Robustness and Controllability Analysis for Autonomous Navigation of Two-Wheeled Mobile Robots

2007 ◽  
Author(s):  
Alessio Salerno ◽  
Jorge Angeles
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Upper Bounds ◽  
Small Time ◽  
Dynamics Model ◽  
Wheeled Mobile Robots ◽  
Rank Condition ◽  
Dynamics Response ◽  
Kalman Rank Condition

This work deals with the robustness and controllability analysis for autonomous navigation of two-wheeled mobile robots. The analysis of controllability of the systems at hand is conducted using both the Kalman rank condition for controllability and the Lie Algebra rank condition. We show that the robots targeted in this work can be controlled using a model for autonomous navigation by means of their dynamics model: kinematics will not be sufficient to completely control these underactuated systems. After having proven that these autonomous robots are small-time locally controllable from every equilibrium point and locally accessible from the remaining points, the uncertainty is modeled resorting to a multiplicative approach. The dynamics response of these robots is analyzed in the frequency domain. Upper bounds for the complex uncertainty are established.

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

Agriculture ◽  
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.

Visual Navigation of Wheeled Mobile Robots Using Deep Reinforcement Learning: Simulation to Real-Time Implementation

2020 ◽  
Author(s):  
Ezebuugo Nwaonumah ◽  
Biswanath Samanta
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Real Life ◽  
Visual Navigation ◽  
Wheeled Mobile Robots ◽  
Depth Images ◽  
Gradient Based ◽  
Simulation Environments

Abstract A study is presented on applying deep reinforcement learning (DRL) for visual navigation of wheeled mobile robots (WMR), both in simulation and real-time implementation under dynamic and unknown environments. The policy gradient based asynchronous advantage actor critic (A3C), has been considered. RGB (red, green and blue) and depth images have been used as inputs in implementation of A3C algorithm to generate control commands for autonomous navigation of WMR. The initial A3C network was generated and trained progressively in OpenAI Gym Gazebo based simulation environments within robot operating system (ROS) framework for a popular target WMR, Kobuki TurtleBot2. A pre-trained deep neural network ResNet50 was used after further training with regrouped objects commonly found in laboratory setting for target-driven visual navigation of Turlebot2 through DRL. The performance of A3C with multiple computation threads (4, 6, and 8) was simulated and compared in three simulation environments. The performance of A3C improved with number of threads. The trained model of A3C with 8 threads was implemented with online learning using Nvidia Jetson TX2 on-board Turtlebot2 for mapless navigation in different real-life environments. Details of the methodology, results of simulation and real-time implementation through transfer learning are presented along with recommendations for future work.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

Sign in / Sign up

Export Citation Format

Share Document