or dynamic) environment. The higher-level controller operates in cooperation with robot's state estimation and mapping algorithm, extended Kalman filter - simultaneous localisation and mapping (EKF-SLAM), and the lower-level controller (PID) controls the motion of the robot when it encounters an obstacle and manoeuvres the robot around the obstacle/s. The higher-level controller jumps in as soon as the robot is out of the obstacle range and moves the robot to the goal. The obstacle avoidance technique involves a novel approach to calculate the rebound angle. The experimental results show that the proposed dual-level control structure can be effectively used to manoeuvre a mobile robot or a team of mobile robots to navigate through a dynamic environment.

Design of Real-time Safety Accident Prevention Solution for Socially Vulnerable using Object Recognition and Tracking Technology

All countries around the world want to create a safe society and a safe nation. However, crime targeting the socially vulnerable groups continues to increase. This is because the vulnerable groups have physical and physical weaknesses. Therefore, in order to pursue a safe society, it is necessary to protect the socially vulnerable. In this paper, we utilize the latest trend, drones, to protect against vulnerable groups. That is, we designed a real-time accident prevention solution for the elderly, the disabled, children and women using the drones. Drones' autonomous flight technology, obstacle detection technology, collision avoidance technology, wireless communication linkage technology, and real time image transmission technology. Through this, we have studied to enable safe activities in the blind spot of CCTV. In this paper, we have identified the problem of crime prevention system using existing CCTV system. To solve this problem, we have developed a safety accident prevention solution using drones. To this end, we designed the drones to enable real-time tracking by applying autonomous flight technology, obstacle avoidance technique, and object tracking technology. In addition, wireless communication between the object and the drone was applied to prevent flight deviation. And, it is designed to link emergency notification service through real-time video transmission during flight.

From Sensor-Space to Eigenspace – A Novel Real-Time Obstacle Avoidance Method for Mobile Robots

This paper presents a novel real-time obstacle avoidance and navigation technique called as “Free-configuration Eigenspace” (FCE). The FCE enables an autonomous robot to detect unknown obstacles and avoid collisions while simultaneously steering the robot towards the target. The methodology utilizes a two-dimensional (2D) Cartesian Eigenspace as a world model. 2D way points (in world model) are extracted by computing (through FCE model) stacked Eigenvectors of laser data at discrete time scans which manifest desired robotic trajectory. The world model is updated continuously upon obtaining discrete time laser scans sampled by on-board Light Detection and Ranging (LiDAR) sensors. The FCE technique has been implemented on Robotic Operating System and real-time robotics simulator Gazebo. Encouraging results are obtained and shown in this paper. The FCE has also been tested in real-time with laser scans obtained from VL6180X LiDAR sensor mounted on a custom autonomous robot. The results obtained are further compared with the state-of-the-art obstacle avoidance and path planning method called as vector field histogram (VFH). The FCE results (shown in the paper) are comparable, encouraging, and outperform VFH in path length and safe distance maintenance.

Dynamic optimization of snake robot with environment detection and analysis

Recent developments of snake robots have seen increasing interest in the aid of robotic systems to assist in disaster operations. This paper presents a design of snake robot for dynamic optimization to maneuver in various environments. The robot makes utilization of the friction between the body of the robot and the environment to move in. The components of snake robot chassis fabricated using 3D printing. Environment parameters such as temperature and moisture can be used to analysis the hazardous scenario. Obstacle avoidance techniques were also incorporated. Such a model was also simulated using the V-REP to verify its movements capabilities and to compare with real-time operation. Zig-bee module were utilized to wirelessly control the snake robot in remote areas for search and rescue operations. This paper describes the dynamic optimization of snake robot and presents experimental results of the robot locomotion through various environments.

Optimal path planning for unmanned ground vehicles using potential field method and optimal control method

This paper presents an optimal path planning algorithm for unmanned ground vehicle (UGV) to control its direction during parking manoeuvres by employing artificial potential field method (APF) combined with optimal control theory. A linear two-degree-of-freedom vehicle model with lateral and yaw motion is derived and simulated in MATLAB. The optimal control theory is employed to generate an optimal collision-free path For UGV from starting to the desired locations. The obstacle avoidance technique is mathematically modelled using APF including both the attractive and repulsive potential fields. The inclusion of these two potential fields ends up with a new potential field which is implemented to control the steering angle of the UGV to reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The results demonstrate the generated path for the UGV can satisfy vehicle dynamics constraints, avoid obstacles and reach the target location.

Optimal path planning for an autonomous articulated vehicle with two trailers

This paper proposed an optimal path planning algorithm for autonomous vehicle with two trailers in autonomous navigation. The proposed algorithm is based on combination of artificial potential field (APF) method and optimal control theory. A linear two-degree-of-freedom vehicle model with both lateral and yaw motion is derived and simulated in MATLAB environment. The optimal control theory is applied to generate an optimal free-obstacle path of the robotic vehicle from a starting point to the goal location. The obstacle-avoidance technique is mathematically modelled using a potential function based on the proposed sigmoid function. The constructed potential field model can achieve an accurate analytic description of objects in three dimensions. Moreover, the proposed model of potential field requires very modest computation at run time. The APF includes both the attractive (the target) and repulsive (the obstacles) potential fields that will control the steering angle of the vehicle so that it can reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The illustrated results demonstrate the generated optimal path of autonomous vehicles with consideration of vehicle dynamics constraints, obstacle avoidance and collision free criteria in reaching the goal location.

Optimal path planning for an autonomous articulated vehicle with two trailers

This paper proposed an optimal path planning algorithm for autonomous vehicle with two trailers in autonomous navigation. The proposed algorithm is based on combination of artificial potential field (APF) method and optimal control theory. A linear two-degree-of-freedom vehicle model with both lateral and yaw motion is derived and simulated in MATLAB environment. The optimal control theory is applied to generate an optimal free-obstacle path of the robotic vehicle from a starting point to the goal location. The obstacle-avoidance technique is mathematically modelled using a potential function based on the proposed sigmoid function. The constructed potential field model can achieve an accurate analytic description of objects in three dimensions. Moreover, the proposed model of potential field requires very modest computation at run time. The APF includes both the attractive (the target) and repulsive (the obstacles) potential fields that will control the steering angle of the vehicle so that it can reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The illustrated results demonstrate the generated optimal path of autonomous vehicles with consideration of vehicle dynamics constraints, obstacle avoidance and collision free criteria in reaching the goal location.

An Obstacle avoidance technique of small UAV (Unmanned Aerial Vehicle) using PSO (Particle Swarm Optimization)

An Obstacle avoidance technique of small UAV (Unmanned Aerial Vehicle) using PSO (Particle Swarm Optimization)

SDRE controller for motion design of cable-suspended robot with uncertainties and moving obstacles

In this paper an optimal control approach for nonlinear dynamical systems was proposed based on State Dependent Riccati Equation (SDRE) and its robustness against uncertainties is shown by simulation results. The proposed method was applied on a spatial six-cable suspended robot, which was designed to carry loads or perform different tasks in huge workspaces. Motion planning for cable-suspended robots in such a big workspace is subjected to uncertainties and obstacles. First, we emphasized the ability of SDRE to construct a systematic basis and efficient design of controller for wide variety of nonlinear dynamical systems. Then we showed how this systematic design improved the robustness of the system and facilitated the integration of motion planning techniques with the controller. In particular, obstacle avoidance technique based on artificial potential field (APF) can be easily combined with SDRE controller with efficient performance. Due to difficulties of exact solution for SDRE, an approximation method was used based on power series expansion. The efficiency and robustness of the SDRE controller was illustrated on a six-cable suspended robot with proper simulations.

PATH PLANNING TECHNIQUE TO ACHIEVE A DYNAMIC TARGET POINT

In this paper a method has been proposed to identify the lowest cost path to reach a destination point from a source point. However, unlike most of the colloquial graph traversal problems, here the destination point is dynamic— not fixed, i.e. changes its position with time. This causes the chaser to update its path planning accordingly, by constantly sensing the position of the destination. During the progression, the chaser may has to side-track many obstacles, for which, in addition to the algorithm for reaching dynamic target through optimal path, two techniques have also been proposed for avoiding obstacles. The proposed method has been compared with the existing techniques for reaching dynamic target such as D*, D* Lite etc. and also with the existing obstacle avoidance techniques such as Bug, NHNA etc., mainly used in Robotics.

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor actuator control techniques The applications of the autonomous mobile robot in many fields such as industry space defence and transportation and other social sectors are growing day by day The mobile robot performs many tasks such as rescue operation patrolling disaster relief planetary exploration and material handling etc Therefore an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance The present article focuses on the study of the intelligent navigation techniques which are capable of navigating a mobile robot autonomously in static as well as dynamic environments

Robust Navigation and Seamless Localization for Carlike Robots in Indoor-outdoor Environments

This paper describes a navigation and seamless localization system that permits carlike robots to move safely in heterogeneous scenarios within indoor and outdoor environments. The robot localization integrates different sensor (GPS, odometry, laser rangefinders) information depending on the kind of area (indoors, outdoors, and areas between) or on the sensor uncertainty in such a way that there are no discontinuities in the localization, and a bounded uncertainty is constantly maintained. Transitions through indoor and outdoor environments are thoroughly considered to assure a smooth change in-between. The paper addresses a navigation technique that combines two well-known obstacle avoidance techniques, namely the nearness diagram and the dynamic window approaches, exploiting the advantages and properties of both, and integrating the seamless localization technique. The navigation technique is developed for carlike robots by considering their shape and kinodynamic constraints, and the restrictions imposed by the environment. Forward-backward maneuvers are also integrated in the method, allowing difficult situations in dense scenarios to be managed. The whole system has been tested in simulations and experiments in real large-scale scenarios.

Modeling decision and game theory based pedestrian velocity vector decisions with interacting individuals

This paper explores the incorporation of decision and game theory in the velocity vector decisions found in human movement. These decisions are made by individuals when maneuvering in an environment occupied by other people or obstacles. The model developed in this paper examines how human movement can be captured as a result of a decision-making process rather than modeling the behavior directly. In this paper, a novel utility function is formed from the biological-based preference of minimizing energy consumption. This energy function is partially fashioned from the energies due to moving, colliding, and waiting. Time and risk preferences are incorporated into the utility function to uniquely capture human desires. Decision and game theories are used to model the decision making process of individuals when alone and when interacting with a range of evacuee types (panicked, distracted and alert). Beliefs are formed and updated concerning both the environment and individuals in the environment, to truly capture the knowledge of individual evacuees. Assumptions that were made during the creation of this novel decision-focused movement model are discussed throughout the paper. This work expands upon previous research performed using the obstacle avoidance technique known as the velocity obstacle method. Common human traits and tendencies are proven to exist in the model through multiple examples. Future modifications of this alternative approach to traditional behavior-focused movement models are discussed.

Path planning with obstacle avoidance by [formula omitted] PH quintic splines

We propose a two-step approach for the construction of planar smooth collision-free navigation paths. Obstacle avoidance techniques that rely on classical data structures are initially considered for the identification of piecewise linear paths having no intersection with the obstacles of a given scenario. Variations of the shortest piecewise linear path with angle-based criteria are proposed and discussed. In the second part of the scheme we rely on spline interpolation algorithms with tension parameters to provide a smooth planar control strategy. In particular, we consider the class of curves with Pythagorean structures, because they provide an exact computation of fundamental geometric quantities. A selection of test cases demonstrates the quality of the new motion planning scheme.

Visual Tracking in Unknown Environments Using Fuzzy Logic and Dead Reckoning

This paper addresses the problem of making a non-holonomic wheeled mobile robot (WMR) move to a target object using computer vision and obstacle-avoidance techniques. If a priori information about the obstacles is available, pre-planning the desired path can be a good candidate method. However, in so many cases, obstacles are dynamic. Therefore, our first challenge is to make the WMR move to a desired target while autonomously avoiding any obstacle along its path. The second challenge deals with visual-tracking loss; that is, when the target is lost from the camera scope, the robot should use Dead Reckoning (DR) to get back on its path towards the target. The Visual Tracking (VT) algorithm then takes the relay to reach the final destination, compensating for any errors due to DR by calculating the distance to the target when it is within the scope of the camera. The proposed system also uses two fuzzy-logic controllers; the first controller avoids objects while the second manages the path to the target. Different complex scenarios have been implemented, showing the validity of our multi-controller model.

Obstacle Avoidance Method for a Group of Humanoids Inspired by Social Force Model

This paper presents a new formulation for obstacle and collision behavior on a group of humanoid robots that adopts walking behavior of pedestrian crowd. A pedestrian receives position information from the other pedestrians, calculate his movement and then continuing his objective. This capability is defined as socio-dynamic capability of a pedestrian. Pedestrian’s walking behavior in a crowd is an example of a sociodynamics system and known as Social Force Model (SFM). This research is trying to implement the avoidance terms in SFM into robot’s behavior. The aim of the integration of SFM into robot’s behavior is to increase robot’s ability to maintain its safety by avoiding the obstacles and collision with the other robots. The attractive feature of the proposed algorithm is the fact that the behavior of the humanoids will imitate the human’s behavior while avoiding the obstacle. The proposed algorithm combines formation control using Consensus Algorithm (CA) with collision and obstacle avoidance technique using SFM. Simulation and experiment results show the effectiveness of the proposed algorithm.

Localization and Path Planning for an Autonomous Mobile Robot Equipped with Sonar Sensor

In the field of mobile robotics, the process of robot localization and global trajectory planning in robot operating scenes, that are completely or partially known, represents one of the main issues that are essential for providing the desired robot functionality. This paper introduces the basic elements of path planning for an autonomous mobile robot equipped with sonar sensors, operating in a static environment. The path planning process is initially performed by using a known map. Next, the sonar sensors are used to localize the robot, based on obstacle avoidance techniques. The effectiveness and efficiency of the algorithm proposed in this paper is demonstrated by the simulation results.