a semi-autonomous tracked robot system for rescue missions

8/12/2019 A Semi-Autonomous Tracked Robot System for Rescue Missions

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A semi-autonomous tracked robot system for rescue missions

Daniele Calisi1, Daniele Nardi1, Kazunori Ohno2 and Satoshi Tadokoro2

1Department of Computer and Systems Science, Sapienza University of Rome, Italy

(E-mail: [email protected])2Graduate School of Information Science, Tohoku University, Sendai, Japan

(E-mail: [email protected])

Abstract: In this paper we describe our work in integrating the results of two previous researches carried on on two

different robots. Both robots are designed to help human operators in rescue missions. In the first case, a wheeled robot

has been used to develop some software modules. These modules provide the capabilities for autonomously explore an

unknown environment and build a metric map while navigating. The second system has been designed to face more

challenging scenarios: the robot has tracks and flippers, and features a semi-autonomous behavior to overcome small

obstacles and avoid rollovers while moving on debris and unstructured ground. We show how the set of software modules

of the first system has been installed on the tracked robot and merged with its semi-autonomous behavior algorithm, and

we discuss the results of this integration with a set of experiments.

Keywords: tracked robot, rescue mission, autonomy

1. INTRODUCTION

In recent years, increasing attention has been devoted

to rescue robotics, both from the research community and

from the rescue operators. Robots can consistently help

humans in dangerous tasks during rescue operations in

several ways.

A rescue scenario is usually unstructured and unstable,

thus requiring the use of complex mechanical and loco-

motive design of the robots involved. On the other hand,

communication unreliability and the difficulty of direct

control of complex robots require some degree of auton-omy.

The robotic system described in this paper is the inte-

gration of two previous works developed in our laborato-

ries:

a wheeled robot system that is able to autonomously

explore a flat environment;

a tracked robot that features a semi-autonomous behav-

ior to overcome small obstacles, debris and stairs.

The software modules of the first system has been in-

stalled on the tracked robot, and merged with its semi-

autonomous behavior. In this way, the robot is able to

autonomously explore more challenging scenarios, with

respect to the wheeled robot. Experiments have been con-ducted on the real tracked robot and on a simulator, and

show that the resulting system is able to explore an un-

known environment and to deal with debris and small ob-

stacles, with a very limited operator support.

2. SYSTEM DESCRIPTION

The whole system can be divided into two conceptual

parts: the hardware part, that comprises the mechanical

design and realization of the robot and the sensors that

have been used, and the software part, that includes the

software implementation of the algorithms. The overall

goal of the system is to show effective autonomous andsemi-autonomous behaviors in a rescue environment. In

particular, we want to provide the robot with:

2D mapping capability;

autonomous and semi-autonomous navigation and ex-

ploration capabilities;

detection of motion hazards and the ability of address

them (we will call this ability semi-autonomous obstacle

overcoming behavior);

communication infrastructure and human-robot inter-

face that make it possible for a human operator to super-

vise the robot and take control of it in critical situations.

2.1 Hardware

The unstructured and unstable environment in a rescuescenario makes the mechanical and locomotive design of

the robot critical. Our robot is a 6-DOF crawler robot,

currently under development, called Aladdin (see Fig-

ure 1). The robot has four flippers connected with the

main body and a system of tracks that covers both the flip-

pers and the main body, allowing for forward/backward

movements and turning. The high mobility of this kind

of crawler robots in complex environments is mainly due

to the moving flippers that allow for multi degree-of-

freedom maneuvers.

A 2D laser scanner (Hokuyo URG-04LX) is mounted

on top of the robot and provides a 2D scan of the sur-rounding. This allows for autonomous mapping and lo-

calization in the environment. The sensors used for the

semi-autonomous obstacle overcoming behavior include:

32 touch sensors mounted inside the main body, a set of

current sensors (URD Corp. HPS-10-AS, used to mea-

sure the torque on the front and rear flippers), and a grav-

ity sensor (Crossbow Corp. CXL04LP3).

Other details of this robot system can be found in [7].

2.2 Software

A set of software modules provides the robot with

the requested autonomous capabilities. Among them,

the most important are: two 2D mapping and localiza-tion modules, a ground contact detection mechanism, a

three-level navigation algorithm, and an autonomous ex-

SICE Annual Conference 2008August 20-22, 2008, The University Electro-Communications, Japan

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Fig. 1 The Aladdin crawler robot

ploration method based on frontiers. A previous ver-

sion of this system, that includes the same exploration

method, the 2D localization and mapping capabilities and

an autonomous navigation module for flat grounds, can

be found in [1]. These modules will be described in de-

tail in the following subsections.

The communication between the robot and the rescue

operators can be extremely unreliable in a rescue sce-

nario. This requires some degree of autonomy for the

robots to accomplish their mission. The exact level of au-

tonomy is decided by many factors such as: the current

communication reliability, the number of robots in the

team, the need of the operator intervention in some crit-

ical situation that the robot is not able to autonomously

deal with. As we will describe in detail in Section 2.3

, the different levels of autonomy are obtained activating

or deactivating some software modules, and giving to the

operator the ability to take more or less control over robot

actions.

2.2.1 Mapping and localization

For mapping and localization purposes, we use the 2D

laser range finder. Some laser beams have been disabled

because they are directed towards the flippers when these

are raised. The localization method is scan-matching

based and does not need odometry data (that, indeed, is

very noisy in tracked robots). The mapping module inte-

grates the current laser scan readings with the map, using

an occupancy grid approach. The localization has been

proven to be robust enough to slight changes in the pitch

or in the roll of the robot pose, and to be able to realign

the robot to the right position in the map when it returnson a horizontal ground. Anyway, during this event, the

resulting map can be damaged. For this reason, when an

excessive roll or pitch is detected (thanks to the gravity

sensor), the mapping module is immediately disabled to

avoid artifact on the map, while the localization module

is kept running.

2.2.2 Exploration

When enabled, this module sends target positions to

the autonomous navigation algorithm. These target posi-

tions lie on the nearest frontier computed on the current

map, i.e., the border between free and unknown space.This method has been proved to be able to guide the robot

to a full exploration of an unknown environment (see [8]).

2.2.3 Navigation

The navigation subsystem has three levels. In the first

level, a topological path is computed on the global 2D

map, in order to guide the next levels and to avoid lo-

cal minima. In the next level, an RRT-based (Rapid-

exploring Random Trees, see [6]) algorithm builds a tree

of plans in order to steer the robot toward the target posi-

tion; each step of each plan takes into account the robot

shape, the collisions with the obstacles, the kinetic (and,

possibly, also the dynamic) constraints of the robot move-

ments. Moreover, the plan building process is interleaved

with the plan execution and correction, the latter being

necessary in order to allow for uncertainty in motion

command effects and new obstacles. Details of these two

levels can be found in [3].

In the third level of the navigation system, a reactive

behavior to overcome obstacles is provided. While the

previous two levels control the speeds of the tracks (i.e.,

steer the robot in 2D), this behavior makes use of a set

of rules and thresholds to decide the position of the flip-pers in order to avoid rollovers and to climb over obsta-

cles. Sensors used for these decisions are the touch sen-

sors (that detect the robot body contact with the ground),

the current sensors (that detect the flipper contact with

some obstacles) and the gravity sensor (that detects roll

and pitch changes). The rules for the front flippers are

shown in Figure 2, an analogous set of rules has been de-

veloped for the rear flippers.

2.3 Autonomy levels and HRI

The system can operate in four different degrees of au-

tonomy and the human operator can switch among them

during the mission:

Full autonomy. In this mode, the exploration module

sends periodically target positions (that lies on a frontier)

to the navigation subsystem. In this case, if the commu-

nication with the operator is working, the system sends

a warning to the operator, but keeps on behaving au-

tonomously.

Partial autonomy. In this case, the exploration mod-

ule is disabled. The operator, through a graphical inter-

face, can send target positions to the robot. The naviga-

tion subsystem autonomously guide the robot to the tar-

gets and builds the map.

Partial tele-operated mode. The exploration is dis-abled as well as the first two modules of the navigation

subsystem. The semi-autonomous obstacle overcoming


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FlipperContact Contact Contact NotContact NotContact

BodyContact RobotPitch Upward Downwar d U pward Down ward

Contact DOWN UP UP UP

NotContact DOWN UP DOWN DOWN

IF (FlipperContact == Contact AND RobotPitch == Upward)

OR (BodyContact == NotContact AND FlipperContact == NotContact)

THEN FrontFlipper = DOWN

ELSE FrontFlipper = UP

Fig. 2 Control rules for the front flipper of the Aladdin robot

behavior is kept running, in this way the operator can de-

cide the speed of the tracks (i.e., the linear speed and turn-

ing speed in 2D), while the module chooses the flipper

positions.

Full tele-operated mode. In this case, the operator

has also full control over the flipper positions. This is

the most difficult and time-consuming method, and in our

experiments has be used only to recover from a flipper

stuck (see below), an operation that usually lasts for a

very short time (less than 5 seconds).

If the communication channel goes down, the robot auto-

matically switches to Full autonomyfrom any other op-

erational mode.

3. EXPERIMENTS

Experiments have been performed both in a real sce-

nario, using the Aladdin robot described before, and us-

ing the USARSim simulator1 [2]. In all experiments, the

robot task is to autonomously explore all the arena; if the

robot is in a critical situation, the operator can intervene

and restore the mission.

Experiments in the real environment have been con-

ducted in a small rescue arena [5] and show the effec-

tive exploration behavior of the robotic system also in

presence of small obstacles and debris. The robot au-

tonomously explores the environment and detects loss of

contact with the ground or other kind of mobility haz-

ards, being able to address them. In Figure 3 we show a

map built during a mission: in the position marked with

1, the robot starts its exploration; the small purple box

labeled with 2 is the mark put by the hazard detection

module (it is the place where contact with the ground is

detected; in that place, a 20 cm wooden bar, not visible

with the laser scanner, lays on the ground); finally, the

robot last position is marked with 3.Another set of experiments have been performed us-

ing the USARSim simulator and a simulated model of the

robot that is similar to Aladdin (i.e., a tracked robot with

four indipendent flippers). A rescue arena has been built

inside the simulated world and contains obstacles and de-

bris that the laser scanner cannot detect (see Figure 5).

The arena is 7x5 m2.

Figure 4 shows the results of the experiments con-

ducted in the simulated arena. In only one case the robot

flipper got blocked in one of the wood frames and the op-

erator intervention could not restore the robot functional-

ity in order to continue the mission. Figure 6 show the

resulting map of one of the missions. On the left side of

1http://usarsim.sf.net

Fig. 3 The map built during the real experiment: 1)

robot start position, 2) the obstacle detected using

touch/torque sensors, 3) robot final position

Fig. 5 The simulated scenario used in the experiments.

the figure, a partial map is shown: purple bars show de-

tected obstacles that lie outside of the 2D laser scannerdetection plane, a green cross shows the current target of

the robot navigation subsystem (the target is on a fron-

tier) and a tree of plans is shown starting from the robot

position. On the right side of the figure, the full map of

the environment is shown, the places marked with op

are where the operator intervention has been necessary

for the robot to continue the mission.

4. CONCLUSION AND FUTURE WORK

The robot system presented in this paper has been

proven to be effective in scenarios where small obstacles

and mobility hazards are present. The set of experimentsshows that the operator intervention is very limited and

is mainly due to the flippers that can get blocked within


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Completed Mission Operator interventions Tele-operated

missions duration per mission time

90% 7.8 min 0.6 min 1.8 11.4 s

Fig. 4 Results of the simulated missions (mean over 10 missions)

Fig. 6 On the left: the tree of motion plans, built while the robot is moving. On the right: the final map built during the

autonomous exploration of the simulated scenario.

obstacles. For this reason, the obstacle overcoming be-

havior has been recently improved in order to avoid flip-

per stucks ([9]). Indeed, this happened often during the

experiments and is the main reason of operator interven-

tions. As a future work, we want to integrate this im-

provement in the presented system.

The major drawback of the presented approach is the

use of a 2D based localization and mapping methods.

These are stable enough to be used in simple situations,

but eventually fail in more unstructured environments. In

order to overcome these limitations, two improvements

are proposed: from the one hand, a hardware mechanismthat is able to keep the laser range finder horizontally

will improve localization (this method is currently in-

vestigated in other research works by the scientific com-

munity); from the other hand, the use of an additional

tilted (i.e., pointing toward the ground) laser can make

it possible to measure elevation maps (the so called 2D

and a half maps) and detect possible mobility hazards

before the robot reaches them. The latter method has

been already developed and applied on a simulated robot

and successfully demonstrated during the RoboCupRes-

cue Virtual Robots competition, in 2007 [4].

REFERENCES

[1] S. Bahadori, D. Calisi, A. Censi, A. Farinelli,

G. Grisetti, L. Iocchi, and D. Nardi. Intelligent sys-

tems for search and rescue. Proc. of IROS Workshop

Urban Search and Rescue: from RoboCup to real

world applications (IROS), Sendai, Japan, 2004.

[2] S. Balakirsky, C. Scrapper, S. Carpin, and M. Lewis.

USARSim: providing a framework for multi-robot

performance evaluation. InProceedings of the Inter-

national Workshop on Performance Metrics for In-

tellingent Systems (PerMIS), 2006.

[3] D. Calisi, A. Farinelli, L. Iocchi, and D. Nardi.Autonomous navigation and exploration in a res-

cue environment. In Proceedings of IEEE Inter-

national Workshop on Safety, Security and Rescue

Robotics (SSRR), pages 5459, Kobe, Japan, June

2005. ISBN: 0-7803-8946-8.

[4] D. Calisi, A. Farinelli, and S. Pellegrini.

RoboCupRescue - Virtual Robots League, Team

SPQR-Virtual, Italy, 2007.

[5] A. Jacoff and E. Messina. DHS/NIST response robot

evaluation exercises. In IEEE International Work-

shop on Safety Security and Rescue Robots, Gaithers-

burg, MD, USA, 2006.

[6] J. Kuffner and S. LaValle. RRT-Connect: An ef-

ficient approach to single-query path planning. InProc. of IEEE Int. Conf. on Robotics and Automation

(ICRA2000), 2000.

[7] K. Ohno, S. Morimura, S. Tadokoro, E. Koyanagi,

and T. Yoshida. Semi-autonomous control of a 6-

DOF crawler robot having flippers for getting over

unknown steps. InProc. of the IEEE/RSJ Int. Conf.

on Intelligent Robots & Systems (IROS), pages 2559

2560, 2007.

[8] B. Yamauchi. A frontier based approach for au-

tonomous exploration. InIEEE International Sympo-

sium on Computational Intelligence in Robotics and

Automation, 1997.

[9] T. Yuzawa, K. Ohno, S. Tadokoro, and Koyanagi E.Development of sensor reflexive flipper control rule

for getting over uknown 3D terrain. Submitted.

a semi-autonomous tracked robot system for rescue missions

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