Aided Navigation Techniques for Indoor and Outdoor Unmanned Vehicles

Gurkan Tuna Dept. of Computer Programming

Trakya University Edirne, Turkey

gurkantuna@trakya.edu.tr

Kayhan Gulez Dept. of Control and Automation Engineering

Yildiz Technical University Istanbul, Turkey

gulez@yildiz.edu.tr

Abstract—This paper presents design considerations of indoor and outdoor navigation techniques proposed for unmanned vehicles (UV). In this paper, we mainly investigate the use and the advantages of wireless sensor networks (WSNs) for indoor navigation, and Global Positioning System (GPS) for outdoor navigation. The system primarily uses laser range finder (LRF) measurements for indoor navigation, and is based on Extended Kalman Filter (EKF). For outdoor navigation the system uses Inertial Navigation System (INS) measurements. At periodical intervals the system integrates the measurements of an absolute sensor to improve estimations. The absolute sensor is a WSN interface for indoor navigation, and a GPS receiver for outdoor navigation. Simulation studies were conducted using Unified System for Automation and Robot Simulation (USARSim) and Player/Stage. The results of USARSim based simulations prove the advantages of integrating GPS measurements. Player/Stage based simulations show the advantages of integrating Received Signal Strength Indicator (RSSI) measurements obtained from WSN interfaces. In addition to the simulation studies, field tests with a custom-built Corobot autonomous robot platform will be realized to prove the effectiveness of the methods.

Keywords-aided navigation; global positioning system; wireless sensor networks; USARSim; player/stage

I. INTRODUCTION Unmanned vehicles (UVs) are used to aid humans in

performing military and non-military tasks such as search and rescue, planetary exploration, reconnaissance, etc. UVs can be used for different purposes such as taking a passenger to a specific address, delivering some materials in dangerous environments, observing some particular places, etc. The main requirements of these envisioned tasks are localization and navigation processes. The accuracy of these processes mainly depends on the algorithm and the sensors used for perception by UVs [1]. Sensors provide several measurements including the information such as direction, speed, and position [1].

For outdoor localization and navigation the use of Global Positioning System (GPS) receivers and Inertial Navigation System (INS) units are very common in manned/unmanned vehicles. Typical unmanned vehicle applications require high reliability, low cost, and sufficient accuracy under all conditions but this is not possible with using a GPS receiver alone for driving due to frequent GPS blockages and

multipaths [2]. Another problem with using GPS receivers for localization and navigation tasks is that they require a clear view of the sky to function properly. In this study for outdoor navigation of UVs we mainly propose an INS based navigation system. The navigation system runs an Extended Kalman Filter (EKF) based algorithm and primarily uses the INS. Since it is not mainly depended on GPS, it can function in both rural and urban areas where GPS signals cannot be received effectively. GPS measurements are fused to the system at periodical intervals.

For indoor localization and navigation tasks Laser Range Finders (LRFs) are very common. They are not too expensive and provide highly accurate positioning in most cases. Typical indoor environments include several objects which can be used during mapping, localization, and navigation. Hence, LRFs are suitable sensors for indoor environments. In this study for indoor navigation of UVs we mainly propose an EKF based navigation system, and primarily uses LRF measurements. Received Signal Strength Indicator (RSSI) measurements obtained from wireless sensor network (WSN) interfaces are fused to the system at predetermined intervals. The problem with this method is that RSSI measurements are very noisy, especially in an indoor environment due to interference and reflections of signals [3], [4]. Since the proposed system is not mainly depended on RSSI measurements, it can function in indoor environments.

The paper is organized as follows. The details of the proposed indoor and outdoor navigation systems are explained in Section II. Related simulation studies are explained in Section III. Conclusions of the paper and future work are given in Section IV.

II. INDOOR AND OUTDOOR NAVIGATION SYSTEMS Both indoor and outdoor aided navigation systems

proposed in this paper are based on EKF. EKF consists of five steps. These steps are state prediction, observation, measurement prediction, matching and estimation [5], [6]. EKF uses a landmark based map and operates recursively in two stages: Prediction and Update. In the prediction stage, a control command u(k) and vehicle motion model are utilized to estimate the vehicle’s location. Then, in the update stage, to update the landmark’s position and to refine the estimation of

This research has been supported by Yildiz Technical UniversityScientific Research Projects Coordination Department. Project Number: 2010-04-02-ODAP01

978-1-4673-0229-6/12/$31.00 ©2012 IEEE

the vehicle’s location, new observation z(k) from an exteroceptive sensor is used [5].

Navigation solutions can be classified based on accuracy, availability, and cost [9]. Navigation solutions can be classified into two categories: absolute, and relative. Absolute navigation is performed with respect to a reference point and a map, on the other hand relative navigation only focuses on topology discovery.

Though for indoor navigation some type of sensors such as LRFs provide accurate localization and navigation in most cases, the use of exterior tools may help improve positional errors, and increase accuracy. Using exterior positioning tools may also prevent large drifts from the ground truth, kidnapping, and loop closure errors. Similar case exists for outdoor navigation where instead of LRFs INSs are preferred due to the scanning range limitations of LRFs. If an UV drives a long way without receiving an observation from an exteroceptive sensor and depends highly on dead reckoning, then it drifts from the ground truth.

A. Aided Navigation System for Outdoor Unmanned Vehicles Aided navigation system proposed for outdoor unmanned

vehicles uses GPS and INS measurements. The reason for integrating GPS and INS measurements instead of using GPS measurements alone is that despite their advantages, GPS receivers cannot be used alone for navigation due to their low sample rates and the requirement of having an open view of at least four GPS satellites. GPS receivers provide absolute information of position and speed, and do not need information about their previous states to produce a navigation solution since the position GPS satellites are known.

The INS keeps the track to the actual position, velocity and attitude with the aid of the GPS receiver. A simple model of the proposed system is shown in Fig. 1. System state information of the UV is available to the filter. In this model the difference between the INS and the GPS is calculated and delivered to the filter. Also, by feeding back the estimated errors to the INS, the possibility of the growth of the observed error is prevented.

Figure 1. The proposed system uses the INS as the primary sensor to predict the UV’s states. When an observation from the absolute sensor arrives, the

filter updates the predicted states.

B. Aided Navigation System for Indoor Unmanned Vehicles Aided navigation system proposed for indoor unmanned

vehicles uses LRF measurements. The proposed system uses

the LRF as the primary sensor to predict the UV’s states. When an RSSI measurement arrives, the filter updates the predicted states. RSSI measurements are based on the relationship between transmission distances and signal attenuation [3]. The RSSI path loss measurements are given in decibels. UVs estimate the distances to nearby wireless sensor nodes by measuring the RSSI of the received radio messages [4]. The problem with this method is that especially in indoor environments due to interferences and reflections of signals RSSI measurements are very noisy. In this study we use RSSI measurements only to update position estimations. Since the locations of wireless sensors are exactly known and the UV starts at a known position, then at predetermined intervals RSSI measurements are received by the UV and position estimations are updated. For positioning in an environment where the locations of wireless sensor nodes are exactly known, trilateration method is employed. Three or more independent distance measurements with respect to beacon nodes are used to solve a 3D trilateration problem [9]. If these beacons reside at a known location such as the one in the proposed system, then the absolute position can be given in reference to this inertial system. RSSI path loss measurements are in decibels but they can be converted to values of distances by using (1) [7].

P 10n log r-A= − (1)

where n represents signal propagation constant, r represents distance from sender, and A represents received signal strength at a distance of one meter. Details of this method can be found in [7], [8]. The accuracy of the trilateration method depends on the configuration of wireless sensor nodes, the geometry of the position references, and the accuracy of the range measurements.

III. SIMULATION STUDIES We performed simulation studies of the proposed outdoor

navigation system in USARSim [10]. We developed a custom simulation environment using USARSim and Visual C++ 2010. USARSim is a simulation of robots and environments, and is based on the Unreal Tournament (UT) game engine [10], [11]. The Unreal Engine (UE) loads a set of geometrical models which describe all the objects in the environment. USARSim object models can be created with the Unreal Editor (UnrealEd) or existing models can be imported [11]. Users can connect to USARSim with the UT client or can start their own controller application to control robots inside the simulated environment. We developed a control application to interface and to control the robot in USARSim. We used a part of an external library designed for GPS and INS integration [13]. Communication with the USARSim server was realized using port 3000. A callback function was used to handle the received data from USARSim. We used INS.uc, USARUtils.uc, GPSSensor.uc class files. We simulated a scenario which includes a Pioneer P2AT robot as shown in Fig. 2. Since the outdoor navigation method is based on using GPS and INS measurements to navigate the P2AT, we placed a GPS receiver on the robot in addition to default sensors.

We performed the simulation studies of the proposed indoor navigation system in Player/Stage [12]. The Player/Stage project is an advanced robotics simulation and interface platform. The Player/Stage allows using the Player server to control either a physical robot or a simulated robot in Stage. We simulated a scenario which includes a Pioneer P2DX robot equipped with a mica2 wireless sensor node and six mica2 wireless sensor nodes distributed in the environment. Pioneer P2DX also has a Sick laser scanner and a front sonar ring as exteroceptive sensors. The wireless sensor node positioned on the Pioneer robot receives data packets from the motes located one-hop away. The simulate scenario is shown in Fig. 3. The positions of wireless sensor nodes are as follows:

node_id:0 position=-5, 7 node_id:1 position=-2, 1 node_id:2 position=6, 5 node_id:3 position=-7, -4 node_id:4 position=0, 7 node_id:5 position=6, -7

Figure 2. The Simulated P2AT in USARSim.

Figure 3. The simulated P2DX running in the Stage world.

To control the Pioneer robot, we developed a control program using Visual C++ 2010 and Player C++ libraries. Player C++ libraries handle all the socket and data-packing details and makes getting at the sensor data and sending actuator commands easier. Player server runs at port 6665. Source codes of the simulation application are available upon request.

During simulations of the proposed outdoor and indoor navigation methods we calculated positional errors of the robots from the real trajectory. For outdoor navigation, positional errors (in longitude and latitude) are shown in Fig. 4 and Fig. 5. For indoor navigation, positional errors (in longitude and latitude) are shown in Fig. 6 and Fig. 7. Only the first 250 seconds of the simulations are shown in the figures. When calculating the positional errors, we compared the filter outputs with the ground truth values obtained from USARSim and Player/Stage.

Considering the results of the USARSim and Player/Stage based simulations we can conclude that an UV can follow a predetermined trajectory with minor positional errors using the proposed systems. For outdoor navigation trajectory errors of the simulated UV are less than 0.4 m in longitude and latitude, which means that the UV can navigate successfully in outdoor environments. Similar case exists for indoor navigation. Trajectory errors in this case are less than or around 0.2 m.

Figure 4. Outdoor Trajectory Errors - Longitude (in meters).

Figure 5. Outdoor Trajectory Errors –Latitude (in meters).

Figure 6. Indoor Trajectory Errors - Longitude (in meters).

Figure 7. Indoor Trajectory Errors –Latitude (in meters).

In addition to the simulations, we are planning to realize field tests with a Corobot autonomous robot platform shown in Fig. 8 [14]. Corobot is an autonomous mobile robot with an onboard PC, and runs Robot Operating System (ROS) [15] on Ubuntu. To comply with the requirements of our field tests, we made some modifications on the robot. Our field tests are in progress and have not been completed yet.

Figure 8. Corobot autonomous robot platform.

IV. CONCLUSION In this paper, two different navigation systems are

proposed. For outdoor navigation an INS and GPS integrated system is proposed. This system runs an EKF based algorithm and uses INS measurements. At periodical intervals, it fuses GPS measurements to improve estimations. For indoor navigation an EKF based navigation system is proposed. The system primarily uses LRF range measurements and at

predetermined intervals RSSI measurements are fused. Experimental studies were conducted to prove the methods’ effectiveness. The results of simulation studies show that for fusing information from an external absolute sensor improves the accuracy of navigation both outdoor and indoor environments. As a future work, a field test with Corobot mobile robots will be implemented to show the effectiveness of the proposed methods.

REFERENCES

[1] E. M. Nebot, “Sensors used for autonomous navigation,” chap. 7 in Advances in Intelligent Autonomous Systems, Kluwer, pp. 135-156, 2000.

[2] J. Huang, H.-S. Tan, “A Low-Order DGPS-Based Vehicle Positioning System Under Urban Environment,” IEEE Transactions on Mechatronics, vol. 11, no. 5, pp. 567-575, 2006.

[3] L. Yangming, M. Q.-H. Meng, L. Huawei, L. Shuai, C. Wanming, “Particle filtering for WSN aided SLAM,” in Proc of IEEE/ASME AIM2008, 2008, pp. 740-745.

[4] G. Tuna, K. Gulez, V. C. Gungor, “Communication Related Design Considerations of WSN-aided Multi-Robot SLAM,” in Proceedings of the 2011 IEEE International Conference on Mechatronics (ICM 2011), Istanbul, Turkey, 2011, pp. 493-498.

[5] G. Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, M. Csorba, “A Solution to the Simultaneous Localization and Map Building (SLAM) Problem,” IEEE Transactions on Robotics and Automation, vol. 17, no. 3, pp. 229-241, 2001.

[6] S. B. Williams, Efficient Solutions to Autonomous Mapping and Navigation Problems, Ph.D. Dissertation, University of Sydney, 2001.

[7] J.-E. Berg, “Building Penetration Loss along Urban Street Microcells,” in Proc. of Personal, Indoor and Mobile Radio Communication Conf. (PIMRC), 1996, pp. 795-797.

[8] S. Hara, Z. Dapeng, K. Yanagihara, J. Taketsugu, K. Fukui, S. Fukunaga, K. Kitayama, ”Propagation characteristics of IEEE 802.15.4 radio signal and their application for location estimation,’’ in Proc. of Vehicular Technology Conference, 2005, pp. 97-101.

[9] C. Savarese, J. M. Rabaey, J. Beutel, “Locationing in Distributed Ad-Hoc Wireless Sensor Networks,” in Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001, pp. 203-2040.

[10] S. Carpin, M. Lewis, J. Wang, S. Balakirsky, C. Scrapper, “USARSim: a robot simulator for research and education,” in Proceedings of the 2007 IEEE, 2007.

[11] (2011) USARSim. http://sourceforge.net/apps/mediawiki/usarsim/index.php?title=Introduction

[12] (2011) Player Project. http://playerstage.sourceforge.net/ [13] (2011) https://bitbucket.org/jbrandmeyer/libeknav/wiki/Home [14] (2011) Corobot. http://robotics.coroware.com/ [15] (2011) ROS. http://www.ros.org/wiki/