=Paper=
{{Paper
|id=Vol-2236/paper-09-009
|storemode=property
|title=
Implementation of the Pathfinding System for Autonomous Navigation of Mobile Ground Robot
|pdfUrl=https://ceur-ws.org/Vol-2236/paper-09-009.pdf
|volume=Vol-2236
|authors=Andrey V. Bokovoy,Maxim B. Fomin,Konstantin S. Yakovlev
}}
==
Implementation of the Pathfinding System for Autonomous Navigation of Mobile Ground Robot
==
https://ceur-ws.org/Vol-2236/paper-09-009.pdf
72
UDC 004.4, 004.932.7, 004.021
Implementation of the Pathfinding System for Autonomous
Navigation of Mobile Ground Robot
Andrey V. Bokovoy*† , Maxim B. Fomin* , Konstantin S. Yakovlev*‡
*
Department of Information Technologies,
Peoples’ Friendship University of Russia (RUDN University),
Miklukho-Maklaya str. 6, Moscow, 117198, Russia
†
Institute for Problems of Artificial Intelligence,
Federal Research Center "Computer Science and Control" of Russian Academy of Sciences,
Vavilova str. 44/2, Moscow, 119333, Russia
‡
Moscow Institute of Physics and Technology,
9 Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russia
Email: 1042160097@rudn.university, fomin_mb@rudn.university, yakovlev@isa.ru
The paper considers the problem of autonomous navigation of unmanned ground vehicle
and the way to solve it by using simultaneous localization and mapping methods based on the
data, provided by the laser rangefinder, and path planning algorithms. We propose the control
system’s architecture (including low-level communication protocols and high-level planning
and mapping mechanisms) and it’s implementation based on Robot Operating System (ROS)
framework. The system is planned to use as a toolbox for pathplanning algorithms evaluation
on real robotic system in real environment. We also provide the visualization of current state
of system. The evaluation is carried out on a Nexus wheeled robot, which specification is
also given. Future work includes multi-robot modification of developed system (shared map
over all users of the system), exploration algorithms implementation (including multi-agent
exploration), multi-agent pathplanning algorithms embedding and moving the whole system’s
operation on board of mobile ground robotic system.
Key words and phrases: ground unmanned vehicle, mobile robot, localization and
mapping, laser rangefinder, path planning.
Copyright © 2018 for the individual papers by the papers’ authors. Copying permitted for private
and academic purposes. This volume is published and copyrighted by its editors.
In: K. E. Samouylov, L. A. Sevastianov, D. S. Kulyabov (eds.): Selected Papers of the 1st Workshop
(Summer Session) in the framework of the Conference “Information and Telecommunication
Technologies and Mathematical Modeling of High-Tech Systems”, Tampere, Finland, 20–23 August,
2018, published at http://ceur-ws.org
Bokovoy Andrey V., Fomin Maxim B., Yakovlev Konstantin S. 73
1. Introduction
Nowadays unmanned ground vehicles (mobile robots) are widely used for academic
and practical purposes. The application area of such robots varies from being evaluation
testbeds for methods and algorithms developed in laboratory environment to real world
applications like search-and-rescue, monitoring, guarding etc. Increasing the autonomy
is one of the core tasks in modern robotics [1, 2] as, obviously, autonomous mobile
robots have more capacities to solve the tasks than remotely-operated robots, especially
when the large groups and coalitions of robotic systems are involved [3–5]. The ability
for self-navigation (e.g. without external control by a human) in known or unknown
environment is the basic block needed to achieve high level of robot’s autonomy [6].
Different approaches to solve the navigation problem exist. Applicability of different
approaches depends on the properties of the robot’s environment (known or unknown
environment, availability of global position systems and etc.) and on-board sensors.
Modern navigation methods can be split to reactive and deliberative. Reactive
methods operate by handling "at-the-moment" sensors’ information and perform move-
ment based on the current state of the system and the surrounding environment. This
approach is mainly used for navigation in dynamically changing environment, e.g. for
obstacle-avoiding tasks, path following etc. This approach is also useful when the time
horizon is short and decisions should be made very quickly. Deliberative methods
assume that the robot posses some information about the environment (e.g. it has the
map) and performs the navigation taking into account this information. This class
is represented by path planning algorithms, simultaneous localization and mapping
(SLAM) algorithms etc. We follow the deliberative approach in this work.
The main tasks we consider are mapping of unknown environment, localization in
resultant (or known apriori) map and path planning. We follow the typical approach
when localization and mapping are combined into the coherent SLAM, e.g. simultaneous
localization and mapping, framework [7–10]. A decision on how to solve a SLAM
problem, e.g. which method to use, depends vastly on the type of on-board sensors
mobile robot carries. In our case we rely on the scanning laser rangefinder (lidar) and
inertial measurement unit, and utilize the SLAM method, that integrates the information
from these sensors. The output of the SLAM algorithm is the 2-D map (occupancy grid)
of the environment with the blocked and free areas pointed out. Having such a map
well-established heuristic search algorithms like A* and others [11, 12] can be utilized to
solve path planning queries.
The main goal of this work is to investigate the ways of creating a coherent, mod-
ularized software control system used for autonomous navigation of a wheeled robot
having SLAM and pathfinding as the main components that can be plugged in and out
for evaluating different approaches and methods.
2. Specification of navigation system
2.1. Hardware and software organization of ground robot
As the experimental platform for developing the navigation system, we use ground
unmanned vehicle, that is based on Nexus platform as shown in Fig. 1.
The hardware specification of robotic platform:
– Chassis with kinematic differential schema;
– Driving wheel drive with reducer and encoder;
– Chassis controller;
– Battery;
– On-board embedded controller ODROID-C2 [13]:
– ARM architecture;
– 4 cores;
– 2 GHz;
– 2 GB RAM;
74 ITTMM-WSS—2018
Figure 1. Ground research platform
– 8 GB flash memory.
– Web-camera;
– 6-DOF manipulator;
– Scanning lidar;
– Programmable servo controller;
– Wireless router.
The on-board computer is powerful enough to run Linux-based operating system
and is capable of processing complex algorithm in real-time. As the main framework
for interfacing individual components (sensors, controllers and etc.) and autonomous
control we chose Robot Operating System (ROS) [14].
Using ROS for system’s organization makes low-level robot control (servomotors
signal control, sensor access etc.) more abstract for end-user through standard high-
level protocol called topics and services. Also, ROS makes applications, built for this
framework, modular by executing different parts independently in nodes.
The communication with ground platform is done using Wi-Fi, making possible to
control robot remotely in autonomous, semi-autonomous and manual modes. In case of
fully autonomous control, all the algorithms may be ported directly to ground robot
without additional rework.
Bokovoy Andrey V., Fomin Maxim B., Yakovlev Konstantin S. 75
The on-board computer grants standard interface to the following control mechanisms
and sensors’ data:
– Laser rangefinder (lidar) data in LaserScan format
– Environment map in OccupancyGrid format
– Odometry data in Odom format
– Robot’s position in resultant map in geometry_msgs/PoseStamped format
– Robot movements using control vector with geometry_msgs/Twist commands
– Position following (geometry_msgs/Pose)
The architecture of hardware’ communication is shown in Fig. 2.
Figure 2. Hardware communication architecture
Localization and mapping is done using on-board computer with data from lidar
sensor and odometry. For simultaneous localization and mapping problem we use known
algorithm "gmapping" [15], that uses an array of distances from robot to objects in the
environment and the data from chassis’ sensors to build new map or update an excising
map of unknown environment and localize the robot in this map. Also, the algorithm
makes possible to localize robot in known map.
The on-board computer also implements the action_server and action_client mecha-
nism for known-goal-following tasks. In case of path following algorithm, mobile platform
"Nexus" implements the Pure Pursuit [16, 17] following algorithm.
2.2. Software architecture of autonomous control system
We use C++11 for autonomous control system implementation. Application was
released as a ROS framework node, that uses the standard access interface for system’s
components for normal processing, visualization with RViz [18] package and debugging
(rosbag).
The system consists of 2 main parts:
1. Pathplanner
2. action_client for pathfollowing and building the trajectory
76 ITTMM-WSS—2018
The software architecture of control system is shown in Fig.3. For demonstration
purposes of pathplanning algorithm we use Theta* [19], but the system provides the
mechanism to replace the pathplanning algorithm with other one. For future work,
we plan to experimentally evaluate more complex pathplanning algorithms, including
angle-constrained methods LIAN [20].
Figure 3. Software architecture for autonomous control system
The system can operate in 2 modes: pathplanning in known and unknown environ-
ments. For known environments, unknown parts of map considered as obstacles and
the trajectory planner assumes this cells occupied. For unknown environment, unknown
parts of the map considered as the new type of cells. As the algorithm proceeds, the
system plans the trajectory as if this cells were free. When the system reaches such a
cell, system replans the trajectory using new observations and map.
3. System’s demonstration
Fig. 5 shows the visualization of our control system’s execution using RViz ROS
package. The figure demonstrates the mapping, localization in built map and the
trajectory from robotic system to goal point planned with Theta* algorithm.
As an experemental environment, we used the 6x3.5m room. The map was built
using lidar and odometry data. The size of a single cell is 0.05m (the size is related to
lidar’s error). The trajectory is planned without concidering the robot’s size. The robot’s
position and orientation are corrected during movements using inertial measurement
unit data. If the robot goes far from planned trajectory, then the robot’s position may
be corrected manually.
The pathplannig output then goes to pathfollowing algorithm as an array of mid-
points and the robot proceed towards this points one-by-one. In case of significant
deviation from planned trajectory, the robot attempts to return to the nearest position
of built trajectory. The robot moves with 0.2 m/s speed.
This demonstes the working capacity of our system in real environment using Path-
Planning and SLAM algorithms with "Nexus" robotic platform.
Bokovoy Andrey V., Fomin Maxim B., Yakovlev Konstantin S. 77
Figure 4. Software architecture for autonomous control system
Figure 5. Software architecture for autonomous control system
4. Conclusions
This work presents the specification for unmanned ground robot autonomous control
system based on "Nexus" platform. We described the software architecture and the
mechanism of communication with robotic system. The final system may be executed on
remote control system or on-board of ground robot. Also, the system may be adapted
to any occupancy grid-based pathplanning algorithm.
The results provide us an opportunity to evaluate modern pathplanning algorithms
on real robots.
Acknowledgments
The publication has been prepared with the support of the "RUDN University
Program 5-100" and partially supported by RFBR grant No 17-07-00281.
78 ITTMM-WSS—2018
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