Distributed autonomous robotic systems 8

Tai Lieu Chat Luong Distributed Autonomous Robotic Systems Hajime Asama, Haruhisa Kurokawa, Jun Ota, Kosuke Sekiyama (Eds.) Distributed Autonomous Robotic Systems ABC Hajime Asama RACE (Research into Artifacts, Center for Engineering) The University of Tokyo Kashiwanoha 5-1-5 Kashiwa-shi, Chiba 277-8568 Japan E-mail: asama@race.u-tokyo.ac.jp Jun Ota The University of Tokyo Graduate School of Engineering 7-3-1 Hongo, Bunkyo-Ku Tokyo 113-8656 Japan E-mail: ota@robot.t.u-tokyo.ac.jp Haruhisa Kurokawa Distributed System Design Research Group National Institute of Advanced Industrial Science and Technology (AIST) 1-2-1 Namiki, Tsukuba Ibaraki 305-8564 Japan E-mail: kurokawa-h@aist.go.jp Kosuke Sekiyama Department of Micro-Nano Systems Engineering Nagoya University Nagoya 464-8603 Japan E-mail: sekiyama@mein.nagoya-ac.jp ISBN 978-3-642-00643-2 e-ISBN 978-3-642-00644-9 DOI 10.1007/978-3-642-00644-9 Library of Congress Control Number: 2009922104 c 2009 Springer-Verlag Berlin Heidelberg  This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable to prosecution under the German Copyright Law The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Typeset and Cover Design: Scientific Publishing Services Pvt Ltd., Chennai, India Printed in acid-free paper 987654321 springer.com Preface The International Symposia on Distributed Autonomous Robotic Systems (DARS) started at Riken, Japan in 1992 Since then, the DARS symposia have been held every two years: in 1994 and 1996 in Japan (Riken, Wako), in 1998 in Germany (Karlsruhe), in 2000 in the USA (Knoxville, TN), in 2002 in Japan (Fukuoka), in 2004 in France (Toulouse), and in 2006 in the USA (Minneapolis, MN) The 9th DARS symposium, which was held during November 17–19 in Tsukuba, Japan, hosted 84 participants from 13 countries The 48 papers presented there were selected through rigorous peer review with a 50% acceptance ratio Along with three invited talks, they addressed the spreading research fields of DARS, which are classifiable along two streams: theoretical and standard studies of DARS, and interdisciplinary studies using DARS concepts The former stream includes multi-robot cooperation (task assignment methodology among multiple robots, multi-robot localization, etc.), swarm intelligence, and modular robots The latter includes distributed sensing, mobiligence, ambient intelligence, and multiagent systems interaction with human beings This book not only offers readers the latest research results related to DARS from theoretical studies to application-oriented ones; it also describes the present trends of this field With the diversity and depth revealed herein, we expect that DARS technologies will flourish soon We thank everyone involved with the organization of DARS 2008 The members of the program committee organized sessions, reviewed papers, and contributed to enhancement of the quality of the program We thank Prof Alan Winfield (University of the West of England), Prof Katsuhiro Nishinari (The University of Tokyo), and Prof Gregory S Chirikjian (The Johns Hopkins University) for their plenary and keynote talks We truly appreciate co-sponsorship by the Mobiligence program of MEXT, and technical co-sponsorship by other organizations (IEEE RAS, SICE, RSJ, and JSME), and grants from the Inoue Foundation for Science and from the Suzuki Foundation Kind assistance extended by the Tsukuba Convention Bureau was indispensable for us We would like also to thank Dr Kuniaki Kawabata, Dr Masao Sugi, and Dr Kohji Tomita for their invaluable help with local arrangements, conference website, and document edition January 2009 Hajime Asama Haruhisa Kurokawa Jun Ota Kosuke Sekiyama Contents Part I: Distributed Sensing Multi-Robot Uniform Frequency Coverage of Significant Locations in the Environment Marco Baglietto, Giorgio Cannata, Francesco Capezio, Antonio Sgorbissa Measuring the Accuracy of Distributed Algorithms on Multi-robot Systems with Dynamic Network Topologies James McLurkin 15 Network Topology Reconfiguration Based on Risk Management Kosuke Sekiyama, Hirohisa Araki 27 Energy-Efficient Distributed Target Tracking Using Wireless Relay Robots Chia Ching Ooi, Christian Schindelhauer 39 Cooperative Object Tracking with Mobile Robotic Sensor Network Junji Takahashi, Kosuke Sekiyama, Toshio Fukuda 51 Deployment and Management of Wireless Sensor Network Using Mobile Robots for Gathering Environmental Information Tsuyoshi Suzuki, Ryuji Sugizaki, Kuniaki Kawabata, Yasushi Hada, Yoshito Tobe 63 VIII Contents Global Pose Estimation of Multiple Cameras with Particle Filters Ryuichi Ueda, Stefanos Nikolaidis, Akinobu Hayashi, Tamio Arai 73 Part II: Mobiligence Motion Control of Dense Robot Colony Using Thermodynamics Antonio D’Angelo, Tetsuro Funato, Enrico Pagello 85 Modeling of Self-organized Competition Hierarchy with Body Weight Development in Larval Cricket, Gryllus bimaculatus Shiro Yano, Yusuke Ikemoto, Hitoshi Aonuma, Takashi Nagao, Hajime Asama 97 Intelligent Mobility Playing the Role of Impulse Absorption 109 Jae Heon Chung, Byung-Ju Yi, Chang Soo Han Part III: Ambient Intelligence Cognitive Ontology: A Concept Structure for Dynamic Event Interpretation and Description from Visual Scene 123 Yuki Wakuda, Kosuke Sekiyama, Toshio Fukuda Subjective Timing Control in Synchronized Motion of Humans: A Basic Study for Human-Robot Interaction 135 Mitsuharu Nojima, Hiroaki Shimo, Yoshihiro Miyake Robot Software Framework Using Object and Aspect Oriented Programming Paradigm 149 Fumio Ozaki, Jun’ichiro Ooga, Kunikatsu Takase Distributed Context Assessment for Robots in Intelligent Environments 161 Fulvio Mastrogiovanni, Antonio Sgorbissa, Renato Zaccaria Part IV: Swarm Intelligence Jamology: Physics of Self-driven Particles and Toward Solution of All Jams 175 Katsuhiro Nishinari Contents IX Towards an Engineering Science of Robot Foraging 185 Alan F.T Winfield A Modular Robot Driven by Protoplasmic Streaming 193 Takuya Umedachi, Taichi Kitamura, Koichi Takeda, Toshiyuki Nakagaki, Ryo Kobayashi, Akio Ishiguro A Distributed Scalable Approach to Formation Control in Multi-robot Systems 203 I˜ naki Navarro, Jim Pugh, Alcherio Martinoli, Fernando Mat´ıa Guiding a Robot Flock via Informed Robots 215 Hande C ¸ elikkanat, Ali Emre Turgut, Erol S ¸ ahin Theoretical and Empirical Study of Pedestrian Outflow through an Exit 227 Daichi Yanagisawa, Ayako Kimura, Ryosuke Nishi, Akiyasu Tomoeda, Katsuhiro Nishinari Understanding the Potential Impact of Multiple Robots in Odor Source Localization 239 Thomas Lochmatter, Alcherio Martinoli Analyzing Multi-agent Activity Logs Using Process Mining Techniques 251 Anne Rozinat, Stefan Zickler, Manuela Veloso, Wil M.P van der Aalst, Colin McMillen Altruistic Relationships for Optimizing Task Fulfillment in Robot Communities 261 Christopher M Clark, Ryan Morton, George A Bekey Part V: Multi-robot Cooperation Robotic Self-replication, Self-diagnosis, and Self-repair: Probabilistic Considerations 273 Gregory S Chirikjian Analysis of Multi-robot Play Effectiveness and of Distributed Incidental Play Recognition 283 Colin McMillen, Manuela Veloso Sparsing of Information Matrix for Practical Application of SLAM for Autonomous Robots 295 Haiwei Dong, Zhiwei Luo X Contents Trajectory Generation for Multiple Robots of a Car Transportation System 305 Mitsuru Endo, Kenji Hirose, Yusuke Sugahara, Yasuhisa Hirata, Kazuhiro Kosuge, Takashi Kanbayashi, Mitsukazu Oomoto, Koki Suzuki, Kazunori Murakami, Kenichi Nakamura Distributed Control and Coordination of Cooperative Mobile Manipulator Systems 315 Enrico Simetti, Alessio Turetta, Giuseppe Casalino Rearrangement Task by Multiple Robots Using a Territorial Approach 325 Norisuke Fujii, Reiko Inoue, Jun Ota A Task Planner for an Autonomous Social Robot 335 Samir Alili, Rachid Alami, Vincent Montreuil A Stochastic Clustering Auction (SCA) for Centralized and Distributed Task Allocation in Multi-agent Teams 345 Kai Zhang, Emmanuel G Collins Jr., Dongqing Shi, Xiuwen Liu, Oscar Chuy Jr Corridors for Robot Team Navigation 355 Zack Butler, Carlos Bribiescas Part VI: Practical Control of Modular Robots Efficient Distributed Reinforcement Learning through Agreement 367 Paulina Varshavskaya, Leslie Pack Kaelbling, Daniela Rus Morphology Independent Learning in Modular Robots 379 David Johan Christensen, Mirko Bordignon, Ulrik Pagh Schultz, Danish Shaikh, Kasper Stoy Reconfigurable Modular Robots Adaptively Transforming a Mechanical Structure (Numerical Expression of Transformation Criteria of “CHOBIE II” and Motion Experiments) 393 Yosuke Suzuki, Norio Inou, Michihiko Koseki, Hitoshi Kimura Toward Flexible and Scalable Self-reconfiguration of M-TRAN 405 Haruhisa Kurokawa, Kohji Tomita, Akiya Kamimura, Satoshi Murata Contents XI Reconfigurable Teams: Cooperative Goal Seeking with Self-Reconfigurable Robots 417 Zack Butler, Eric Fabricant “Deformable Wheel”-A Self-recovering Modular Rolling Track 429 Harris Chi Ho Chiu, Michael Rubenstein, Wei-Min Shen Exploit Morphology to Simplify Docking of Self-reconfigurable Robots 441 Kasper Stoy, David Johan Christensen, David Brandt, Mirko Bordignon, Ulrik Pagh Schultz Reconfigurable Modular Universal Unit (MUU) for Mobile Robots 453 Shugen Ma, Changlong Ye, Bin Li, Yuechao Wang Part VII: Multi-robot Systems Design and Analysis for AGV Systems Using Competitive Co-evolution 465 Ryosuke Chiba, Tamio Arai, Jun Ota Cooperative Control of Multi-robot Nonholonomic Systems with Dynamics Uncertainties and Control Time-Delays 477 Junjie Zhang, Suhada Jayasuriya Guaranteed-Performance Multi-robot Routing under Limited Communication Range 491 Alejandro R Mosteo, Luis Montano, Michail G Lagoudakis Pipeless Batch Plant with Operating Robots for a Multiproduct Production System 503 Satoshi Hoshino, Hiroya Seki, Yuji Naka Control Methodology of Transfer Crane with Look-Ahead Rule in Harbor Transportation System 513 Hisato Hino, Satoshi Hoshino, Tomoharu Fujisawa, Shigehisa Maruyama, Jun Ota A Novel Marsupial Robot Society: Towards Long-Term Autonomy 523 Marek Matusiak, Janne Paanajă arvi, Pekka Appelqvist, Mikko Elomaa, Mika Vainio, Tomi Ylikorpi, Aarne Halme 562 M Shiomi et al for training the BN in real-time After a few selections, the system started sending supervision requests to the operator, who stopped selecting targets and began to respond to the requests for decisions 4.2 Results The experiments consisted of two phases: training and requesting In the training phase, real-time training was conducted for 60 minutes For training, the operator used 184 data sets of approach targets that consisted of ’yes’ or ’no’ labels, the time of the operator’s decision, and the behavioral and spatial primitives The operator also gave 128 data sets of non-approaching targets We measured the operating time in the requesting phase; the experiment was conducted for 2414 seconds (about 40 minutes) We also measured the time someone was in the environment within m of the robot, which was 1587 seconds (about 27 minutes), because the operator directly selected targets more than m from the robot Moreover, we measured the success rate of supervision requests after the training phase using a data set with 52 approach and 657 non-approach targets 4.2.1 Operating Time Table shows the measured operating time The experimental time represents the total time of the experiment, which was 2,414 seconds The time someone existed in the environment within m of the robot was 1587 seconds and the operating time was 845 seconds The time during which only decisions were made was 147.5 seconds Thus, operation time accounted for 34.9% (845/2,414) of the experimental time and 53.2% (845/1587) of the time someone was within m of the robot These results indicate that the developed system successfully reduced the operation time The average decision time was 1.83 seconds 4.2.2 Success Rate of Supervising Requests Table shows the success rate of supervision requests It was 86.5% for approach targets when the request timings were two seconds quicker than the operator’s decision The supervision request rate of non-approach targets was 34.6% These results indicate that the adaptive supervisory control system did not overlook targets to be approached and successfully reduced requests for non-approach targets Conclusion We developed an adaptive supervisory control system that enables a humanoid robot to approach appropriate people to provide information without bothering those who are not interested The system autonomously selects a target person and requests Adaptive Supervisory Control of a Communication Robot 563 permission to approach from the operator Moreover, it learns from the operator decisions and improves its target selections to approach Since the humanoid robot is mostly autonomous, the operator only decides whether to approach After receiving permission, the robot approaches the target and provides information to that person We conducted a field trial in a shopping mall The results suggest that the system well worked in a real environment The system successfully requested 86.5% of targets to approach The operator only controlled the robot for 6% of the experiment time to decide to approach and only monitored 34.9% for safety purposes Acknowledgements We wish to thank the staff of the SUMISHO URBAN KAIHATSU CO., LTD We also thank the following ATR members for their helpful suggestions and cooperation: Dylan F Glass and Zenta Miyashita This research was supported by the Ministry of Internal Affairs and Communications of Japan References Wada, K., Shibata, T.: Robot therapy in a care house - results of case studies In: Proc IEEE RO-MAN 2006, pp 581–586 (2006) Movellan, J.R., Tanaka, F., Fasel, I.R., Taylor, C., Ruvolo, P., Eckhardt, M.: The RUBI project: a progress report In: Int Conf on Human Robot Interaction, pp 333–339 (2007) Breazeal, C., Scassellati, B.: Infant-like social interactions between a robot and a human caretaker Adaptive Behavior 8(1), 49–74 (2000) Nakadai, K., et al.: Real-Time Auditory and Visual Multiple-Object Tracking for Robots In: Proc Int Joint Conf on Artificial Intelligence, pp 1425–1432 (2001) Koizumi, S., et al.: Preliminary Field Trial for Tele-operated Communication Robots In: IEEE International Workshop on Robot and Human Communication, pp 145–150 (2006) Woods, S., et al.: Comparing Human Robot Interaction Scenarios Using Live and Video Based Methods, Towards a Novel Methodological Approach In: Int Workshop on Advanced Motion Control, pp 750–755 (2006) Green, A., et al.: Applying the Wizard-of-Oz Framework to Cooperative Service Discovery and Configuration In: Proc IEEE Int Workshop on Robot and Human Interactive Communication, pp 575–580 (2004) Shiomi, M., et al.: A Semi-autonomous Communication Robot -A Field Trial at a Train Station In: ACM/IEEE 3rd Annual Conference on Human-Robot Interaction, pp 303– 310 (2008) Hayashi, K., et al.: Humanoid robots as a passive-social medium - a field experiment at a train station In: ACM/IEEE 2nd Annual Conference on Human-Robot Interaction, pp 137–144 (2007) 10 Bergstr, N., et al.: Modeling of Natural Human-Robot Encounter In: IROS 2008 (2008) (to appear) 11 Glas, D.F., et al.: Laser Tracking of Human Body Motion Using Adaptive Shape Modeling In: IEEE/RSJ Int Conf on Intelligent robots and systems, pp 602–608 (2008) 12 Hall, E.T.: The Hidden Dimension Anchor Books (1990) 13 Nishio, S., et al.: Robotic Platforms Structuring Information on People and Environment In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2008) (to appear) 564 M Shiomi et al 14 Jensen, F.V.: Bayesian networks and Decision Graphs Springer, Heidelberg (2001) 15 Tajika, T., et al.: Automatic Categorization of Haptic Interactions -What are the Typical Haptic Interactions between a Human and a Robot? In: IEEE International Conference on Humanoid Robots, pp 490–496 (2006) 16 Sugiyama, O., et al.: Three-layered Draw-Attention Model for Humanoid Robots with Gestures and Verbal Cues In: IEEE/RSJ Int Conf on Intelligent Robots and Systems, pp 2140–2145 (2005) Behavior Design of a Human-Interactive Robot through Parallel Tasks Optimization Yuichi Kobayashi, Masaki Onishi, Shigeyuki Hosoe, and Zhiwei Luo Abstract Robots that interact with humans are required to achieve multiple simultaneous tasks such as carrying objects, collision avoidance and conversation with human, in real time This paper presents a design framework of the control and the recognition processes to meet the requirement by considering stochastic behavior of humans The proposed designing method first introduces petri-net The petri-net formulation is converted to Markov decision processes and dealt with in optimal control framework Two tasks of safety confirmation and conversation tasks are implemented Tasks that normally tend to be designed by integrating many if-then rules can be dealt with in a systematic manner in the proposed framework The proposed method was verified by simulations and experiments using RI-MAN Introduction Human-robot interaction has come to gather attention recently [1, 2, 3] It is also expected that robots interact with humans in household environments Yuichi Kobayashi Tokyo University of Agriculture and Technology e-mail: yu-koba@cc.tuat.ac.jp Masaki Onishi Information Technology Research Institute, AIST e-mail: onishi@ni.aist.go.jp Shigeyuki Hosoe RIKEN Bio-mimetic Control Research Center e-mail: hosoe@bmc.riken.jp Luo Zhiwei Kobe University e-mail: luo@gold.kobe-u.ac.jp 566 Y Kobayashi et al In the environments where robots interact with humans, robots are required to multiple tasks, such as conveyance of objects, conversation with humans, collision avoidance against humans These parallel tasks should be sometimes done simultaneously in real time In addition, human interacting robots are required to realize state recognition which includes uncertainty This uncertainty is mainly caused by arbitrary motions of humans The problem of real-time processing of parallel tasks has been discussed in the field of task scheduling [4] In the case of multiple tasks of the human interacting robot, multiple tasks and requirement for the task are not well-defined because there have not been many trials of formulation of the parallel tasks of robots interacting with humans One reason for this is that the uncertainty caused by human behaviors is not simple enough to formulate in a standard task scheduling framework In the literature of robot control architectures, there have been many models to realize reactive and adaptive behaviors of robots [5, 6] As applications of the petri-net [9], a selection framework of multiple navigation behaviors [3], a hierarchical control including exceptional handling [7], and a motion generation of a humanoid robot using timed petri-net [8] were proposed These works mainly focused on navigation or the motion of the robot body as applications and human-interacting aspects are not considered because of the difficulty of formulation In this paper, human-interacting tasks such as collision avoidance and conversation are implemented The robot receives command by conversation while taking care about the safety of the human (collision avoidance) This paper proposes dealing with the design of human-interacting behavior through the modeling of petri-net, optimal control and models of human behaviors interacting with robots The proposed architecture consists of description of parallel tasks by petri-net and transformation of the petri-net form into Markov Decision Processes (MDPs) A general formulation of the proposed design is described in Later an application to parallel tasks using a human-interacting robot RI-MAN [10] is explained in The experimental and simulation results are shown in 4, followed by conclusion in Formulation of Parallel Tasks and Optimal Control State variables express the state of the environment, human and the robot They consist of continuous variables xc ∈ RNc and discrete ones xd ∈ ZNd Observation variables consist of continuous variables yc ∈ RMc and discrete ones yd ∈ ZMd The output to the actuators and the speaker consists of continuous variables uc ∈ RLc and discrete ones ud ∈ ZLd 2.1 Description of Parallel Tasks with Petri-net The tasks are expressed first by Petri-net Petri-net consists of places and transitions (see Fig.1) A circle on a place denotes a token The token moves Behavior Design of a Human-Interactive Robot Fig Example of petrinet expression for two tasks i and j 567 task j task i d1j d 2j d1i d 2i place transition d 3j d 0j d 0i token objective place from a place to another through a transition When a token moves, the corresponding transition is said to ‘fire’ A transition which is connected to multiple places as input can fire only when all of its input places have tokens The internal stages of task execution are expressed by places of petri-net The number of tasks is denoted by n An objective place di0 is defined to describe the desired stage of task i The number of places for task i is denoted by mi and places of task i are denoted by di0 , di1 , · · · , dimi In this paper it is assumed that there is always only one token per task There are cases where a single place has multiple transitions as outputs In such cases, which of these transitions will fire is characterized by firing probabilities In addition, two assumptions are introduced; 1) an expected duration is assigned to each transition and 2) a token can stay at the same place for a certain period 2) is expressed by defining transition from a place to itself in the process of transformation to MDPs 2.2 Optimal Control in Markov Decision Process The stages of the task execution of the robot can be represented as s = {s(1) , · · · , s(n) }, where s(i) denotes the place that has a token in task i This can be regarded as ‘state’ of the task execution When an action is determined as a function of state as a = π(s), π is called a policy Note here that the transitions of states are stochastic even for the same actions because of uncertainties caused by human motions The transitions among the states are characterized by state transition (i) (i) (i) (i) probabilities p(sk , sl , a) and expected durations r(sk , sl , a) for transi(i) (i) tions from sk to sl This expected duration is dependent on the action That is, a common expected duration T (a) is applied to all transitions of all (1) (1) (n) (n) tasks as r(sk , sl , a) = · · · = r(si , sj , a) = T (a) The goal for task i is to make the token for task i reach the objective place within the shortest expected time In the framework of optimal control with discrete state [13],this problem is to find a policy π which minimizes the ∞ (i)  (i) expected value Eπ , where rt denotes the duration of an action r t=0 t at step t in task i The state value function is defined as the expected return: In this research, we use basic definitions of transitions and omit inhibition and synchronization functions for simplicity 568 Y Kobayashi et al  (i) Vπ(i) (sk ) = Eπ ∞  t=0  (i) (i) r(sk , sl , a) , r(s0 , s0 , a) = 0, Vπ(i) (s0 ) = 0, s0 = di0 (i) (i) (i) (i) (1) The optimal state value function V ∗ (s) satisfies the Bellman equation [12] (i)  (i) V∗ (sk ) = a∈ A(s)  (i) (i) (i) (i) (i) (i) p(sk , sl , a) r(sk , sl , a) + V∗ (sl ) , (2) (i) sl (i) where s = {· · · , sk , · · · } and A(s) denotes action set for state s If the state transition probabilities and the expected durations are known, the state value (i) function V∗ (s) can be calculated by the above equation The optimal policy for the problem can be also derived based on V ∗ (s) 2.3 Optimization of Action Parameters An action a is more concretely expressed as a sequence of continuous outputs uc or a successive discrete output ud for a certain period The parameterization of action is generally expressed by a(θ), where θ = [α, β] in this case In our optimal control problem, parameter θ is determined instead of deciding action a Parameter set depends on actions, which is expressed by Θ(a) The Bellman equation with action parameterization can be rewritten as   (i) (i) (i) (i) (i) (i) (i) (i) V∗ (sk ) = p(sk , sl , a(θ)) r(sk , sl , a(θ)) + V∗ (sl ) , (3) a∈A(s), (i) θ∈Θ(a) sl (i) {· · · , sk , · · · } where s = Let π(θ) denote that a policy is parameterized by θ Note that in this research, parameter θ is adjusted for seeking optimal control while fixing policy π The state value of task i under π(θ) satisfies the following:   (i) (i) (i) (i) (i) (i) (i) (i) Vπ(θ) (sk ) = p(sk , sl , a(θ)) r(sk , sl , a(θ)) + Vπ(θ) (sl ) , (4) (i) sl (i) , sk , · · · } where s = {· · · In order to save calculation amount for finding optimal solution for θ, nominal parameter sets θ¯ are introduced The value ¯ can be computed off-line using (4) by fixing function under the policy π(θ) (i) ¯ θ = θ Using the values of Vπ(θ) ¯ (s), an approximation of the optimal multitask parameter selection can be done by [θ|s] = arg n  θ (i) (i) Qπ(θ) ¯ (sk , a(θ)) i=1 ≡ (i) (i) wi Qπ(θ) ¯ (sk , a(θ)),  sl  (i) (i) (i) (i) (i) (i) p(sk , sl , a(θ)) r(sk , sl , a(θ)) + Vπ(θ) ¯ (sl ) (5) Behavior Design of a Human-Interactive Robot 569 and wi > denotes weighting coefficient for setting priorities among tasks By (5), an appropriate action parameter can be selected considering (approximate) optimality of the shortest time control and priorities among tasks Implementation of Human-Interacting Parallel Tasks RI-MAN was developed to realize human-interacting tasks The robots has a speaker, two CCD cameras and two microphones on the head The state variables are xc = [xTangle , xThuman ]T and xd = [xcom , xnh ]T , where xangle denotes joint angles of the robot, xcom denotes command given by a human instructor, xnh denotes the number of humans and xhuman denotes positions of humans in the robot coordinate The observation variables are T T T yc = [yhuman , ysound , ytac ] and yd = ycom , where yhuman denotes position of human (face) obtained by image processing, ysound denotes orientation of human who generate sound, which is obtained by sound source localization [16], ycom denotes command or conversation ID recognized by speech and ytac denotes contact information between the robot body and humans The output variables are uc = uangle and ud = uspeech, where uangle denotes desired velocities of joint angles and uspeech denotes ID of speech 3.1 Definition of Parallel Tasks Fig.2 shows the tasks implemented in this research Two tasks of security of collision avoidance task and conversation task are simultaneously executed Task 1: Security of Collision Avoidance Task The robot looks around itself and confirms whether human exists or not When the robot judges that a human exists in vicinity, the robot speaks to human so that human does not approach to the robot any more The robot estimates probability of human existence ph (i1 , i2 ), where [i1 , i2 ] denotes a grid that is generated by dividing the 2D space around the robot For the judgment of human existence, two threshold values pth1 and pth2 (0 < pth1 < pth2 < 1) are introduced and used as human exists at [i1 , i2 ] when ph (i1 , i2 ) > pth2 (6) when ph (i1 , i2 ) < pth1 (7) human does not exist at [i1 , i2 ] human existence is unknown at [i1 , i2 ] when pth1 < ph (i1 , i2 ) < pth2 (8) Let Rvicinity denote the vicinity of robot set and Rcontact denote the contact set The grids are classified into three sets, Rvicinity, Rcontact and the rest Rrest = Rall \(Rvicinity ∪ Rcontact ) There are four places in the task: 570 Fig Expression of tasks by Petri-net Y Kobayashi et al d 21 d11 unknown attention speak found lost d 02 d12 d 22 conversation task d 31 safety collision d 01 security of collision avoidance task • ‘COLLISION’; there exists a grid [i1 , i2 ] with probability ph (i1 , i2 ) > pth2 , [i1 , i2 ] ∈ Rcontact • ‘ATTENTION’; the token is not at ‘COLLISION’ and there exists a grid with probability ph such that ph (i1 , i2 ) > pth2 , [i1 , i2 ] ∈ Rvicinity • ‘UNKNOWN’; the token is not at ‘COLLISION’ nor ‘ATTENTION’, and there exists a grid such that pth1 < ph < pth2 , [i1 , i2 ] ∈ Rrest ∪ Rvicinity • ‘SAFETY confirmed’; the token is not at other places That is, all the grids in Rvicinity has the lower probability ph such that ph < pth1 SAFETY is the objective place of the security task COLLISION is judged by tactile sensor Task 2: Conversation Task The robot faces to a human and speaks to him or her When the robot does not receive any speech from humans, the robot promote the conversation by orienting the face to a person There are three places in the conversation task • ‘Human FOUND’; there exists grid [i1 , i2 ] such that ph (i1 , i2 ) > pth2 The token on this place will transit to SPEAK when a speech is recognized • ‘SPEAK’; based on the result of recognition of human speech, the robot outputs some reply through the speaker The end of the output triggers the transition of the token from SPEAK to FOUND • ‘Human LOST’; there does not exist any grid such that ph (i1 , i2 ) > pth2 SPEAK is the objective place in the conversation task The transition from FOUND to SPEAK depends on the utterance of human This process is expressed by a stochastic transition 3.2 Head Trajectory Generation through Optimization The head swinging action is executed for all places of the security task and FOUND and LOST places of the conversation task Let ntraj denote the number of candidate trajectories The head trajectory is generated by choosing one from ntraj candidates through optimization To generate a candidate of trajectory θk (k = 1, · · · , ntraj ), several grids are extracted stochastically (see Fig.4) Head angles (denoted by [qpan , qtilt ]T ) that correspond to the positions of those grids are calculated The corresponding angles of the head comprise a candidate trajectory ph (i1 , i2 ) gives a probability to be extracted as a via Behavior Design of a Human-Interactive Robot 571 Rext qtilt q2 qpan Rvicinity Rall q3 q1 Rcontact robot qpan Fig Grid set around robot Fig Generation of trajectory with via points point of a trajectory That is, grids with high ph tend to be used as via points The selecting probability function is denoted by pselect (ph ), which is close to one when ph  and close to zero when ph  Let qi denote ith via point as qi = [qpani , qtilti ]T By succession of those points, a trajectory is generated as θk = {q1 , q2 , · · · , qnkv },where nkv denotes the number of via-points of candidate θk The head angles are ordered so that arctan(qtilti /qpani ) becomes monotonically increasing By setting the velocity of head angle vhead as constant, the total time for a periodic motion can be expressed by nv  qj+1 − qj  k T (a(θk )) = j=1 vhead , qnkv +1 ≡ q1 (i) (9) (i) This total time corresponds to the duration as r(sk , sl , a(θk )) = T (a(θk )) (1) (1) In the case of security task, the transition probability p(sk , sl , a(θk )) is calculated as followings First, grids that are visible by a sequence of head motions generated by head trajectory θk are calculated and denoted as set Rvisible Let Rugrid denote set of grids where humans existence is not known (defined by (8)) If view range realized by θk does not cover unknown grids, the token remains at UNKNOWN place This can be expressed as (1) (1) If Rvisible ∩ Runknown = ∅, then p(s2 , s2 , a(θk )) = 1, (1) (1) (1) (1) p(s2 , s1 , a(θk )) = p(s2 , s0 , a(θk )) = 0, (10) (1) (1) (1) where s0 , s1 and s2 denote SAFETY, ATTENTION, UNKNOWN, respectively On the other hand, when Rvisible ∩ Runknown = ∅, transition probabilities are given by the followings (1) (1) p(s2 , s1 , a(θk )) = (1 − ph (i1 , i2 )) (11) [i1 ,i2 ]∈Rvisible ∩Rvicinity (1) (1) p(s2 , s2 , a(θk )) (1) (1) (1) (1) = 0, p(s2 , s0 , a(θk )) = − p(s2 , s1 , a(θk )) (12) 572 Y Kobayashi et al 3.3 Model of Human Conversation It is assumed that the speech of human happens stochastically and the probability of speaking depends on how much the robot is looking at the person The motion of the robot head is periodic with a period of T (a(θk )) The ratio of duration when the robot is facing to the human is defined as ξ(θk ) = τ(i1 ,i2 ) (θk ) , T (a(θk )) a human exists in grid [i1 , i2 ], (13) where τ(i1 ,i2 ) (θk ) denotes the duration when [i1 , i2 ] is included in the view range The probability of human speech can be expressed as a function of ξ(θk ) as pspeech(ξ(θk )) pspeech(ξ)) is defined so that it becomes close to one when ξ  and decreases as ξ gets close to zero The transition probability (2) (2) in the conversation task is expressed as p(s1 , s0 , a(θk )) = pspeech(ξ(θk )) Experiment and Simulation The proposed framework is evaluated in simulation and experiment In the evaluation, stochasticity of human behavior is omitted by 1) evaluating how the robot acts on the human instead of evaluating human utterance, and 2) fixing trajectories of humans instead of letting humans walk arbitrarily Simulation was performed ten trials for each strategy, 300 [sec] for one trial Two virtual humans repeat walking along fixed trajectories around the robot Gazing time, neglecting time and average number of unknown grids are plotted in Fig.5, Fig.6 and Fig.7 Gazing time denotes the duration when the robot was looking at a human This duration affects the probability of human utterance and thus affects achievement of the conversation task Neglecting time denotes the duration when the robot did not notice a human who was in the region of Rvicinity The long duration of neglecting time means that the security task is not sufficiently achieved, because a token on UNKNOWN should move to ATTENTION as soon as possible Average number of unknown grids is calculated using (8) This number indirectly relates to the achievement of the security task because it becomes smaller when the robot keeps searching longer than tracking When [w1 , w2 ] = [1, 0], optimization of (5) is done considering the security task only As a result, the robot keeps searching On the contrary, when [w1 , w2 ] = [0, 1], the conversation task is considered in the optimization In this case, the robot keeps following humans By changing [w1 , w2 ], the balance between searching and tracking humans can be adjusted From Fig.5-Fig.7, it can be seen that the performance of the robot is adjusted by changing parameters w1 , w2 In Fig.5, the gazing time was the longest in case of [w1 , w2 ] = [0, 1] That is, the best performance of the conversation task was achieved by putting priority to the conversation task On the contrary in Fig.7, the number of unknown grids was the largest in case of 100 neglected time [sec] gazing time [sec] 250 200 150 100 50 [1,0] [1,1] [0.7,1] [0.5,1] [0,1] [w1 , w2 ] Fig Gazing time 80 60 40 20 [1,0] [1,1] [0.7,1][0.5,1] [0,1] [w1 , w2 ] Fig Neglected time neglected time [sec] gazing time [sec] 200 160 120 80 40 573 [1,0] [0.7,1] [w1 , w2 ] [0,1] Fig Gazing time in experiment # of unknown grids [sec] Behavior Design of a Human-Interactive Robot 12 10 [1,0] [1,1] [0.7,1][0.5,1] [0,1] [w1 , w2 ] Fig Unknown grids 200 160 120 80 40 [1,0] [0.7,1] [w1 , w2 ] [0,1] Fig Neglected time in experiment [w1 , w2 ] = [0, 1] That is, the performance of the security task, to look around and decrease the number of unknown grids, was sacrificed by putting priority to the tracking behavior Next in the experiment, two (real) humans walk around RI-MAN along fixed trajectories for 420 [sec] per one trial In Fig.8, the gazing time is maximum in the case of [w1 , w2 ] = [0, 1] In Fig.9, the neglecting time is minimum in the case of [w1 , w2 ] = [1, 0] In the case of [w1 , w2 ] = [0.7, 1], an intermediate performance was obtained both in the gazing time and the neglecting time Thus, a similar tendency to the case of simulation could be seen also in the experiment Conclusion This paper proposed a behavior design of a human-interacting robot which is required to execute multiple parallel tasks under uncertainties caused by humans MDPs were constructed based on the description of parallel tasks by the petri-net The control framework was proposed as a shortest-time optimal control problem and the multiple task problem could be dealt with in a systematic manner In the application to the security task and the conversation task of RI-MAN, models of human behaviors were introduced By simulation and experiment, it was verified that the proposed framework enables to adjust the performance of the robot by changing weighting parameters 574 Y Kobayashi et al References Kanda, T., Hirano, T., Eaton, D., Ishiguro, H.: Interactive robots as social partners and peer tutors for children: A field trial Human Computer Interaction 19(1-2), 61–84 (2004) Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N.: Interactive humanoid robots for a science museum IEEE Intelligent Systems 22(2), 25–32 (2007) Kim, G., Chung, W., Park, S., Kim, M.: Experimental research of navigation behavior selection using generalized stochastic petri nets for a tour-guide robot In: Proc of IEEE/RSJ Int Conf on Intelligent Robots and Systems (2005) Bazewicz, J.: Scheduling computer and manufacturing processes Springer, Heidelberg (1996) Brooks, R.A.: A robust layered control system for a mobile robot IEEE Journal of Robotics and Automation RA-2, 253–262 (1986) Connell, J.H.: Sss: A hybrid architecture applied to robot navigation In: Proc of the 1992 IEEE Conf on Robotics and Automation, pp 2719–2724 (1992) Lehmann, A., Mikut, R., Asfour, T.: Petri nets for task supervision in humanoid robots In: Proc 37th International Symposium on Robotics, pp 71–73 (2006) Kobayashi, K., Nakatani, A., Takahashi, H., Ushio, T.: Motion planning for humanoid robots using timed petri net and modular state net In: Proc of the 2002 Int Conf on Systems, Man & Cybernetics, pp 334–339 (2002) Haas, P.J.: Stochastic Petri Nets Springer Series in Operations Research (2002) 10 Odashima, T., et al.: A soft human-interactive robot ri-man In: Video Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2006) 11 Ramage, P.J.G., Wonham, W.M.: The control of discrete event system Proc IEEE 77(1), 81–98 (1989) 12 Sutton, R.S., Barto, A.G.: Reinforcement Learning MIT Press, Cambridge (1998) 13 Bertsekas, D.: Dynamic Programming and Optimal Control Athena Scientific (2005) 14 Elfes, A.: Using Occupancy Grids for Mobile Robot Perception and Navigation Computer 22(6), 46–57 (1989) 15 Stepan, P., Kulich, M., Preucil, L.: Robust data fusion with occupancy grid IEEE Trans on Systems, Man, and Cybernetics Part C 35, (2005) 16 Nakashima, H., Ohnishi, N., Mukai, T.: Self-Organization of a Sound Source Localization Robot by Perceptual Cycle In: 9th Int Conf on Neural Information Processing, vol 2, pp 834–838 (2002) Part VIII Human-Robot Interaction Tracking and Following People and Robots in Crowded Environment by a Mobile Robot with SOKUIKI Sensor Akira Ohshima and Shin’ichi Yuta Abstract An experimental system of the autonomous mobile robot witch follows a people or other robots was implemented and demonstrated in various situation For this purpose, an algorithm to track an object by the moving sensor is considered and designed In this method, SOKUIKI sensor (Scanning Laser Range Finder) output are combined with odometry data, in order to find the particular target object the others, and to estimate the target object motion The many experimental have shown the robust target object following ability of this system Introduction Recently, many types of the service applications for the people by the robots are considered For such applications, the robot is requested to work in the ordinary environment where people live Especially for the mobile robots, the tasks such as going to the particular target position, or following a particular object robustly are often required to be realize in the cluttered environment with many people In this research, we have realized a robust mobile robot motion of following a human or the other moving objects in relatively environments The target motion, or just chasing the targets, is a simple task, and have already been demonstrated by others ([1][2][3][6][4][5]) However, most of then could not move robustly enough in the cluttered real environments, because the behaviors of the people in the environment or the objects are difficult to be modeled In this research, an approach with the incremental design and implementation using many concrete experiments in the realistic environments are adopted to realize a real robot system which achieves such a task Akira Ohshima and Shin’ichi Yuta Intelligent Robot Laboratory University of Tsukuba, Japan e-mail: {oosima,yuta}@roboken.esys.tsukuba.ac.jp http://www.roboken.esys.tsukuba.ac.jp/