Human-Manipulator Interface Using Particle Filter

Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 692165, 12 pages http://dx.doi.org/10.1155/2014/692165 Research Article Human-Manipulator Interface Using Particle Filter Guanglong Du, Ping Zhang, and Xueqian Wang South China University of Technology, Higher Education Mega Center, Guangzhou 510006, China Correspondence should be addressed to Ping Zhang; Received 30 September 2013; Accepted 28 December 2013; Published 16 March 2014 Academic Editors: L. Cheng and I. Uzmay Copyright © 2014 Guanglong Du 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. This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. 1. Introduction Human intelligence is required to make a decision and control the robot especially when it is in unstructured dynamic environments. Thus, robot teleoperation is necessary in this situation especially when objects are unfamiliar and shapeless. There are some human-robot interfaces [1] like joysticks [2–4], dials, and robot replicas, and they have been commonly used. However, for completing a teleoperation task, these contacting mechanical devices always require unnatural hand and arm motion. There is another way to communicate complex motions to a remote robot and it is more natural compared with using contacting mechanical devices. This method uses inertial sensors, contacting electromagnetic tracking sensors, gloves instruments with angle sensors, and exoskeleton systems [5] to track the operator hand-arm motion which completes the required task. However, these contacting devices may hinder natural human-limb motion. Because vision-based techniques are noncontacting, they seldom hinder the hand-arm motion. Vision-based methods often use physical markers which are placed on the anatomical body part [6–8]. There are a lot of applications [6, 9, 10] using this marker-based tracking method. However, because body markers may hinder the motion for some highly dexterous tasks, operators may get occluded. Thus, this marker-based tracking method is not always practical. Due to this reason, a markerless approach seems better for many applications. Compared to image-based tracking method which uses markers, markerless method not only is less invasive, but also eliminates problems about marker occlusion and identification [11]. Thus, for remote robot teleoperation, markerless tracking may be a better approach. However, existing markerless human-limb tracking techniques have a lot of limitations so that they may be difficult to use in robot teleoperation applications. Many existing markerless-tracking techniques capture images and then compute the motion later [12–15]. Thus, the robot manipulator can be controlled by continuous robot motion using the markerless tracking method. To allow the human operator to perform hand-arm motions for a task in a natural way without any interruption, the position and orientation of the hand and arm should be provided immediately. Many techniques can only provide 2D image information of the human motion [16, 17]; thus the tracking methods cannot be extended for accurate 3D jointposition data. An end-effector of a remote robot requires the 3D position and orientation information of the operator’s 2 limb-joint centers. How to identify human body parts in different orientations has always been a main challenge [12, 13, 18]. Some limited research towards markerless humantracking has been done for robot teleoperation. Some use a human-robot interface based on hand-gesture recognition to control the robot motion [19–21]. Ionescu et al. [22] developed markerless hand-gesture recognition methods which can be used for mobile robot control and only need a few different commands such as “go,” “stop,” “left,” and “right.” However, for object manipulation in 3D space, it is not possible to achieve natural control and flexible robot motion by only gestures. If an operator wants to use gesture recognition method, he/she needs to think of those limited separate commands which human-robot interface can understand such as move up, down, and forward. A better way of human-robot interaction would be allowing the operator to focus on the complex global task and complete the task naturally when grasping and manipulating objects in 3D space instead of thinking about what type of hand motions is required. To achieve this goal, a method that allows the operator to complete the task using natural hand-arm motions has to provide the robot manipulator with information of the hand-arm motion in real time. The information includes the hand and arm anatomical position and orientation [6, 23]. In [23], operators are required to use bare hands to control the robot and the accuracy is not enough to cope with the high-precision manipulation. Method [6] uses stereo vision to measure the human hand for controlling the robot. But the precision of the stereo vision is not so good and an occlusion is encountered easily. What is more, because method [6] needs the operator to make the big movements to carry out the task, it is time-consuming. The proposed system uses a 3D Camera to locate the hand of the human operator and a camera to measure the distance between the robot tool and the target. This paper proposes Camshift to track the human hand and PF to estimate the position of the hand, as well as an adaptive multispace transformation method to improve the accuracy and efficiency of manipulation. Experimental results to validate the proposed methods are also presented. The remainder of the paper is organized as follows. In Section 2, overview of our paper is presented. The human hand tracking system is then detailed in Section 3. Section 4 describes the position estimation using PF. The met (...truncated)