Studies on Simulation and Real Time Implementation of LQG Controller for Autonomous Navigation

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

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COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

Journal of Marine Science and Engineering ◽

10.3390/jmse9040405 ◽

2021 ◽

Vol 9 (4) ◽

pp. 405

Author(s):

Raphael Zaccone

Keyword(s):

Path Planning ◽

Real Time ◽

Collision Avoidance ◽

Autonomous Navigation ◽

Optimal Path ◽

Open Water ◽

Optimal Path Planning ◽

Guidance And Control ◽

Real Time Applications ◽

And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

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Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework

Sensors ◽

10.3390/s21123943 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3943

Author(s):

Nicolas Montés ◽

Francisco Chinesta ◽

Marta C. Mora ◽

Antonio Falcó ◽

Lucia Hilario ◽

...

Keyword(s):

Path Planning ◽

Real Time ◽

Harmonic Functions ◽

Autonomous Navigation ◽

Numerical Technique ◽

Computational Cost ◽

Main Idea ◽

Main Property ◽

Proper Generalized Decomposition ◽

Computational Vademecum

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

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Wall-E Surveyor Robot using Wireless Networks

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v4i2.pp143-149 ◽

2015 ◽

Vol 4 (2) ◽

pp. 143

Author(s):

Aatish Chandak ◽

Arjun Aravind ◽

Nithin Kamath

Keyword(s):

Wireless Networks ◽

Real Time ◽

Real World ◽

Autonomous Navigation ◽

Remote Access ◽

Outdoor Environment ◽

Ultrasonic Sensors ◽

Moving Obstacles ◽

Area Of Interest ◽

World Environment

The methods for autonomous navigation of a robot in a real world environment is an area of interest for current researchers. Although there have been a variety of models developed, there are problems with regards to the integration of sensors for navigation in an outdoor environment like moving obstacles, sensor and component accuracy. This paper details an attempt to develop an autonomous robot prototype using only ultrasonic sensors for sensing the environment and GPS/ GSM and a digital compass for position and localization. An algorithm for the navigation based on reactive behaviour is presented. Once the robot has navigated to its final location based on remote access by the owner, it surveys the geographical region and uploads the real time images to the owner using an API that is developed for the Raspberry PI’s kernel.

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Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽

10.3390/agriculture11100954 ◽

2021 ◽

Vol 11 (10) ◽

pp. 954

Author(s):

Abhijeet Ravankar ◽

Ankit A. Ravankar ◽

Arpit Rawankar ◽

Yohei Hoshino

Keyword(s):

Mobile Robots ◽

Real Time ◽

Collision Avoidance ◽

Autonomous Navigation ◽

Autonomous Robots ◽

Robot Navigation ◽

Indoor Environments ◽

Safe Distance ◽

Field Robots ◽

Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-06-2020-0023 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Chittaranjan Paital ◽

Saroj Kumar ◽

Manoj Kumar Muni ◽

Dayal R. Parhi ◽

Prasant Ranjan Dhal

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Mobile Robots ◽

Real Time ◽

Autonomous Navigation ◽

Simulation Software ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Content Type ◽

Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

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Implementation of FastSLAM2.0 on an Embedded System and HIL Validation using Different Sensors Data

International Journal of Adaptive Resilient and Autonomic Systems ◽

10.4018/ijaras.2015070105 ◽

2015 ◽

Vol 6 (2) ◽

pp. 88-116

Author(s):

Abouzahir Mohamed ◽

Elouardi Abdelhafid ◽

Bouaziz Samir ◽

Latif Rachid ◽

Tajer Abdelouahed

Keyword(s):

Embedded Systems ◽

Embedded System ◽

Particle Filter ◽

Real Time ◽

Autonomous Navigation ◽

Hardware In The Loop ◽

Heterogeneous Architectures ◽

Localization And Mapping ◽

Embedded Applications ◽

Robotic Applications

The improved particle filter based simultaneous localization and mapping (SLAM) has been developed for many robotic applications. The main purpose of this article is to demonstrate that recent heterogeneous architectures can be used to implement the FastSLAM2.0 and can greatly help to design embedded systems based robot applications and autonomous navigation. The algorithm is studied, optimized and evaluated with a real dataset using different sensors data and a hardware in the loop (HIL) method. Authors have implemented the algorithm on a system based embedded applications. Results demonstrate that an optimized FastSLAM2.0 algorithm provides a consistent localization according to a reference. Such systems are suitable for real time SLAM applications.

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