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  • Advanced Robotics: Autonomous Mobile Robots

    Arshad Jamal,

    Scientist, Intelligent Systems and Robotics Division Centre for AI & Robotics, DRDO

    Bengaluru

  • 1. Motivation 2. History and Current Scenario 3. Technologies for Autonomous Mobile

    Robots 4. Capability requirements 5. Robotic Systems developed by CAIR

    a. Systems with different mobility b. Semi-autonomous mobile robots c. Autonomous mobile robots d. GPS-less autonomous navigation

    i. SLAM algorithms ii. Autonomous search robot

    Outline

  • Why Autonomous Robotics Systems?

    1. First response, Surveillance and Reconnaissance, Patrolling

    2. IED handling, UXO handling, Mine laying and breaching

    3. Communication relays, Logistics transport 4. Convoy protection, Road clearance 5. Target Identification and Tracking, Remotely

    operated weapons 6. Disaster management

  • History of Autonomous Vehicles 1977: Tsukuba Mechanical Engineering Lab in Japan creates the first autonomous,

    intelligent, vehicle. It tracked white street markers and achieved speeds up to 30 km/h.

    1987-1995: The pan-European Prometheus project, also known as the EUREKA

    Prometheus Project, the largest autonomous vehicle project so far, is funded by the European Commission.

    1997: Demo '97 in San Diego, California, in which about 20 automated vehicles,

    including cars, buses, and trucks, were demonstrated 2000s: Several DARPA challenges made significant contributions in development of

    autonomous vehicles. Google starts work on its driverless car in 2009 2010s: All major automobile companies are working on driverless autonomous

    cars

  • Major Research Programme for Autonomous Vehicle

    DARPA Grand Challenge 2005 Autonomous driving through 150 miles of

    desert terrain in less than 10 hours Winner: Stanford Racing Team (6:54 hrs),

    Stanford University

    DARPA Urban Challenge 2007 Autonomous driving through 60 miles in an

    urban environment in less than 6 hours Winner: Tartan Racing (4:10 hrs), Carnegie

    Mellon University

    DARPA Robotics Challenge 2014 Humanoid robots in disaster management

    Enter, drive and exit a vehicle, Clear obstacles and open a door, Climb a ladder, Turn off a valve, Attach a hose

  • Global Scenario - Military Light weight man-portable to heavy duty systems

    Scouting operations, intelligence, surveillance and reconnaissance, IED handling, logistics support, mine clearance

    Dragon runner Viper PackBot Talon

    MULE Gaurdium Big Dog Panther

  • Technologies for Autonomous Mobile Robots

    Mobility

    Power Communication

    Human-Robot Interaction

    Localization

    Perception Planning

    Navigation

    Learning/ Adaptation

    Enabling Technologies

    Autonomous Behaviour

  • 1. Perception a. Multi-modal environment sensing and data fusion

    Vision (EO, IR), ranging (LIDAR, RADAR, SONAR), tactile (haptic) b. Highly influenced by operational environment and platform

    embodiment 2. Knowledge representation

    a. Ontologies Describes entities and their relationships

    b. Enables abstraction of concepts and inference c. Provides the bridge between perception and machine reasoning

    3. Reasoning a. What-If scenario modeling - Projection of actions and consequences

    into the future in a given context b. Highly dependent on embodiment c. Enables high level behaviours

    Collaboration

    Key Capabilities for Unmanned Systems

  • 4. Planning a. Decomposition of a mission specification into specific tasks

    Considers available platforms and capabilities vis--vis mission requirements non-trivial!

    b. Requires semantic characterization of capabilities c. Requires reasoning to infer that a composition of capabilities can

    meet a mission requirement d. Scheduling, monitoring and re-planning in context e. Key enabler for effective collaboration

    5. Learning a. The defining principle of intelligence! b. Enables high-level behaviours

    improvisation, adaptation, cunning, strategy 6. Self-monitoring for effective immunity from external infections

    a. Guard against internal corruption in the system b. Immunization against adversarial take-over c. Identify the right set of security requirements starting from policy to

    architecture to models to mechanisms; more than just encryption!

    Key Capabilities of Unmanned Systems

  • 1. Consists of three repeated steps a. Sense your

    environment b. Plan what to do next

    by building a world model through sensor fusion and taking all goals (both short term and long term) into account

    c. Execute the plan through actuators

    Key Capabilities: Sense-Plan-Act Paradigm for Autonomous Systems

    Sensory Inputs

    Actuation

  • Intelligent Robotics Systems Developed at CAIR

  • Technology Focus Areas Mobility

    Leg & Wheel Legged Serpentine Wall Climbing Tracked

    Autonomous Navigation

    Robot Sentry Wheeled Vehicle Tracked Vehicle

    Flapping Quad-rotor

    Manipulation

    Hot slug handling, NMRL

    Inspection of Composites, HAL

    Steam generator inspection, NPCIL

    Educational Manipulators arms

    Mobile Manipulator

    Perception

    Change Detection Fusion Tracking SLAM

  • 1. Suspension with linkage mechanism a. Six actuated wheels b. Parallel bogies in center c. Fork suspension in front d. Step climbing capability upto

    1.5 times wheel diameter

    2. Suspension with Spring-Damper a. Six actuated wheels b. Lower vibration c. Suspension in both roll and

    pitch

    Wheeled Locomotion- Passive Suspension

  • 1. Requirements a. Stair climbing b. Self righting c. High ground clearance

    2. Multi-segment tracked robot (MiniUGV) a. Main tracks in center b. Tracked flippers in

    front and rear with endless rotation

    c. Camera in front and rear

    d. Remote operation with 160m NLOS range

    e. 3 hrs of endurance f. 50 Kg payload

    capacity on flat terrain

    Tracked Locomotion

  • 1. Hexapod a. Cockroach type

    i. 6 legs with 2 DoF b. Crab type

    i. 6 legs with 3 DoF ii. Joint level leg control (Angles)

    c. Omni-hex i. 6 legs with 3 DoF ii. Cartesian level leg control (X, Y, Z) iii. Ultrasonic Sensors for obstacle

    avoidance

    Legged Locomotion

  • 1. Quadruped a. No static stability b. Lateral movement of

    body can shift the CG to stable location

    c. 3-DoF Leg design with extra Hip joint

    d. Gaits i. Crawl: one leg at a

    time with body sway ii. Trot: diagonal legs

    at a time at higher speed

    Legged Locomotion

  • 1. Design a. 2-DoF Joints with alternate

    horizontal and vertical joints b. Free wheels to emulate differential

    friction 2. Combination of travelling waves

    along horizontal and vertical plane generate

    a. Lateral undulation b. Caterpillar gait c. Side-winding gait d. Rolling gait

    Serpentine Locomotion

    Transverse wave propagates along the body Differential Friction results in forward motion

    Wave travel

    Body displacement

  • 1) Low pressure adhesion 1. Impeller to generate vacuum for

    sticking to wall a. Suction force large enough

    to offset the robot weight b. Suction force Small enough

    to enable locomotion 2. Differential tracks for motion 3. Camera on 2-DoF arm

    2) Electro-adhesion based - Adhesion due to electrostatic

    force - Noiseless operation - High endurance

    Wall Climbing

  • Semi-autonomous Mobile Robot

  • 1. Autonomous Navigation a. with a-priori map

    2. Continuous Video Feed a. via Pan-Tilt-Zoom Network

    Color Camera 3. GPS and Stabilized Digital

    Compass a. for Localization

    4. Scanning Laser Range Finder a. for Obstacle Avoidance

    5. WiFi Link a. for Command & Control

    Semi-Autonomous Navigation Sentry Robot

    Current Pos/Head Desired Pos Laser range data

  • Autonomous Mobile Robot

  • 1. Unstructured environment 2. No predefined map is available 3. Only coarse waypoints may be available 4. Day and night operation 5. Modular, scalable and extensible hardware and

    software architecture

    Autonomous Tracked Platform - Scope

  • Hardware Architecture

    Drive-by-wire system interface

    Vehicle speed and heading controller

    Steering actuation Throttle actuation

  • Topological Architecture

    21-03-2016 24

    Goal Specification

    Vehicle Drive-by-Wire

    Control

    GPS

    INS

    Odometer

    Localization Data Fusion

    LOCALIZATION PERCEPTION

    Monocular Camera

    Stereo Camera LIDAR RADAR

    Perception Data Fusion

    Global path-planning

    Local path-planning PATH PLANNING

  • Localization

    50 Hz

    Obstacle map

    Occupancy map

    Perception

    10 Hz 15 Hz 4 Hz

    5 Hz

    Goal specification

    Global path

    planning

    Local path planning

    Path planning

    0.5 Hz 10 Hz

    Vehicle control

    Navigation 20 Hz

    Autonomous Navigation System

  • Goal specification

    Global path

    planning

    Local path planning

    Path planning

    0.5 Hz 10 Hz

    Localization

    50 Hz

    Obstacle map

    Occupancy map

    Perception

    10 Hz 15 Hz 4 Hz

    5 Hz

    Vehicle control

    Navigation 20 Hz

    Sensor interface for data acquisition Vehicle pose (position and att

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