Neural network-based approaches for mobile robot navigation in static and moving obstacles environments

Mobile robots can travel by acquiring the information using sensor-actuator control techniques from surrounding and perform several tasks. Due to the ability of traversing, mobile robots are used in different application for different places. In the field of robotic research, robot navigation is the fundamental problem and it is easier in static environment than dynamic environment. This paper presents a new method for generating a collision-free, near-optimal path and speed for a mobile robot in a dynamic environment containing moving and static obstacles using artificial neural network. For each robot motion, the workspace is divided into five equal segments. The multilayer perceptron (MLP) neural network is used to choose a collision-free segment and also controls the speed of the robot for each motion. Simulation results show that the method is efficient and gives near-optimal path reaching the target position of the mobile robot.

[1]  Nabil Derbel,et al.  Fuzzy logic controllers design for omnidirectional mobile robot navigation , 2016, Appl. Soft Comput..

[2]  Anish Pandey,et al.  Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm , 2017 .

[3]  Dayal R. Parhi,et al.  Path optimisation of a mobile robot using an artificial neural network controller , 2011, Int. J. Syst. Sci..

[4]  Dayal R. Parhi,et al.  A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System , 2014 .

[5]  Weria Khaksar,et al.  Automatic navigation of mobile robots in unknown environments , 2013, Neural Computing and Applications.

[6]  Danica Janglova,et al.  Neural Networks in Mobile Robot Motion , 2004, ArXiv.

[7]  M. Begnini,et al.  A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results , 2017 .

[8]  Mohamed Jallouli,et al.  Intelligent mobile manipulator navigation using hybrid adaptive-fuzzy controller , 2016, Comput. Electr. Eng..

[9]  Genci Capi,et al.  Neural Network based Guide Robot Navigation: An Evolutionary Approach☆ , 2015 .

[10]  Rached Dhaouadi,et al.  Neural Based Autonomous Navigation of Wheeled Mobile Robots , 2016 .

[11]  Mahdi Fakoor,et al.  Humanoid Robot Path Planning with Fuzzy Markov Decision Processes , 2016 .

[12]  Wen Yu,et al.  Path planning of multi-agent systems in unknown environment with neural kernel smoothing and reinforcement learning , 2017, Neurocomputing.

[13]  Mohammed Faisal,et al.  Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment , 2013 .

[14]  Jiang Wu,et al.  Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field , 2018 .

[15]  François Aioun,et al.  Path planning with fractional potential fields for autonomous vehicles , 2017 .

[16]  Brahim Bouzouia,et al.  Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control , 2017, Robotics Auton. Syst..

[17]  Dayal R. Parhi,et al.  Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system , 2015, Robotics Auton. Syst..

[18]  Mohammed Chadli,et al.  Robust adaptive neural network-based trajectory tracking control approach for nonholonomic electrically driven mobile robots , 2017, Robotics Auton. Syst..

[19]  Pedro U. Lima,et al.  Artificial Intelligence and Systems Theory: Applied to Cooperative Robots , 2004, ArXiv.

[20]  Ngangbam Herojit Singh,et al.  Mobile Robot Navigation Using Fuzzy Logic in Static Environments , 2018 .

[21]  Min Tan,et al.  A Neural Network-Based Camera Calibration Method for Mobile Robot Localization Problems , 2005, ISNN.

[22]  Dayal R. Parhi,et al.  Mobile robot navigation in unknown static environments using ANFIS controller , 2016 .

[23]  Mohamed Slim Masmoudi,et al.  Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation , 2016, Comput. Intell. Neurosci..

[24]  H. D. Taghirad,et al.  A new method for mobile robot navigation in dynamic environment: Escaping algorithm , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[25]  Rahib H. Abiyev,et al.  Fuzzy control of omnidirectional robot , 2017 .

[26]  Mohamed Naceur Abdelkrim,et al.  Path planning of a group of robots with potential field approach: Decentralized architecture , 2017 .

[27]  Kalyanmoy Deb,et al.  A genetic-fuzzy approach for mobile robot navigation among moving obstacles , 1999, Int. J. Approx. Reason..

[28]  Benjamin J. Southwell,et al.  Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor , 2013 .

[29]  Farhad Bayat,et al.  Mobile robots path planning: Electrostatic potential field approach , 2018, Expert Syst. Appl..

[30]  Sunil Kumar Kashyap,et al.  Probabilistic fuzzy controller based robotics path decision theory , 2016 .

[31]  Dayal R. Parhi,et al.  Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone , 2018, Defence Technology.

[32]  Robin De Keyser,et al.  Heuristic approaches in robot path planning: A survey , 2016, Robotics Auton. Syst..

[33]  Hassan Mathkour,et al.  Comparative study of soft computing techniques for mobile robot navigation in an unknown environment , 2015, Comput. Hum. Behav..

[34]  Christian Brecher,et al.  Motion Planning for Industrial Robots using Reinforcement Learning , 2017 .

[35]  Max Q.-H. Meng,et al.  An efficient neural network approach to dynamic robot motion planning , 2000, Neural Networks.

[36]  Guan-Chun Luh,et al.  An immunological approach to mobile robot reactive navigation , 2008, Appl. Soft Comput..