By R. W. H. T. Hudson
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Additionally, the coordinates of each object are calculated. The object identifier and the respective coordinates of all objects found are transmitted to the robots microcontroller. New objects can be learned by a supervised learning algorithm: Typical examples of each object class are shown to the camera and the learning algorithm assigns the mean values of each parameter to the class these objects belong to. 5 times the standard deviation calculated during the teach-in procedure. 46 P. Nauth Average Hue Ha Feature vector of an unknown object assigned to class 1 Class 1 X Mean of Ha and F Class 3 Tolerance of Ha Class 2 Tolerance of F Form Factor F Fig.
In: Proc. Int. Work on Technical Challenges for Dependable Robots in Human Environments, Rome, Italy (2007) Goal Understanding and Self-generating Will for Autonomous Humanoid Robots P. de Abstract. An intelligent robot has been developed which understands the goal a user wants to be met, recognizes its environment, develops strategies to achieve the goal and operates autonomously. e. the task the user wants the robot to perform such as to bring a specific object. Next, the robot uses its smart camera and other sensors to scan the environment and to search for the demanded object.
Assembling Assembling is the task that allows fitting together two objects closely. Occurs when an object is placed over a surface or when an object is inserted into another, like a peg in a hole. Texture, collision, reaction force, and rigidity are the main indicators for this task. 5 Example of an Application To visualize these concepts with an example, a typical pick and place robotic application is considered, in which a rigid object is moved from one place to another (Fig. 3). The sequence of basic tasks involved are: 1.
Kummer's quartic surface by R. W. H. T. Hudson