150 results about "Behavior control" patented technology

A behavior control system of a robot for learning a phoneme sequence includes a sound inputting device inputting a phoneme sequence, a sound signal learning unit operable to convert the phoneme sequence into a sound synthesis parameter and to learn or evaluate a relationship between a sound synthesis parameter of a phoneme sequence that is generated by the robot and a sound synthesis parameter used for sound imitation, and a sound synthesizer operable to generate a phoneme sequence based on the sound synthesis parameter obtained by the sound signal learning unit.

A behavior control system and a behavior control method for a robot apparatus are disclosed. The behavior control system and the behavior control method for a robot apparatus include a function of adaptively switching between a behavior selection standard, taking into account the own state, required of an autonomous robot, and a behavior selection standard, taking into account the state of a counterpart, responsive to a situation. A behavior selection control system in a robot apparatus includes a situation-dependent behavior layer (SBL), capable of selecting a particular behavior from plural behaviors, and outputting the so selected behavior, and an AL calculating unit 120 for calculating the AL (activation level), indicating the priority of execution of the behaviors, for behavior selection. This AL calculating unit 120 includes a self AL calculating unit 122 and a counterpart AL calculating unit 124 for calculating the self AL and the counterpart AL, and an AL integrating unit 125 for summing the self AL and the counterpart AL with weighting by a parameter used for determining whether emphasis is to be placed on the self state or on the counterpart state, to output an ultimate AL. The counterpart is a subject of interaction of the robot apparatus. The self AL and the counterpart AL indicate the priority of execution of the behavior with the self and with the co8unbterpart as a reference, respectively.

Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

In a robot device, an action selecting/control system (100) includes a plurality of elementary action modules each of which outputs an action when selected, an activation level calculation unit (120) to calculate an activation level AL of each elementary action on the basis of information from an internal-state manager (91) and external-stimulus recognition unit (80) and with reference to a data base, and an action selector (130) to select an elementary action whose activation level AL is highest as an action to be implemented. Each action is associated with a predetermined internal state and external stimulus. The activation level calculation unit (120) calculates an activation level AL of each action on the basis of a predicted satisfaction0level variation based on the level of an instinct for an action corresponding to an input internal state and a predicted internal-state variation predictable based on an input external stimulus.

The object of the invention is to enable the appropriate change of a path according to a situation at that time of a user himself/herself and a situation of a target store and the path without having a special terminal when the user of facilities utilizes the target store and to perform appropriate guidance. To achieve the object, first, plural spatial recognition nodes respectively provided with a sensor and information processing equipment are installed in each location in the facilities and are connected via a network. The node recognizes the position, the action and the behavior of a user of the facilities, a server that controls a system manages the result of recognition and constantly grasp the situation of the user. Besides, the situation of the user is recognized by the node installed at a destination and on a path and is similarly notified to the server. The server displays appropriate path information based on the result of recognition on any of plural information displays installed in the facilities when the user approaches.

The invention relates to a humanoid-head robot and a behavior control method thereof, in particular to a humanoid-head robot device with human-computer interaction function and the behavior control method thereof, solving the problems that the prior humanoid-head robot can not completely realize the reproduction of human facial expressions, has limited perceptive function and does not have manual feeling models and the human-computer interaction function. The behavior control method comprises the following steps: a sensor perceptive system outputs perceived information to a main control computer for processing; control system software in a robot behavior control system obtains the relative control quantity of a corresponding motor according to the manual feeling models, executes a motion control instruction to output a PWM pulse by a motion control card to drive the corresponding motor to move to an appointed position and realize the human-computer interaction function and various feeling reactions of a robot; and the sensor perceptive system perceives external feeling signals, recognizes corresponding feeling signals and utilizes the manual feeling models to realize the behavior control of the robot. The invention realizes the reproduction of the human facial expressions and has anthropopathic multi-perception function, such as smell, touch, vision, and the like.

A behavior control apparatus for a vehicle is disclosed which allows a driver to select permission or inhibition of control. The apparatus includes a braking control section for performing, when the vehicle behavior detected is displaced to the behavior instability side with respect to a control start reference, braking control to stabilize the vehicle behavior, and an engine output suppressing control section for performing, when the vehicle behavior detected is displaced to the behavior instability side with respect to another control start reference, control to suppress the engine output power to stabilize the vehicle behavior. If it is selected by a selection section that the engine output suppressing control should be inhibited, then the engine output suppressing control is inhibited, and further, if it is decided by a decision section that the driver has an acceleration intention, then the braking control for the stabilization of the vehicle behavior is ended.

a mechanism and method for controlling the rights and/or behavior of applications in a terminal, especially in a mobile terminal, are disclosed. At least some of the messages generated by an application residing in the terminal and destined for a communication network are diverted to an independent controlling entity also residing in the terminal. In the controlling entity, the messages are controlled before being transmitted to the network. Depending on the application and its behavior in the terminal, the control entity may modify the messages or even prevent their sending to the network. The modification may include inserting control data, such as a digest, which can be used to authenticate the application.

Methods, apparatus, and systems for providing feedback to a subject in connection with a performance of a task, such as a movement task. The movement task may involve an operation or movement of an object by the subject, such as a body part or a sporting implement. During the movement task, the object has an expected motion path that is associated with one or more target areas. Behavior control feedback may be provided to the subject based on one or more of a position of the object, a motion of the object and an orientation of the object relative to one or more target areas as the subject performs the movement task. One example of an apparatus for providing behavior control feedback includes one or more radiation sources, one or more radiation detectors, and one or more indicators, any of which may be coupled to or otherwise integrated with the object. The object is operated to perform the movement task such that radiation emitted from a source of the apparatus impinges on one or more target areas covered with one or more reflectors. A radiation detector of the apparatus detects radiation that is reflected from the target areas, and an indicator coupled to the detector provides the behavior control feedback to the subject based on the detected radiation.

In a case where one of a left and right pair of drive wheels is a non-controlled wheel to which braking force of a vehicle behavior control is not applied, and the other of the drive wheels is a controlled wheel to which braking force is applied, a detected vehicle wheel speed for the non-controlled wheel is corrected to a smaller value in accordance with increase in the roll angle, and increase in the braking force of the controlled wheel when a roll angle of a vehicle body is larger than a threshold value. In a vehicle behavior control, when lateral G acts upon the vehicle during turning, braking force is applied to the controlled wheel that is at the outside of the turn. This braking force application causes driving force of the controlled wheel to be partially transferred to the non-corrected wheel that is at the inside of the turn, whereby the vehicle wheel speed of the non-corrected wheel is increased. Accordingly, in the present invention, correction calculation is performed.

The invention discloses an industrial control security defense system which comprises a potential threat exclusion layer, an industrial control security audit system, a monitoring center, a field monitoring layer, a control layer and a physical layer. The potential threat elimination layer comprises a deep protocol analysis mechanism and a white list mechanism; the field monitoring layer performs data exchange and behavior control on the control layer and the physical layer through an industrial Ethernet; the industrial control security auditing system can analyze and identify illegal operation, abnormal events and external attacks in an industrial control network from multiple angles and give an alarm in real time; identity recognition is carried out through multiple means, system operation authority can be limited, override operation and malicious operation are avoided, it is avoided that external equipment carries viruses to affect system safety, meanwhile, exclusive detection can be carried out on common attack means, and the operation resource occupancy is small. The alarm response time of the whole system can be prolonged through unified monitoring management of the monitoring center, and the accident handling speed is increased.

A behavior control apparatus for a vehicle, comprising a control unit that controls braking/driving forces of wheels so that the magnitude of a deviation between a steering angle and a steering angle conversion value of a yaw rate decreases when it is determined that the deviation exceeds a threshold value in magnitude and the vehicle is in an understeer state; the vehicle has a steering device including a variable gear ratio type rack bar; and, when the sign of the steering angle is the same as the sign of the offset amount and the zero point offset amount exceeds a reference value in magnitude, the control device corrects at least one of the threshold value and the magnitude of the deviation according to the steering angle and the zero point offset amount so that it becomes difficult to determine that the magnitude of the deviation exceeds the threshold value.

The present invention relates to a tag-based process enforcement behavior control method and system. The method comprises the following steps that: 1) a safety mark is set in a program file, and the safety mark is preferably stored in an extended attribute of the program file; 2) when the program file with the security mark runs, the operating system kernel reads the rule content of the security mark and stores the rule content in the process control block of the process; 3) when that process needs to execute a new program, match the path of the new program with the rule content stored in theprocess control block; 4) according to the matching result of the rules, the invention carries out the compulsory behavior control of the process. The invention adopts the compulsory mechanism to restrict the behavior range of the process, and once the compulsory execution of the process is beyond the limited range, the compulsory execution of the process can be terminated, the invention can effectively prevent the execution of arbitrary program after the process is injected by the code, and avoid the occurrence of the security event that the server is hijacked.

The invention discloses a cooperative control method and system for escort tasks based on obstacle environment and bounded input. The method comprises the steps of describing a physical model of a escort task by adopting a multi-Euler-Lagrange system; using an inner and outer loop control structure, an outer loop adopting a behavior control method based on empty space, and generating expected speed and expected motion trails required by an inner loop physical model; an inner ring being based on an improved proportional derivative sliding mode control method of an adaptive radial basis functionneural network, so that each physical model can track an expected speed and an expected motion trail in the presence of interference and uncertain parameters, and zero steady-state error and boundedinput are realized. The controller provided by the invention has the advantages of simplicity, no model and capability of providing continuous control signals, and the adaptive radial basis function neural network adopted in the design of the controller has strong learning ability and robustness, so that interference is well compensated, and zero steady-state error is finally realized.

The embodiment of the invention provides a target equipment control method and electronic equipment. The method is applied to the electronic equipment and comprises the steps of acquiring activity data of a user; performing state analysis on the user according to the activity data to obtain state information of the user; performing behavior prediction on the user according to the state information, the activity data and historical behavior data of target behaviors to obtain a predicted behavior of the user, wherein the predicted behavior is a behavior with the maximum prediction probability inthe target behaviors; and controlling target equipment to set the operation mode as an operation mode corresponding to the predicted behavior, wherein the target equipment is equipment in communication connection with the electronic equipment. According to the embodiment of the invention, the problem that the operation mode of a smart home system needs to be frequently selected by the user in theprior art is solved.

A handheld tool includes a handle; an implement mount configured to detachably accept and to rigidly hold a user-assistive implement; an actuator assembly mounted to the handle to physically manipulate the implement mount relative to the handle; a first sensor disposed to sense an orientation of the handle; a second sensor disposed to sense an orientation of the user-assistive implement; a controller disposed in or on the handle and coupled to the actuator assembly and the first and second sensors; and memory coupled to the controller. The memory stores instructions for identifying a type of the user-assistive implement attached to the implement mount, selecting a behavior routine based upon the type of the user-assistive implement identified, and manipulating the user-assistive implement relative to the handle according to the behavior routine to aid performance of a task with the handheld tool.