Path planning using harmonic functions and probabilistic cell decomposition

Potential-field approach based on harmonic functions have good path planning properties, although the explicit knowledge of the robot’s Configuration Space is required. To overcome this drawback, a combination with a random sampling scheme is proposed. Harmonic functions are computed over computed over a 2d –tree decomposition of a d-dimensional Configuration Space that is obtained with a probabilistic cell decomposition (sampling and classification). Cell sampling is biased towards the more promising regions by using the harmonic function values. Cell classification is performed by evaluating a set of configurations of the cell obtained with a deterministic sampling sequence that provides a good uniform and incremental coverage of the cell. The proposed planning framework open the use of harmonic functions to higher dimensional C-spaces

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Distributed cooperation of multiple robots under operational constraints via lean communication

Η αυτόνομη λειτουργία των ρομπότ εντός περίπλοκων χώρων εργασίας αποτελεί ένα επίκαιρο θέμα έρευνας και η αυτόνομη πλοήγηση είναι αναμφισβήτητα ένα θεμελιώδες κομμάτι αυτής. Επιπλέον, καθώς οι εργασίες που τα ρομπότ καλούνται να εκπληρώσουν αυξάνονται σε πολυπλοκότητα μέρα με τη μέρα, η χρήση πολύ-ρομποτικών συστημάτων, τα οποία εμφανίζουν γενικά υψηλότερη ευρωστία και ευελιξία, αυξάνεται προοδευτικά. Ως εκ τούτου, τα προβλήματα αυτόνομης πλοήγησης που πρέπει να επιλυθούν γίνονται όλο και πιο απαιτητικά, αυξάνοντας την ανάγκη για πιο αποτελεσματικά και σθεναρά σχήματα σχεδιασμού πορείας και κίνησης

Trajectory planning using reachable-state density functions

Presents a trajectory planning algorithm for mobile robots which may be subject to kinodynamic constraints. Using computational methods from noncommutative harmonic analysis, the algorithm efficiently constructs an approximation to the robot's reachable-state density function. Based on a multiscale approach, the density function is then used to plan a path. One variation of the algorithm exhibits time complexity that is logarithmic in the number of steps. Simulations illustrate the method