Catálogo de publicaciones - libros

We propose a framework for reactive motion and sensing planning based on critical events. A critical event amounts to crossing a critical curve, which divides the environment. We have applied our approach to two different problems: i) object finding and ii) pursuit-evasion. We claim that the proposed framework is in general useful for reactive motion planning based on information provided by sensors. We generalize and formalize the approach and suggest other possible applications.

For autonomous mobile robots following a planned path, self-localization is a very important task. Cumulative errors derived from the different noisy sensors make it absolutely necessary. Absolute robot localization is commonly made measuring relative distance from the robot to previously learnt landmarks on the environment. Landmarks could be interest points, colored objects, or rectangular regions as posters or emergency signs, which are very useful and not intrusive beacons in human environments. This paper presents an active localization method: a visual planning function selects from a free collision path and a set of planar landmarks, a subset of visible landmarks and the best combination of camera parameters (pan, tilt and zoom) for positions sampled along the path. A visibility measurement and some utility measurements were defined in order to select for each position, the camera modality and the subset of landmarks that maximize these local criteria. Finally, a dynamic programming method is proposed in order to minimize saccadic movements all over the trajectory.

A new reference walking trajectory for a planar five-link biped, considering both the SSP and the DSP, is presented firstly. A new combined controller to generate walking gaits following the reference trajectory is designed subsequently. The controller of five-link biped is consisted of PD controller and a fuzzy wavelet neural network controller. The scalable and shiftable coefficients of the wavelet function and weights of the network can be acquired by training the network by back-propagation algorithm online. The simulation results of the reference trajectory show that the proposed reference trajectory have good stability, repeatability and continuity during both SSP and the DSP, and when given the different initial conditions, the compatible trajectories can be achieved correspondingly. The simulation results of the trained controller show that the controller can generate the walking gaits to track the reference trajectory as close as possible.

Robotic end effectors are used over a diverse range of applications where they are required to grip with optimal force to avoid the object be either dropped or crushed. The slipping state can be easily detected by the output of the slip sensor. When the output has a non-zero value, the object is slipping. Conversely, detecting the deformation (crushing) state is more difficult, especially in an unstructured environment. Current proposed methodologies are and specialised to the particular object or objects to be handled. Consequently, the gripper can only manipulate prior known objects, constraining the gripper application to a small set of predetermined objects. Accordingly, in this paper, it is proposed a hybrid approach of fuzzy and expert systems that permits to detect when an unknown object is being deformed. To determinate when the gripped object is being deformed, the fuzzy/expert system uses information from three sensors: applied force, slip rate and finger position. Several objects of different characteristics were used to prove the effectiveness of the proposed approach.

Advanced robotic systems require an end effector capable of achieving considerable gripping dexterity in unstructured environments. A dexterous end effector has to be able of dynamic adaptation to novel and unforeseen situation. Thus, it is vital that gripper controller is able to learn from its perception and experience of the environment. An attractive approach to solve this problem is intelligent control, which is a collection of complementary ’soft computing’ techniques within a framework of machine learning. Several attempts have been made to combine methodologies to provide a better framework for intelligent control, of which the most successful has probably been that of neurofuzzy modelling. Here, a neurofuzzy controller is trained using the actor-critic method. Further, an expert system is attached to the neurofuzzy system in order to provide the reward signal and failure signal. Results show that the proposed framework permits a transparent-robust control of a robotic end effector.

Intelligent service robots (ISRs) adapt to unpredictable environments by determining how to resolve the problems occurred in a troubled situation. As a way to successfully and continuously provide services, we envisage the software system embedded in a robot to dynamically reconfigure itself using new components selected from component repositories. This paper describes a component selection mechanism, which is an essential function to support such dynamic reconfiguration. We adopt a semantically-based component selection mechanism in which situational information around ISRs is represented as critical semantic information for the service robots to select software components.

Industrial manufacturing cells involving fixtureless environments require more efficient methods to achieve assembly tasks. This paper introduces an approach for Robotic Fixtureless Assembly (RFA). The approach is based on the Fuzzy ARTMAP neural network and learning strategies to acquire the skill from scratch without knowledge about the assembly system. The vision system provides the necessary information to accomplish the assembly task such as pose, orientation and type of component. Different ad-hoc input vectors were used as input to the assembly and the vision systems through several experiments which are described. The paper also describes the task knowledge acquisition and the followed strategies to solve the problem of automating the peg-in-hole assembly using 2D images. The approach is validated through experimental work using an industrial robot.

Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architecture called MARTMAP which is based on the biological model of sensorial perception and has been designed to be a more versatile alternative to data fusion techniques and non-modular neural architectures. Besides the computational overload compared to FuzzyARTMAP, MARTMAP reaches similar performance. This paper reports the results found in simulated environments and also the observed results during assembly operations using an industrial robot provided with vision and force sensing capabilities.

We present a remote operated robot application based on a standard CORBA distributed system to control a MOTOMAN 6-DOF arm manipulator. The robot is based on XRC2001 robot controller. Current approach could be extented to other Yaskawa controllers (e.g. ERC, ERCII, MRC, MRCII) without major changes. The main idea is to define a set of generic IDL interfaces that can be used to integrate commercial proprietary libraries hiding the intricate of low level components. The challenge is to create a remote client-server application which facilitates the integration of one or several arm manipulators based on mentioned controllers independently from computer system or different platforms.

Two communities work in parallel in model-based diagnosis: FDI and DX. In this work an integration of the FDI and the DX communities is proposed. Only relevant information for the identification of the minimal diagnosis is used. In the first step, the system is divided into clusters of components, and each cluster is separated into nodes. The minimal and necessary set of contexts is then obtained for each cluster. These two steps automatically reduce the computational complexity since only the essential contexts are generated. In the last step, a signature matrix and a set of rules are used in order to obtain the minimal diagnosis. The evaluation of the signature matrix is on-line, the rest of the process is totally off-line.