IEEE International Conference on Robotics and Biomimetics

A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains

Gait planning for the humanoid robot is a very essential and basic requirement. The humanoid robot is balanced at two feet; therefore, special attention is required for gait analysis for the execution of assigned tasks. In this paper, the linear inverted pendulum (LIPM) model is considered to simplify the study and to obtain better gait planning of humanoid robot NAO. Center of mass (COM) and zero moment point (ZMP) criterion are applied with the LIPM model for a better understanding of selecting the step length and period. In addition, a PSO (particle swarm optimization) tuned PID (proportional-integral-derivative) controller has been implemented. Sensory data such as the location of obstacles and the target along with the desired trajectory aided inverse kinematics have been embedded to the conventional PID controller, which provides an interim angle to start the navigation. This interim angle has been carried forward to the PSO technique accompanied by the desired trajectory. It tunes the parameters of the conventional PID controller and provides an optimum turning angle, which avoids obstacles and increases the stabilization of the robot while crossing it. It reduces travel time and shortens travel length. PSO technique minimizes the computational complexity and number of iteration because it requires fewer tuning parameters. Simulations are executed on the simulated NAO robot for the conventional PID controller and the proposed controller. To ratify its findings, experiments are carried out on a real NAO robot in laboratory conditions for both the conventional PID controller and the proposed controller. Simulation and experimental results are presenting a good agreement among each other with deviation under 6%. Applying the PSO tuned PID controller provides a predictable gait and reduces the stabilization time and essentially eliminating the overshoot by 25%. A comparative study with various controllers is performed, and the credibility of the evaluated result has been examined using statistical analysis. The proposed controller has been compared with a previously developed technique to ensure its robustness.

Particle Swarm Optimization aided PID gait controller design for a humanoid robot.

Hybrid Global Positioning System-Adaptive Neuro-Fuzzy Inference System based autonomous mobile robot navigation

The prime challenge in a humanoid robot is its stability on two feet due to the presence of an underactuated system. In this paper, the complete dynamics of the humanoid robot has been described in...

Dynamic Stabilization of NAO Humanoid Robot Based on Whole-Body Control with Simulated Annealing

Purpose




This paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.




Design/methodology/approach




The three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.




Findings




Graphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.




Originality/value




This paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

Motion planning is one of the most important parts when we design any wheeled or walking robots. The meaning of motion planning is to plan a path from start point to goal point between obstacles in any working environment. Many techniques have applied by researchers from last two-three decades for wheeled robot motion planning. This article presents a critical review on motion planning of wheeled robot by using different nature-inspired techniques like evolutionary algorithm and swarm-based optimization algorithm.

/pdf/different-nature-inspired-techniques-applied-for-motion-22mbhejdew.pdf

Abhishek Kumar Kashyap

Papers

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains

Particle Swarm Optimization aided PID gait controller design for a humanoid robot.

Dynamic Stabilization of NAO Humanoid Robot Based on Whole-Body Control with Simulated Annealing

Different Nature-Inspired Techniques Applied for Motion Planning of Wheeled Robot: A Critical Review