This paper introduces a methodology developed for the self-localization of a holon, within the reconfiguration task space of its colony under a highly decentralized decision-making and colony reconfiguration control. Holons act as intelligent agents of a colony in this decentralized structure. We term such an n-holon colony where intelligent agents can self-localize in reconfiguration task space without their tasks within a configuration being assigned by a central controller, as an n-holony. Our approach begins the self localization in task domain of a holon by proposing a way of extracting the invariants of the global state of the holony configuration which will then be used to infer the localization and action estimation by a holon using local knowledge. The holon communicates with its neighbours in order to modify its local knowledge towards a global one. This is the basic inference structure of a holony that is novel when compared to that of a colony of holons.

This paper uses a holonic hand model introduced in our early works (1998), which is composed of 2-stems-of-links-1-joint nonconvex biped planar holons working in colony in order to perform dexterous grasps. This paper focuses on the reconfiguration control of the colony that we structure as a sequence of planar grasp operations defining a holonic regrasp, which uses a graph theoretic formalization of configuration changes that occur in this holonic hand during this regrasping phase.

This paper introduces a methodology developed for the self-organization of a colony of holons in its task space where holons act as intelligent agents in this decentralized robotic structure such that they self localize themselves in the reconfiguration task space of their colony and identify their individual role in the colony re-organization. We term such an n-holon colony where intelligent agents can self-localize in the reconfiguration task space without their individual tasks within the colony configuration being assigned by a central controller, an n-holony. The proposed method is based on a cascaded computation scheme for the self-reconfiguration of the n-holony: self-localization followed by self-organization. The holon communicates with its neighbours in order to acquire knowledge about the colony configuration within a neighbourhood of radius d based on configuration invariants namely the eigenstructures. Self-organization uses this knowledge to take the next action for achieving the desired holony configuration.

A primary characteristic of dexterity in the manipulation of objects by multifingered hands in uncertain environments is the ability to recover from failing grasps through a regrasp procedure. Our focus in this paper is on regrasping control, posterior to the grasp phase, by generating an optimal sequence of hand preshapes, close in terms of manipulability and stability while not losing contact with the object. In our approach we treat the preshaping of a hand and the manipulation of an object with the same hand as a continuous and inseparable process, formulate preshaping manipulability and stability criteria using the vortex theory, and model the problem of regrasping as an optimal search using genetic algorithms toward a predetermined final preshape initiated by the kinematic constraints of a prior preshaping that has optimally contacted an object.

This paper investigates the holonic modeling of multifingered robotic hands where 2-stem-of-links+1-joint holons having basic kinematic properties are considered, working in colony in order to perform dexterous grasps. Those holons are the simplest physical elements, which have the capabilities of independent actuation, and of cooperation with other holons in achieving global behavior for the whole structure. Our work focuses on the concept of self-reconfigurability of the modular colony structure so as the holons reconfigure themselves to a pre-described structure in the absence of a human in the loop. The reconfiguration control of the dynamical holonic hand is a regrasp control and is developed based on hybrid control since our system exhibits both discrete and continuous behaviors.