Robot Collision Avoidance with a Guaranteed Safety Zone and Randomized Symmetry Breaking

Collision avoidance of moving systems is a wellstudiedproblem. The use of an Artificial Potential Field functionis a popular approach to compute in real time a path that avoidscollision between agents. It involves the minimization of aweighted sum of an attractive force and a repulsive force.Previous studies consider these weights to be fixed designparameters, to be determined experimentally. In particular, theseparameters do not change during the run of the algorithm. Ourmain result is based on the observation that by dynamicallychanging these parameters one can obtain a guarantee on aminimum safety distance between the agents. Specifically, if theagents compute their path by minimizing the potential field withproperly chosen weights, there will always be a guaranteed safetydistance between each pair of agents. Our earlier studies showpromising experimental results and we extended the studies onavoiding trajectory symmetry.Our simulation validates ourmodel and demonstrated its effectiveness for a group of noncooperativeagents moving in a small area

Improved dynamic window approach by using Lyapunov stability criteria

This paper presents improvements over the Dynamic Window Approach (I-DWA), used for computing in real time autonomous robot navigation. A novel objective function that includes Lyapunov stability criteria is proposed. It allows to guarantee a global and asymptotic convergence to the goal avoiding collisions and resulting in a more simple and self-contained approach. Experimental results with simulated and real environments are presented to validate the capability of the proposed approach. Additionally, comparisons with the original DWA are given

Vision-based trajectory tracking algorithm with obstacle avoidance for a wheeled mobile robot

Wheeled mobile robots are becoming increasingly important in industry as means of transportation, inspection, and operation because of their efficiency and flexibility. The design of efficient algorithms for autonomous or quasi-autonomous mobile robots navigation in dynamic environments is a challenging problem that has been the focus of many researchers dining the past few decades. Computer vision, maybe, is not the most successful sensing modality used in mobile robotics until now (sonar and infra-red sensors for example being preferred), but it is the sensor which is able to give the information ’’what” and ’’where” most completely for the objects a robot is likely to encounter. In this thesis, we deal with using vision system to navigate the mobile robot to track a reference trajectory and using a sensor-based obstacle avoidance method to pass by the objects located on the trajectory. A tracking control algorithm is also described in this thesis. Finally, The experimental results are presented to verify the tracking and control algorithms

Μελέτη και υλοποίηση ρομποτικής πλατφόρμας διαφορικής κίνησης με MD25 H-Bridge και EMG-30 κινητήρες για ασύρματους αισθητήρες

Σκοπός αυτής της εργασίας είναι η μελέτη αυτοκινούμενων οχημάτων διαφορικής κίνησης και των ασύρματων αισθητήρων για την υλοποίηση ενός ενιαίου ενσωματωμένου συστήματος που λειτουργεί ως κινητός ασύρματος αισθητήρας. Η ρομποτική πλατφόρμα που υλοποιείται έχει σχεδιαστεί ώστε να μπορεί να χρησιμοποιηθεί για τη μελέτη κατανεμημένων αλγορίθμων σε κινητά ασύρματα δίκτυα αισθητήρων, να τηλεκατευθύνεται ή να αυτοκατευθύνεται μέσω του αισθητήρα. Αρχικά γίνεται μια εισαγωγή στη θεωρία ελέγχου και σχεδιασμού ρομποτικής κίνησης και εκτενέστερα στη διαφορική κίνηση 2 τροχών που είναι πολύ διαδεδομένη στα ρομποτικά οχήματα με τροχούς. Παρουσιάζεται η κινηματική της διαφορικής κίνησης και η οδομετρία. Επίσης επισημαίνονται τα χαρακτηριστικά των ασύρματων αισθητήρων και των πολλαπλών εφαρμογών τους και κυρίως τα κινητά ασύρματα δίκτυα αισθητήρων και οι εφαρμογές τους. Έπειτα, γίνεται μια σύντομη αναφορά στο υλικό που χρησιμοποιείται σήμερα για τη σχεδίαση ενός δικτύου αισθητήρων και στα τεχνικά χαρακτηριστικά των πλατφορμών και των πρωτοκόλλων επικοινωνίας. Εκτενέστερα παρουσιάσθηκε η πλατφόρμα Tmote Sky της Moteiv, την οποία και χρησιμοποιήσαμε, αναλυτικότερα οι τρόποι σύνδεσης I2C και serial (UART), όπως επίσης και η γέφυρα MD25 που χρησιμοποιήθηκε για τον έλεγχο των κινητήρων EMG30. Αντίστοιχα παρουσιάζεται το λογισμικό στο οποίο στηρίζεται η πλατφόρμα αυτή, το οποίο περιλαμβάνει το λειτουργικό σύστημα TinyOS και τη γλώσσα προγραμματισμού NesC. Ύστερα, αναφέρεται και η βιβλιοθήκη Swing της Java για το γραφικό περιβάλλον που χρησιμοποιήθηκε. Στη συνέχεια, περιγράφεται το γραφικό περιβάλλον και οι χρήσεις του

Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution

Contributions to Localization, Mapping and Navigation in Mobile Robotics

This thesis focuses on the problem of enabling mobile robots to autonomously build world models of their environments and to employ them as a reference to self–localization and navigation. For mobile robots to become truly autonomous and useful, they must be able of reliably moving towards the locations required by their tasks. This simple requirement gives raise to countless problems that have populated research in the mobile robotics community for the last two decades. Among these issues, two of the most relevant are: (i) secure autonomous navigation, that is, moving to a target avoiding collisions and (ii) the employment of an adequate world model for robot self-referencing within the environment and also for locating places of interest. The present thesis introduces several contributions to both research fields. Among the contributions of this thesis we find a novel approach to extend SLAM to large-scale scenarios by means of a seamless integration of geometric and topological map building in a probabilistic framework that estimates the hybrid metric-topological (HMT) state space of the robot path. The proposed framework unifies the research areas of topological mapping, reasoning on topological maps and metric SLAM, providing also a natural integration of SLAM and the “robot awakening” problem. Other contributions of this thesis cover a wide variety of topics, such as optimal estimation in particle filters, a new probabilistic observation model for laser scanners based on consensus theory, a novel measure of the uncertainty in grid mapping, an efficient method for range-only SLAM, a grounded method for partitioning large maps into submaps, a multi-hypotheses approach to grid map matching, and a mathematical framework for extending simple obstacle avoidance methods to realistic robots