The DustBot Project and more (if you like) Achim J. Lilienthal AASS Learning Systems Lab, Örebro University y Contents 1. The DustBot Project 2. Omnivision-based SLAM (MiniSLAM) 3. Iterative Spectral Clustering Achim J. Lilienthal → Contents y The Dustbot Project Achim J. Lilienthal 1 DustBot Project – Motivation ] Current Challenges in Urban Hygiene \ need for more efficient street cleaning [ reducing urban blight (removal of rubbish) [ improving the hygienic level (monitoring and antogonising pollution) [ reducing particulate matter dusts (≤ PM10) \ rubbish at public places is often difficult to remove [ specialised vehicles do not have access or have problems because of traffic jams and/or the small space available \ harmful pollutants accumulate on street surfaces [ heavy metal and organic matter pollutants contained in deposits from motor emissions, industry fallout, ... Achim J. Lilienthal 1 The DustBot Project ] DustBot – an Urban Hygiene Management System \ network of cooperating robots \ embedded into an Ambient Intelligent infrastructure The DustBot project aims at designing, developing and testing a system for improving the management of urban hygiene based on a network of autonomous and cooperating robots, embedded into an Ambient Intelligence infrastructure. Achim J. Lilienthal 1 The DustBot Scenario Achim J. Lilienthal 1 DustBot Project – DustBot Tasks ] Tasks of the DustBot Robots \ vacuum-cleaning public places \ wash and disinfect the ground \ remove waste from the ground \ collect and transport bin liners to a garbage station Achim J. Lilienthal 1 State of the Art – Cleaning Robots ] State of the Art \ most cleaning robots home/indoor environment (with no big asperity or roughness) Roomba, iRobot Achim J. Lilienthal 1 State of the Art – Cleaning Robots ] State of the Art \ most cleaning robots home/indoor environment (with no big asperity or roughness) \ human-robot interaction problems are not crucial Achim J. Lilienthal 1 DustBot Project – DustBots ] Human-DustBot Interaction \ robots will be equipped with displays to provide information about the air quality, presence of waste on the streets, ... Achim J. Lilienthal 1 State of the Art – Cleaning Robots ] State of the Art \ cleaning robots mostly in a home/indoor environment (with no big asperity or roughness) \ human-robot interaction problems are not crucial \ many automatic machines for outdoor environment exist but they are usually heavy and large or need an operator on board RoboHiter, Subaru Achim J. Lilienthal 1 DustBotProject – DustBots ] Two Types of DustBots \ robots will be equipped with displays to provide information about the air quality, presence of waste on the streets, ... Achim J. Lilienthal 1 DustBotProject – DustBots ] Two Types of DustBots \ cleaning robot [ cleaning pedestrian streets, squares, ... [ brushes and a vacuum-cleaning tool [ environmental sensors (?) \ dust-cart robot [ provides feedback to humans [ characterised by a friendly aspect [ dust cart for bin liner transport + automatic discharge [ environmental sensors \ designed as a modular system Achim J. Lilienthal 1 DustBot Project – DustBot Tasks ] Tasks of the DustBot Robots \ vacuum-cleaning public places \ wash and disinfect the ground \ remove waste from the ground \ collect and transport bin liners to a garbage station \ air quality monitoring and surveillance of pedestrian areas \ communicating pollution levels to pedestrians Achim J. Lilienthal 1 DustBot Environmental Sensors ] Sensors for Monitoring the Air \ real-time monitoring of pollutants (e.g. NOx, SOx, O3, benzene, COx, etc.) Achim J. Lilienthal 1 Pollution Distribution Modelling ] Input \ Concentration measurements for the different analytes (cNOx / cCO / cO3 / cSO2 ...) \ measurements labeled according to the sensor type \ wind measurements (vx, vy, vz) \ timestamp of the measurements \ position of the measurement (x, y, z) \ position uncertainty Achim J. Lilienthal 1 Pollution Distribution Modelling ] Output \ consistent models of the distribution of the analytes \ suggestions for further actions/measurement locations to improve the model Localisation \ estimate of the Module model uncertainty + Known Positions (Stat. Sensors) Pollution Model Suggested Actions Achim J. Lilienthal 1 Pollution Distribution Modelling ] Challenges \ Challenge 1 – Calibration [ measurements labeled with the age of the sensors [ eventual compensation for long-term drift [ may also be used to derive a confidence value for measurements \ Challenge 2 – Compensate for Sensor Dynamics [ determine sensor characteristic [ probabilistic reasoning about the true interaction with the analyte gas Achim J. Lilienthal 1 Pollution Distribution Modelling \ Challenge 3 – Model-free or Model-based Representation? [ model-free • more general, can represent complex distributions [ model-based • may be applied after the model assumptions have been confirmed [ may compensate outliers better \ Challenge 4 – Incorporation of Wind Measurements [ independent model-free wind representation [ model-based gas distribution representation • incorporate average wind direction Achim J. Lilienthal 1 Pollution Distribution Modelling \ Challenge 5 – Model Uncertainty [ measure of local fluctuations (model-free representation) [ from model-free/model-based representation comparison ? \ Challenge 6 – Time-Dependant Representation [ detect significant changes [ recency weighted averaging [ representations at different timescales \ Challenge 7 – Incorporate Position Uncertainty [ use Particle filters for positioning / SLAM [ combine the representations per particle [ incorporate into a fully probabilistic framework Achim J. Lilienthal 1 Pollution Distribution Modelling ] Short-Term Actions 1. state of the art study on stations for pollutants monitoring 2. analysis of the possible pollutants that are interesting in a urban environment 3. study of a more suitable standard that permits the Plug&Play of the sensors 4. outdoor measurements using target chemical sensors Achim J. Lilienthal 1 Pollution Distribution Modelling ] Short-Term Actions 5. gas distribution model [ introduce model uncertainty [ combine with localisation module [ time-dependant representation 6. indoor measurements verifying model under more controlled conditions 7. sensor tests: calibration, long drift Achim J. Lilienthal 1 DustBot Project – DustBot Tasks ] Tasks of the DustBot Robots \ vacuum-cleaning public places \ wash and disinfect the ground \ remove waste from the ground \ collect and transport bin liners to a garbage station \ air quality monitoring and surveillance of pedestrian areas \ communicating pollution levels to pedestrians \ navigation [ GPS, IMU, odometry, laser scanner, ambient cameras, artificial landmarks Achim J. Lilienthal 1 DustBot Project – Challenges ] Dustbot Challenges \ partially unstructured outdoor environment (public places) [ rough surface \ dynamic, shared environment (highly populated areas) \ robots with a selectable level of autonomy \ embedding into an Ambient Intelligence (AmI) \ real-time monitoring of pollutants concentration in air [ distributed sensor system (on-board and AmI sensors) \ human machine interaction [ communicate pollution levels [ interact in a socially acceptable way Achim J. Lilienthal 1 DustBot Project – Expected Results ] Expected Results \ 3 robot prototypes [ 1 cleaning robot and 2 human-friendly dust-cart robots \ localization, navigation, motion planning, obstacle avoidance \ PnP environmental sensor module \ pollution dispersion model \ demonstration activities in Italy, Spain, Sweden \ ... Achim J. Lilienthal 1 DustBot Project – Demonstrations ] Demonstration Activities (5 demos / last 8 months) Achim J. Lilienthal 1 DustBot Risk Management ] Potential Risk Management \ outdoor weather coditions [ careful mechanical design of the robot \ difficulties in the robot locomotion (rough ground / asperity) [ careful mechanical design of the locomotion system \ moving obstacles, complexity of the operative environment [ several sensors with different principles integrated for environment perception and obstacle avoidance \ people safety [ DustBots equipped with acoustic/luminous signals [ ultrasonic sensors, laser, active bumper Achim J. Lilienthal 1 DustBot Risk Management ] Potential Risk Management \ acceptability by the citizens [ well designed human-robot interaction \ robot security [ alarm mechanism (activated by acceleration, temperature, ...) Achim J. Lilienthal 1 DustBot Project – The Role of AASS ] WP2 – “DustBot platform design and specifications” \ defining the system architecture and specifications [ 2 PM: specification of the communication protocols (M2 – M6) ] WP4 – “Robot navigation and obstacle avoidance” \ development of the software for path planning \ sensor system for navigation and obstacle avoidance [ 24 PM: mainly work on the algorithmic side (M7 – M30) ] WP5 – “Distributed env. monitoring sensors” \ implementation of the PnP environmental sensory module [ 24 PM: pollution distribution modelling module (M7 – M34) Achim J. Lilienthal 1 DustBot Project – The Role of AASS ] WP2 – “DustBot platform design and specifications” \ defining the system architecture and specifications [ 2 PM: specification of the communication protocols (M2 – M6) ] WP4 – “Robot navigation and obstacle avoidance” \ development of the software for path planning \ sensor system for navigation and obstacle avoidance [ 24 PM: mainly work on the algorithmic side (M7 – M30) ] WP5 – “DistributedPhD env.position monitoring available!!! sensors”