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2 WIRELESS COMMUNICATION, LOCALIZATION (AND MORE) FOR ROBOTICS PROF. JEROEN HOEBEKE, PROF. BRUNO VOLCKAERT, PROF. PIETER SIMOENS GHENT UNIVERSITY IMEC IDLAB

3 INTERNET & DATA SCIENCE LAB GHENT & ANTWERP 40+ Professors, 40+ Post Docs Total income (projects): 15 M /Y Fundamental: 3 M Strategic: 3,5 M EU projects: 4 M Local industry: 4,5 M

4 IDLAB - RESEARCH FOCUS 3 LAYERS ACT INTELLIGENT THINGS WITH LEARNING CAPABILITIES MONITORING CONTROL OPTIMIZING AUTONOMY DELIVER GUARANTEED NETWORK AND CLOUD SERVICES APPLICATIONS ON SENSORS, GATEWAYS, CLOUDLET AND CLOUD CONNECT ENSURE WIRELESS PERFORMANCE SENSORS, ACTUATORS, CYBER PHYSICAL SYSTEMS, GATEWAYS

5 CONNECT : & DELIVER : WIRELESS COMMUNICATION WIRELESS LOCALIZATION

6 WHY WIRELESS FOR ROBOTICS? WIRED WIRELESS Avoid drilling holes Support systems that can freely move around (ground, air) Localisation Direct interactions with environment (workers, sensors, )

7 WHICH WIRELESS SOLUTION TO PICK? CRITERION: THROUGHPUT AND RANGE (IN AIR) Rate (kbps) Video, 2.4, 5, 60GHz (low-power) control and 2.4GHz Cellular (licensed) (low-power) long range duty-cycle limited 868MHz (also 433Mhz, 169MHz) BAN/PAN LAN MAN/WAN Distance (m)

8 WHICH WIRELESS SOLUTION TO PICK? OTHER CRITERIA Energy Latency (determinism) Topology / infrastructure Reliability / diagnostics Localization capabilities Cost Interference

9 EXAMPLE 1 RELIABLE & ROBUST DRONE COMMUNICATION Reliability / diagnostics Traffic types: control, monitoring, other 1. Real-time diagnostics of wireless communication links II. Advanced communication strategies: Redundancy: combination of different complementary technologies New technologies: LTE unlicensed Traffic handling: classification, priorities 9

10 EXAMPLE 2 A FLEXIBLE AGV NETWORK ARCHITECTURE Topology / infrastructure Broadcast latency of roaming AGV

11 EXAMPLE 2 A FLEXIBLE AGV NETWORK ARCHITECTURE Topology / infrastructure AP MESH REQUIREMENTS Timely deliver broadcast traffic (< 20ms) Deal with mobility Function in absence of infrastructure, exploit when present AGV-to-AGV Handle coverage problems relay via AGV RELAY

12 EXAMPLE 2 A FLEXIBLE AGV NETWORK ARCHITECTURE Topology / infrastructure MIXED MESH/AP MULTI-INTERFACE SOLUTION Fully configurable Low-latency broadcast over mesh Handling coverage problems via meshing < 20ms REQUIREMENTS Timely deliver broadcast traffic (< 20ms) Deal with mobility Function in absence of infrastructure, exploit when present AGV-to-AGV Handle coverage problems relay via AGV Low-latency broadcast traffic

13 EXAMPLE 3 DETERMINISTIC SHUTTLE COMMUNICATION Latency (determinism) CONTINUOUS & RELIABLE WIRELESS COMMUNICATION In a challenging wireless environment and in the presence of continuous mobility. Which technology? Network planning? Determinism? imec.icon HYCOWARE

14 EXAMPLE 3 DETERMINISTIC SHUTTLE COMMUNICATION MEASUREMENT CAMPAIGN TO ASSESS COVERAGE Latency (determinism) imec.icon HYCOWARE

15 EXAMPLE 3 DETERMINISTIC SHUTTLE COMMUNICATION DESIGN OF NOVEL E-BASED ARCHITECTURE Latency (determinism) IEEE e Seamless mobility support and determinism Via smart allocation of schedules imec.icon HYCOWARE

16 EXAMPLE 4 ACCURATE INDUSTRIAL LOCALIZATION Localization capabilities Variety of tests Mobile Tags On-Trolleys & Dynamic Env. Accuracy Test imec.icon HYCOWARE

17 EXAMPLE 4 ACCURATE INDUSTRIAL LOCALIZATION Accuracy Pozyx Dynamicity/mobility Quuppa Localization capabilities On track - Quuppa LOS & Dynamic Environment & Moving Availability % Tag % Tag % Tag % imec.icon HYCOWARE

18 DELIVER & ACT

19 THE INTERNET OF ROBOTIC THINGS WHAT MAKES ROBOTICS HARD? Real-world Let the IoT environment command and assist the robot Controlled environment Let a robot learn instead of executing a program Single task Multiple task

20 EXAMPLE: IOT SENSOR FUSION FOR ROBOT CONTROL MODULAR DEEP REINFORCEMENT LEARNING Off-board LIDAR Off-board On-board OSGI-based middleware for robots, sensors and the cloud Task execution Deep Q-learning Fusion of on-board and offboard sensors Modular approach

21 EXAMPLE: OPEN IOT STANDARDS OPEN IoT STANDARD-BASED Discovery Device and network management Data access and data exchange CONNECTED OPERATOR CONVEYOR SYSTEM SENSOR/ACTUATOR APP LWM2M/IPSO CoAP UDP IPv6/v4 Wi-Fi LWM2M Objects /1 LWM2M Server /3 LWM2M Device /4 Connectivity monitoring /6 Location 2D-SHUTTLE HYBRID TAG for localization /3341 Addressable text display CoMi-based network management LwM2M / IPSO Resources Shuttle Status Shuttle Position Navigation Commands

22 DRONE RESEARCH Reliable software dependable execution in harsh/hostile environments Drone assistance drone assistance in finding and tracking assets of interest Emergency support improve situational awareness through automated decision making support 4 class 1 drone pilots in training <150kg, <90m height Drone networking reliable and robust network communication Drone lab: ~20 drones (3DR, dji, Parrot)

23 EXAMPLE: INDUSTRIAL OPERATOR ASSISTANCE Drone-fleet autonomously tracking labeled goods Planning of drone charging / relieving for uninterrupted view on tracked goods

24 CONCLUSION ROBOTICS IS A MULTI-DISCIPLINARY DOMAIN ACT INTELLIGENT THINGS WITH LEARNING C APABILITIES AUTONOMY OPTIMIZING CONTROL MONITORING DELIVER GUARANTEED NETWORK AND CLOUD SERVICES APPLICATIONS ON SENSORS, GATEWAYS, CLOUDLET AND CLOUD CONNECT ENSURE WIRELESS PERFORMANCE SENSORS, ACTUATORS, CYBER PHYSICAL SYSTEMS, GATEWAYS Collaborations w.r.t. robotic hardware (sensors, actuators, mechanics, etc.), domain knowledge, user interfaces, etc.