Source: http://www.google.com/patents/US6708068?ie=ISO-8859-1&dq=5920316
Timestamp: 2015-05-06 19:44:13
Document Index: 473845360

Matched Legal Cases: ['art 3', 'art 3', 'art 3', 'art 3', 'art 2', 'art 4', 'art 2', 'art 2', 'art 2', 'art 2', 'art 2', 'art 2', 'art 2']

Patent US6708068 - Machine comprised of main module and intercommunicating replaceable modules - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA machine, functioning as a unit, is composed of a main module and at least one replacable module having an ID. The main module includes (i) an ID recognition unit programmed to receive ID information from each replacable module, and (ii) an action decision unit including a decision algorithm programmed...http://www.google.com/patents/US6708068?utm_source=gb-gplus-sharePatent US6708068 - Machine comprised of main module and intercommunicating replaceable modulesAdvanced Patent SearchPublication numberUS6708068 B1Publication typeGrantApplication numberUS 09/627,507Publication dateMar 16, 2004Filing dateJul 28, 2000Priority dateJul 28, 1999Fee statusLapsedAlso published asEP1072365A1Publication number09627507, 627507, US 6708068 B1, US 6708068B1, US-B1-6708068, US6708068 B1, US6708068B1InventorsMasaya SakaueOriginal AssigneeYamaha Hatsudoki Kabushiki KaishaExport CitationBiBTeX, EndNote, RefManPatent Citations (10), Referenced by (4), Classifications (23), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetMachine comprised of main module and intercommunicating replaceable modules
(i) Aan ID recognition unit programmed to receive ID information from each replacable module (in FIG. 1, �F1�, an ID recognition 16).
(ii) An action decision unit comprising a decision algorithm programmed to select an action based on preselected signals (in FIG. 1, �F2�, an action decision 17). The decision algorithm can be of any type which selects an action in sequence based on preselected signals. The decision algorithm may provide an outcome such as �action: approach�, �level: high�, �direction: straight�, etc., as shown in FIG. 1.
(iii) An ID information unit storing ID information identifying said at least one replaceable module (in FIG. 1, �F3�, an ID information 18). The ID information may include the type of module, physical profiles, behavior library, behavior thresholds, etc., as shown in FIG. 1. In another embodiment, the ID information may include only minimum information to identify the module if the ID recognition unit further comprises a memory storing (a) action profiles and action threshold values at which predetermined actions are triggered with reference to each ID, and (b) default profiles and action threshold values (as shown in FIG. 5). If the received ID (numeral 50 in FIG. 5) is not new, corresponding information (numeral 52 in FIG. 5) is retrieved from the memory, and if the received ID is new, the default information (numeral 51 in FIG. 5) is used. Thus, the ID information can contain either full information or minimum information, or something therebetween.
(iv) An action actuation unit comprising an action algorithm programmed to actuate said at least one replaceable module based on signals from said action decision unit (in FIG. 1, �F5�, an action sequence 20). The action algorithm can be of any type which controls movement of the particular module based on information from the action decision unit. The action algorithm may provide an outcome such as �activation of motor #1 in sequence #1 at a high speed�, �activation of motor #2 in sequence #2 at a low speed�, etc., as shown in FIG. 1.
(v) An action coordination unit provided downstream of the action decision unit to coordinate actions of the respective replaceable modules (in FIG. 1, �F4�, a parts coordination 19). This unit is not indispensable, but when the at least two replaceable modules are used, the main module further comprises this unit wherein said action actuation unit receives signals from said action coordination unit. The coordination unit may control each module by controlling �component (module) #1 in sequence #1 while controlling �component (module) #2 in sequence #2 to accomplish an approaching action, as shown in FIG. 1.
Upon receiving the signals after the ID recognition, the action decision algorithm 32 selects an action to be performed. For example, if the selected action is �approaching a target located straight ahead at a high speed�, the decision is outputted to an action coordination algorithm 33. The action coordination algorithm 33 has also received the ID information so that the algorithm can select an appropriate operation sequence of each module in order to accomplish the selected action. For example, if legs are used, movement of each leg must be well coordinated, as compared with the use of wheels, in order to accomplish the selected action. The action coordination algorithm 33 provided in the main module 21 then outputs signals to a motion control algorithm 37 provided in the replaceable module 23 via an interface 40. The motion control algorithm 37 outputs signals specifying an operation sequence of each motor used in the replaceable module 23. Based on the outcome, the module 23 is actuated to initiate an action.
In this embodiment, the module 22 is provided with a detection unit 39 programmed to detect a deviation 35 of the actuated action from the selected action or an intervening event 34 which interferes with accomplishment of the action. The deviation may occur in a case wherein the action is �approaching a target located straight ahead� but the machine drifts slightly, for example. The intervening event may occur in a case wherein the action is �approaching a target located straight ahead� but during the performance, the machine is hit. If the machine is hit while approaching the target, the action decision algorithm 32 may be modified so that �approaching� action will not be triggered easily with relation to the target. If there is no intervening event, but there is a deviation 35 (such as slightly drifting to the right), the motion control algorithm 37 is modified to adjust the drifting. If the modification is not sufficient (36), the action coordination algorithm 33 is modified to adjust the movement of each module. If there is no deviation, the action continues by returning to the action decision algorithm 32.
FIG. 7 is a configuration diagram of a control system. Signals detected by the visual detecting means (a CCD camera) 3 d provided in the head-part module 3 and the auditory detecting means (a microphone) are transmitted to a user-information detecting part 3 g and an environment-information detecting part 3 h. The user-information detecting part 3 g detects the user's face, actions and voice and the environment-information detecting part 3 h detects external environment (the degree of brightness in a room, obstacles, etc.) seen from the robot and the information obtained is sent to the user/environment recognizing part 2 c. Signals detected by the tactile detecting means (torque detection or a pressure-sensitive sensor) provided in the leg-part module 4 are transmitted to a leg-state recognizing part 4 f. Contact with an obstacle, etc. is detected here and the information obtained is sent to the user/environment recognizing part 2 c. The information in the user/environment recognizing part 2 c is sent to a quasi-emotion generating part 2 d and quasi-emotions of the robot are generated here based on a stored emotion model. The emotion model is a calculation formula to compute parameters such as anger, sadness, joy, fear, aversion, fatigue, hunger, sleepiness, etc., which express the robot's emotions and the robot's emotions are generated according to the user information (the user's mood, orders, etc.) detected by sounds or images and the environment information (the degree of brightness in a room, etc.). For example, when the user returns home, the robot expresses an emotion of �joy� and if a stranger enters, it expresses an emotion of �anger�. At this time, by an action of the user such as praising or scolding, the robot's emotions are changed and made to adapt. An emotion model is created beforehand so that the robot reacts by making childish motions when it is a baby and as it grows, it makes adult motions. The character and behaviors of the robot growing up in this way are stored in the information memory part 2 b and learning processing is performed. Further, by learning processing, it is possible to make the robot learn tricks such as �Sit�, �Give me your hand�, etc.
The information in the user/environment-recognizing part 2 c and the quasi-emotion-generating part 2 d is sent to a behavior-deciding part 2 e and the robot's behavior is decided here by referring to a behavior library. The behavior library is a motion sequence for the robot to make a particular expression and is stored in the memory of each module. Table 1 explains behavior library examples and shows behavioral patterns of the leg-part module when it moves with a caterpillar and walks with legs. For example, if a behavior name is �advance�, behavioral patterns stored are to �make one normal rotation right and left at an equal speed� for a caterpillar and to �move each leg in the designated order� for legs. If a behavior name is �change direction�, behavioral patterns stored are to �rotate left and right conversely� for a caterpillar and to �move each leg in the designated order� for legs. If a behavior name is �dance�, behavioral patterns stored are to �repeat advancing, change direction and take turns randomly� for a caterpillar and to �fold hind legs in a sitting posture and raise and lower front legs alternately� for legs.
With this processing, it is possible not only to make a robot perform a different behavior when a module is replaced, but also to make it start from the point learned when re-attaching a previously used module. A behavior change becomes possible, for example, from a dog-like motion to a horse-like motion by attaching a longer leg. Additionally, key information, for example, �character� information or new behavioral pattern information, which manifests a new behavior when the robot learns and develops, is added to the memories 3 b and 4 b beforehand and at the time of attaching a device. Within the memory 2 b of the robot main body, the memory is configured so that any areas, which due to learning are no longer used, are overwritten. For example, by saving learned results regarding how to move legs in the memory 4 b of the leg-part module 4 and assembling into another robot main body the leg-part module 4 which has developed with the certain robot main body 2, it is possible to transmit specific action profiles to the other robot main body. Additionally, a stronger robot can be provided for fun by replacing arms, etc. with those with higher competence and further through learning from playing a match against another robot.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS5212645 *Jul 19, 1990May 18, 1993General Electric CompanyFlexible real-time, multi-tasking architecture for tool condition monitoringUS5220260 *Oct 24, 1991Jun 15, 1993Lex Computer And Management CorporationActuator having electronically controllable tactile responsivenessUS5428713Nov 25, 1992Jun 27, 1995Kabushiki Kaisha ToshibaCompound module type manipulator apparatusUS5687098 *Oct 30, 1995Nov 11, 1997Fisher Controls International, Inc.Device data acquisitionUS5739657 *Apr 30, 1996Apr 14, 1998Fujitsu LimitedApparatus for controlling motion of normal wheeled omni-directional vehicle and method thereofUS5870730 *Jul 7, 1995Feb 9, 1999Hitachi, LtdDecision making methodUS6411055 *Nov 20, 1998Jun 25, 2002Sony CorporationRobot systemDE19624929A1Jun 21, 1996Jan 2, 1998Siemens AgProze�automatisierungssystemEP0818283A1Jul 4, 1997Jan 14, 1998Sony CorporationRobot apparatusEP0924034A2Dec 16, 1998Jun 23, 1999Sony CorporationRobot devices and driving control methods* Cited by examinerReferenced byCiting PatentFiling datePublication dateApplicantTitleUS6967455 *Mar 8, 2002Nov 22, 2005Japan Science And Technology AgencyRobot audiovisual systemUS7873439 *Mar 24, 2006Jan 18, 2011Kabushiki Kaisha ToshibaRobot turning compensating angle error using imagingUS8972053Aug 29, 2012Mar 3, 20155D Robotics, Inc.Universal payload abstractionUS20130178982 *Jan 7, 2013Jul 11, 2013Tit Shing WongInteractive personal robotic apparatus* Cited by examinerClassifications U.S. Classification700/47, 700/247, 700/87, 713/100, 700/245, 700/169, 318/568.12, 318/568.11, 318/568.13, 700/181, 700/250International ClassificationB25J13/00, B25J9/16, G09B9/00, B25J9/22, B25J9/08, A63F13/00Cooperative ClassificationB25J9/1617, B25J9/08, G05B2219/40095, G05B2219/40304European ClassificationB25J9/16K1, B25J9/08Legal EventsDateCodeEventDescriptionMay 6, 2008FPExpired due to failure to pay maintenance feeEffective date: 20080316Mar 16, 2008LAPSLapse for failure to pay maintenance feesSep 24, 2007REMIMaintenance fee reminder mailedJul 28, 2000ASAssignmentOwner name: YAMAHA HATSUDOKI KABUSHIKI KAISHA, JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SAKAUE, MASAYA;REEL/FRAME:011010/0559Effective date: 20000728RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services