Adaptive-based Distributed Cooperative Multi-Robot Coverage

International audienceIn this paper we present a solution to the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with unknown obstacles. The problem is addressed taking into account several physical and environmental constraints like limited sensor capabilities, obstacle-avoidance, etc. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task. Furthermore, we propose a different formulation of the problem in order to obtain a distributed solution which allows us to consider also limited communication capabilities. Extensive simulations are presented to evaluate the efficiency of the proposed algorithm and to compare centralized and distributed approach

Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

International audienceIn this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach

Multi-Robot 3D Coverage of Unknown Terrains

International audienceIn this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage

Cognitive-based Adaptive Control for Cooperative Multi-Robot Coverage

International audienceIn this paper, the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. Furthermore, they are able to communicate one with each other. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution can not be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown and the team is heterogeneous, i.e. each robot is equipped with a different type of visual sensor. Extensive simulations are presented to show the efficiency of the proposed approach

Methodology of moddeling and optimizing the computational intelligence of a robot team

Η παρούσα διδακτορική διατριβή ερευνά τη χρήση μεθοδολογιών βασισμένων σε εργαλεία υπολογιστικής νοημοσύνης: α) για τον έλεγχο ενός ή και ομάδας ρομποτικών οχημάτων, β) για την ανάπτυξη συμπεριφορών που επιτρέπουν την αλληλεπίδραση ρομποτικών οχημάτων προκειμένου να επιτύχουν κοινούς στόχους. Δημιουργήθηκε ένα ολοκληρωμένο σύστημα ελέγχου βασισμένο σε μεθοδολογίες ασαφούς ελέγχου, ειδικά αναπτυγμένο φίλτρο αισθητηρίων και αλγόριθμος προσανατολισμού και ευρέσεως θέσης. Το σύστημα ελέγχου ενσωματώθηκε σε μια πολυεπίπεδη κατανεμημένη αρχιτεκτονική και αξιολογήθηκε πειραματικά σε ομάδα ρομποτικών οχημάτων που λειτουργούν σε εξωτερικό χώρο, με συνθήκες μεγάλης αβεβαιότητας. Το προτεινόμενο σύστημα ελέγχου, τροποποιημένο και κατάλληλα προσαρμοσμένο σε πρωτότυπο ρομποτικό όχημα που αναπτύχθηκε στα πλαίσια της διατριβής, χρησιμοποιήθηκε για την μελέτη βελτιστοποίησης ελεγκτών ρομποτικών οχημάτων. Για την διαδικασία της βελτιστοποίησης επιλέχθηκε η χρήση εξελικτικών αλγορίθμων. Μελετήθηκε η επίδραση των συναρτήσεων προσαρμογής στην απόδοση των ελεγκτών των ρομποτικών οχημάτων σε σχέση με την πληροφορία που παρέχεται κατά την διαδικασία της βελτιστοποίησης. Το αποτέλεσμα της μελέτης αυτής ήταν η ανάπτυξη ενός εμπειρικού συντελεστή που αποτελεί ένα χρήσιμο και αποτελεσματικό εργαλείο για την αξιολόγηση των συναρτήσεων προσαρμογής που χρησιμοποιούνται για την βελτιστοποίηση ελεγκτών ρομποτικών οχημάτων. Επιπλέον αναπτύχθηκε κατανεμημένος αλγόριθμος που επιτρέπει την αλληλεπίδραση και ανταλλαγή πληροφορίας μεταξύ ομάδας ρομποτικών οχημάτων για την επίτευξη κοινού στόχου δίχως την παρουσία εποπτικού ελεγκτή. Τα ρομποτικά οχήματα ελέγχονται από ασαφείς ελεγκτές που είναι υπεύθυνοι για την πλοήγηση και αποφυγή εμποδίων. Εξελικτικοί αλγόριθμοι χρησιμοποιήθηκαν για την μετάλλαξη των παραμέτρων του κατανεμημένου αλγορίθμου και των ασαφών ελεγκτών για την μελέτη, σε περιβάλλον προσομοίωσης, τόσο της βελτιστοποίησης της συνολικής απόδοσης του συστήματος όσο και της ανάδειξης αναδυόμενων συμπεριφορών. Η εξέλιξη οδήγησε σε συμπεριφορές με διαφορετική απόδοση και τρόπο λειτουργίας. Εντοπίστηκε η συνάρτηση προσαρμογής που οδηγεί στα βέλτιστα αποτελέσματα σε σχέση με την μελετώμενη δραστηριότητα καθώς και οι διαφοροποιήσεις που επιφέρει στον τρόπο λειτουργίας των ρομποτικών οχημάτων η επιλογή διαφορετικών συναρτήσεων προσαρμογής

Scalable and Convergent Multi-Robot Passive and Active Sensing

A major barrier preventing the wide employment of mobile networks of robots in tasks such as exploration, mapping, surveillance, and environmental monitoring is the lack of efficient and scalable multi-robot passive and active sensing (estimation) methodologies. The main reason for this is the absence of theoretical and practical tools that can provide computationally tractable methodologies which can deal efficiently with the highly nonlinear and uncertain nature of multi-robot dynamics when employed in the aforementioned tasks. In this paper, a new approach is proposed and analyzed for developing efficient and scalable methodologies for a general class of multi-robot passive and active sensing applications. The proposed approach employs an estimation scheme that switches among linear elements and, as a result, its computational requirements are about the same as those of a linear estimator. The parameters of the switching estimator are calculated off-line using a convex optimization algorithm which is based on optimization and approximation using Sum-of-Squares (SoS) polynomials. As shown by rigorous arguments, the estimation accuracy of the proposed scheme is equal to the optimal estimation accuracy plus a term that is inversely proportional to the number of estimator's switching elements (or, equivalently, to the memory storage capacity of the robots' equipment). The proposed approach can handle various types of constraints such as communication and computational constraints as well as obstacle avoidance and maximum speed constraints and can treat both problems of passive and active sensing in a unified manner. The efficiency of the approach is demonstrated on a 3D active target tracking application employing flying robots

Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

International audienceIn this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach

Cognitive-based Adaptive Control for Cooperative Multi-Robot Coverage

International audienceIn this paper, the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. Furthermore, they are able to communicate one with each other. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution can not be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown and the team is heterogeneous, i.e. each robot is equipped with a different type of visual sensor. Extensive simulations are presented to show the efficiency of the proposed approach