Journal of Robotics

Volume 2018, Article ID 2571243, 7 pages

https://doi.org/10.1155/2018/2571243

## Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm

Hanoi University of Science and Technology, Vietnam

Correspondence should be addressed to Nga Thi-Thuy Vu; nv.ude.tsuh@yuhtihtuv.agn

Received 21 August 2018; Revised 8 November 2018; Accepted 19 November 2018; Published 2 December 2018

Academic Editor: Huosheng Hu

Copyright © 2018 Nga Thi-Thuy Vu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a scheme based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.

#### 1. Introduction

In the construction and mine fields, the excavator which is used to dig and transport of soil or coal is one of the important machines. The work environment of excavator is usually dangerous and harsh. Therefore, developing the automatic excavator system is the general trend. In the unmanual operation system, i.e., excavator system, the trajectory generation for the excavator base and arm is the hot spot because it determines the efficiencies of overall system.

In the real, the excavator arm is a three-degree of freedom (3DOF) manipulator robot. The trajectory planning can be done in both working space and joint space. In the working space, the trajectory is built for end-effectors in three-dimension reference frame so it is quite visual. However, the trajectory built in this space has to face with problems of inverse kinematic and manipulator redundancy [1]. Therefore, in the most case, the trajectory of the manipulator robot is planned in the joint space [2].

In the joint space, the trajectory is planned to meet some specific requirements such as time optimization, energy optimization, jerk optimization, obstacle avoidance, etc. In order to satisfy these conditions, the trajectory is usually predesigned with some via-points then the smooth curve is built using several interpolation such as polynomial, spline, Bezier, etc. In [3, 4] polynomial functions are used to generate the paths for robot arms. Reference [3] proposed a series of polynomials to create desired trajectory for robotic motion via a set of given point; they also addressed a problem of acceleration and jerk optimization. However, the main drawback of [3] is that numbers of parameter proportion to numbers of via-point, which leads to explosion of calculation when the numbers of given point are large. The problems of reducing vibration are solved in [4]; however, the generated trajectory is partial smooth. The Bezier Curve and modifier genetic algorithm are interested in [5] in order to create a path in dynamic field with avoiding obstacle and minimum path’s length.

In recent year, neural networks and fuzzy systems which have ability to approximate functions and fit curves have been widely applied in the path planning field. These algorithms seem to be more flexible and potential than traditional one because the methods based on neural network and fuzzy system can create a path through many via-points without explosion of calculation. In [6–10], the shunting model technique is used to build neural network for path planning problems. In this method, the neural dynamics of each neuron is characterized by a shunting equation or simple additive equation [9]. The trajectories in [6]-[8] are generated for robots to avoid the static obstacles while in [9, 10] robots can work in dynamic environments with moving obstacles. The pulse-couple neural network is used in many application [11] and it is also applied into trajectory generation [12, 13]. This scheme can work in both static and dynamic environments but the complete information about working conditions is necessary. In the field of learning method, fuzzy system also is used to solve the path planning problems [14–17]. In [14] the fuzzy logic based on fuzzy sets algorithm is approached to plan the path for the robotic placement of fabrics on a work table. This fuzzy logic system is developed based on experimental data and it has ability to work with various materials and sizes, while optimal fuzzy scheme is introduced in [15] for path planning of manipulator robots. This is rule-based method which needs specific rules to generate the trajectory for robots and it can deal with moving obstacles. In order to generate a real-time and obstacle avoiding path for cushion robot, a fuzzy system which have capability to transform directly human knowledge in machine is utilized in [16]. Moreover, in [17] the fuzzy logic path planning algorithm is investigated to guarantee the safe motion with obstacle avoidance for mobile robot.

For the excavator, in order to meet the requirement of automatic trend, there are also some researches focusing on path planning topic. In [18, 19], the laser scanner, camera, and sensors are used to build 3D trajectory for automated excavator. This method gives the good result in the clean environment but the reliability of laser scanner and camera will reduce in the dusty environment. In [20], the current position of excavator arm is feedback to control system to predict the trajectory for next cycle. The neural network is used in [21] to determine the characteristic of the soil. From this result, in combination with the reaction force exerted on the bucket, the optimal trajectory is generated for excavator arm. In [22–24], the velocity and acceleration of bucket are used to build the path for excavator arm. The generated trajectory is optimal but velocity and acceleration are difficult to measure.

In this paper, an algorithm based on ANFIS is proposed to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.

#### 2. Path Planning for Excavator Arm Based on ANFIS

##### 2.1. Problem Description

Consider the excavator system as shown in Figure 1. It is assumed that the base is fixed and the arm of excavator operates in the* x*_{0}*O*_{0}*z*_{0} plane.