Stochastic Search Methods for Mobile Manipulators

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This paper analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to a deterministic search approach, we observed improved robustness with comparable search times, thereby enabling effective calibration of the mobile manipulator

Forced Moves or Good Tricks in Design Space? Landmarks in the Evolution of Neural Mechanisms for Action Selection

This review considers some important landmarks in animal evolution, asking to what extent specialized action-selection mechanisms play a role in the functional architecture of different nervous system plans, and looking for “forced moves” or “good tricks” (see Dennett, D., 1995, Darwin’s Dangerous Idea, Penguin Books, London) that could possibly transfer to the design of robot control systems. A key conclusion is that while cnidarians (e.g. jellyfish) appear to have discovered some good tricks for the design of behavior-based control systems—largely lacking specialized selection mechanisms—the emergence of bilaterians may have forced the evolution of a central ganglion, or “archaic brain”, whose main function is to resolve conflicts between peripheral systems. Whilst vertebrates have many interesting selection substrates it is likely that here too the evolution of centralized structures such as the medial reticular formation and the basal ganglia may have been a forced move because of the need to limit connection costs as brains increased in size

A subsumptive, hierarchical, and distributed vision -based architecture for smart robotics

We present a vision-based architecture for smart robotics that is composed of modules, each with a specialized level of competence. At this time we are able to demonstrate the utility of the architecture for stereoscopic visual servoing, but the architecture could easily be extended to tasks such as “assembly-on-the-fly” in which a robot performs assembly operations on a moving target. Our architecture is subsumptive and hierarchical, in the sense that each module adds to the competence level of the modules below, and in the sense that they present a coarse-to-fine gradation with respect to vision sensing. At the coarsest level, the processing of sensory information enables a robot to become aware of the approximate location of an object in its field of view. On the other hand, at the finest end, the processing of stereo information enables a robot to know more precisely the position and orientation of an object in the coordinate frame of the robot. The processing at each level is completely independent and it can be performed at its own rate. A control arbitrator ranks the results of each level according to certain confidence indices, which are derived solely from the sensory information. This architecture has clear advantages regarding overall performance of the system, which is not affected by the “slowest link”, and regarding fault tolerance, since faults of one module don\u27t affect the other modules. A highlight of this architecture is that it is possible to devise the same overall architectural framework for both mobile robots and arm robots. While the framework can be the same, obviously the specific demonstrations of robotic competence are different

A subsumptive, hierarchical, and distributed vision-based architecture for smart robotics

Abstract—We present a distributed vision-based architecture for smart robotics that is composed of multiple control loops, each with a specialized level of competence. Our architecture is subsumptive and hierarchical, in the sense that each control loop can add to the competence level of the loops below, and in the sense that the loops can present a coarse-to-fine gradation with respect to vision sensing. At the coarsest level, the processing of sensory information enables a robot to become aware of the approximate location of an object in its field of view. On the other hand, at the finest end, the processing of stereo information enables a robot to determine more precisely the position and orientation of an object in the coordinate frame of the robot. The processing in each module of the control loops is completely independent and it can be performed at its own rate. A control Arbitrator ranks the results of each loop according to certain confidence indices, which are derived solely from the sensory information. This architecture has clear advantages regarding overall performance of the system, which is not affected by the “slowest link, ” and regarding fault tolerance, since faults in one module does not affect the other modules. At this time we are able to demonstrate the utility of the architecture for stereoscopic visual servoing. The architecture has also been applied to mobile robot navigation and can easily be extended to tasks such as “assembly-on-the-fly.” Index Terms—Assembly-on-the-fly, automation, computer vision, distributed architectures, robotics, vision-based architecture

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH