An embedded particle filter SLAM implementation using an affordable platform

PostprintThe recent growth in robotics applications has put to evidence the need for autonomous robots. In order for a robot to be truly autonomous, it must be able to solve the navigation problem. This paper highlights the main features of a fully embedded particle filter SLAM system and introduces some novel ways of calculating a measurement likelihood. A genetic algorithm calibration approach is used to prevent parameter over-fitting and obtain more generalizable results. Finally, it is depicted how the developed SLAM system was used to autonomously perform a field covering task showing robustness and better performance than a reference system. Several lines of possible improvements to the present system are presented

Version 2.1 Alfredo Weitzenfeld August 1991

this document was supported in part by grant 1R01 NS24926 from NINDS of the National Institutes of Health (Michael A. Arbib, Principal Investigator), and in part by a grant from Rockwell International to the USC Center for Neural Engineering. NSL - Neural Simulation Language Version 2.1 The software described in this document and the document itself are copyrighted and may not be copied or otherwise distributed without the prior written consent of the Brain Simulation Laboratory. The information in this document is subject to change. The Brain Simulation Laboratory assumes no responsibility for any errors that may appear in this document. Users are requested to advise Alfredo Weitzenfeld at the address given below of any comments or suggestions for improvements. To get copies of the software contact : Alfredo Weitzenfeld Brain Simulation Laboratory Center for Neural Engineering University of Southern California Los Angeles, CA 90089-2520 e-mail : [email protected] The lab will charge a fee of $50 for tape, user's manual and delivery. (For FTP, send a request to the above e-mail address.) Copyright 1991 Brain Simulation Laboratory All Rights Reserved Alfredo Weitzenfeld: NSL 2.1 3 Table of Contents 1. Introduction .......................................................................................................................6 2. NSL System Overview.......................................................................................................6 2.1. System Requirements........................................................................................7 2.2. User Expertise..................................................................................................7 2.3. System Design....................................................................

Multi Robot Systems: The EagleKnights/RoboBulls Small- Size League RoboCup Architecture

In this paper we present the system architecture of the Eagle Knights/RoboBulls Small Size League RoboCup Team. In this league two teams composed of five autonomous robots each compete against each other in a medium size field. This league is one of the fastest and most thrilling in RoboCup permitting teams to develop complex coordination strategies. We explain the three main components of the architecture: Vision System, AI System and Robots

ASL: Hierarchy, Composition, Heterogeneity, and Multi-Granularity in Concurrent Object-Oriented Programming

The Abstract Schema Language (ASL) defines a hierarchical computational model for the development of distributed heterogeneous systems. ASL extends the capabilities and methodologies of concurrent object-oriented programming to enable the construction of highly complex multi-granular systems. The ASL model is described in terms of schemas (concurrent objects), supporting aggregation (schema assemblages), and both top-down and bottom-up system designs. ASL encourages code reusability by enabling the integration of heterogeneous components, e.g., procedural and neural network programs. ASL schemas are designed and implemented in an orthogonal fashion; integrated, either statically, through wrapping, or dynamically, via (task) delegation. Schemas include a dynamic interface, made of multiple unidirectional input and output ports, and a body section where schema behavior is specified. Communication is in the form of asynchronous message passing, hierarchically managed, internally, through..

An Overview of ASL: Hierarchy, Composition, Heterogeneity, and Multi-Granularity in Concurrent Object-Oriented Programming

The Abstract Schema Language (ASL) [Weitzenfeld 1992; Weitzenfeld and Arbib 1992] unifies schema modeling [Arbib 1992] with concurrent object-oriented programming (COOP). ASL extends the current state of the art in both areas by providing a hierarchical approach towards heterogeneous and multi-granular concurrent object design. Schemas in ASL are functional units which get implemented in an orthogonal fashion. This aspect not only encourages code reusability, but enables schema implementations as, e.g., procedural programs or neural network processes. ASL addresses the need to separate task specification from actual task implementation, by utilizing the concept of dynamic task delegation and static wrapping of external programs. ASL schemas define class templates from which active objects get dynamically instantiated. Schemas incorporate a dynamic interface made of multiple input and output ports, plus a body section which explicitly specifies the schema behavior. Communication is in t..

Trilateration-Based Robot Localization with Learned Visual Landmarks

Many strategies for robot localization exist, such as trilateration and triangulation algorithms, that compute relative distance and orientation of robot to multiple landmarks. These algorithms require predefined knowledge of landmarks. In this paper we describe a trilateration algorithm for robot localization where new landmarks may be defined using deep learning algorithms. We use a single camera to learn new custom objects. This information is passed as input to the system together with the distance to the objects calculated by a previously calibrated distance detection algorithm. A deep learning algorithm is applied to the object detection model making use of TensorFlow’s Object Detection API to identify custom objects in the environment. The information from the detected object in the camera image is used to calibrate the distance detection algorithm. The relative position of the objects is then used as input data to the trilateration-based localization algorithm. Examples of new objects used as landmarks in our system include a chair, sofa, fridge, shelf, and coffee table. To train the model with custom objects, a dataset of 100 images were collected by taking photos with a laptop camera. These images were randomly separated into two partitions: a train partition with 90 images and a test partition with 10 images. These objects were then tested in experimental work. The paper provides results and discusses shortcomings and future work

Machine Learning Approaches for Attacking Behaviors in Robot Soccer

In this work, Machine Learning approaches were applied to attacking behaviors in RoboCup Small-Size League autonomous robot soccer. Neural networks were used in order to get a binary prediction of an attacking action’s success, while deep reinforcement learning was leveraged to learn low level skills which control the robot’s wheel speeds and kicker. A trained neural network was used to predict whether a shot would be successful, improving the number of goals scored by the attacking behavior by 84 to 186%. The reinforcement learning methodologies used in this work produced behaviors which were efficient in speed, beating manually programmed behaviors in time taken, but can benefit from future refinements to improve accuracy in shooting towards goal

Humanoid Robot Motion Control for Ramps and Stairs

Humanoid robot research and development have been an ongoing effort for many years. Tasks such as humanoid robot walking have been extensively researched, and can be often solved using control theory. In this paper, we explore how a Darwin-Op humanoid robot can autonomously balance and walk on non-flat terrains that include ramps and stairs. We use computer vision to detect the specific type of non-flat terrain based on color. Once the specific terrain has been identified, the humanoid robot computes the distance and orientation that it needs to walk before stopping. We developed the walking model using zero moment point (ZMP) trajectory planning with a cart-table model for the center of mass. We show that the robot is able to walk up the stairs and ramps, and compare experimental results with both stairs and ramps