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Autonomous Mobile Robots and Intelligent Control Issues Sven Seeland
Autonomous Mobile Robots and Intelligent Control Issues
Sven Seeland
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History and Rules● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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Mobile Robots – Motivations
● Can work under hostile environmental conditions
● Can move in confined spaces
● Expendable in dangerous situations
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Autonomy – Definition 1
“Autonomy refers to systems capable of operating in the real-world environment without any form of
external control for extended periods of time.”George A. Bekey, Autonomous Robots: from biological inspiration to implementation and control
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Autonomy – Definition 2
A fully autonomous robot has the ability to
● Gain information about the environment.● Work for an extended period without human intervention.● Move either all or part of itself throughout its operating
environment without human assistance.● Avoid situations that are harmful to people, property, or itself
unless those are part of its design specifications.
- Wikipedia, “Autonomous robot”
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Autonomy – Motivations
● Remote control might be infeasible
● Area too large or cluttered for wired control● Poor wireless reception
● No operator required
● Cheap operation of many units● No set working hours● No fatigue
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Autonomous Mobile Robots – Applications
● Clearing an area of landmines, bombs and other explosives
● Rescue robots
● Service Robots
● Maintenance Robots
● Exploration
● Toys
● Automated Driving
● ...
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Autonomous Cars – Motivation
● Public Transport
● Safer Driving
● More comfortable traveling
● Delivery Tasks
● ...
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History and Rules● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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Autonomous Cars – History
● PROMETHEUS Project (1989-1995)
● Initiated by the European Commission● PROgraMme for a European Traffic of Highest
Efficiency and Unprecedented Safety● $1 billion funding● Most prominent results where VaMP and ARGO
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Autonomous Cars – History
● VaMP (1995)
● Versuchsfahrzeug für autonome Mobilität PKW● >2000 km from Munich to Kopenhagen and back in
normal traffic● Up to 180 km/h● Up to 158 km without human intervention● Mean distance between human interventions: 9 km● Lane changes● Vehicle passing● Active computer vision● Radar
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Autonomous Cars – History
● ARGO Project (1998)
● 2000 km Tour through Italy● Above 90% of the time in automatic mode● Longest distance without intervention: 54.3 km● Two cameras● 200 MHz Pentium MMX
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History and Rules● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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DARPA Grand Challenge - History
● Motivation: Make one-third of ground military forces autonomous by 2015
● Off-road tracks
● 2004:
● 241 km
● $1 Million prize money
● no winner
● Best vehicle travelled 11,78 km
● 2005:
● 213 km
● $2 Million prize money
● 5 vehicles succeed
● All but one got past the maximum distance of 2004
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DARPA Urban Challenge 2007 – Rules 1
● $2 Million, $1 Million and $500.000 prizes
● Complete 60 miles in 6 hours to finish the race
● Urban environment
● Decommissioned Air Force Base● Street network in residential area● Several dirt roads
● Obey traffic laws
● All cars on the course at the same time
● 3 individual missions per car
● No pedestrians or other moving objects
● Time penalties for dangerous or erroneous behavior
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DARPA Urban Challenge 2007 – Rules 2
● One Route Network Definition File (RNDF)
● Handed out 24 hours before the race● Similar to maps used in GPS navigation systems● Contains road positions, number of lanes, intersections,
parking space locations in GPS coordinates● One Mission Description File per Team and Mission (MDF)
● Handed out on the day of the event● Contains a list of checkpoints from the RNDF that the
vehicle needs to cross
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History● Rules
● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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MIT Talos
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MIT Talos – Design Considerations
● Many low-cost sensors
● Increases perception robustness● More complete coverage● Higher efficiency in a multi-processor environment
● Minimal reliance on GPS data
● Highly distributed computer
● Better reaction times● Downside: higher power consumption
● Simple low level controls
● Improve robustness● Minimal sensor fusion / asynchronous sensor update
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MIT Talos – Specifications
● Land Rover LR3
● Human drivable
● EMC AEVIT drive-by-wire system
● 6000 Watts power generator
● 2 ruggedized UPS
● Blade Cluster (10 x 4 64-bit CPUs, 2.3 GHz each)
● Velodyne HDL-64 LIDAR (3D)
● 12 SICK LIDARs (2D)
● 5 cameras (752x480, 22.8 images per second)
● 15 millimeter wave radars
● Applanix navigation solution (GPS, inertial measurement unit and wheel encoder)
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History● Rules
● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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Intelligent Control Systems – Tasks
● Actuation● Collision avoidance● Path-finding / Trajectory planning● Mission planning● Localization
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Intelligent Control Systems – Challenges
● Uncertainty
● Dynamic environment● Perception● Actuation
● Efficiency
● Short reaction times in a dynamic environment● Limited processing power due to limited space on the
moving platform● Scalability
● Potentially huge environment● Potentially long operating times
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Intelligent Control Systems – Requirements
● Robustness
● Input is likely to be inaccurate, incomplete or wrong● Unforeseen conditions are likely to occur
● Speed
● Quickly react to situations● Initial assumptions may be invalid by the time the
deliberation process is finished● Versatility
● Multitude of tasks need to be executed simultaneously● Highly diverse nature of tasks
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Intelligent Control Systems – Basics
● Control Systems consist of:
● Input● Controller● Output
Input OutputController
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Intelligent Control Systems – Reactive Systems
● Purely reactive systems:
● No planning or learning
● No internal state
● Complexity of tasks is limited
● Highly robust
● Very quick reaction times
World
Perception Decision Action
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Intelligent Control Systems – Deliberative Systems
● Deliberative Systems
● Allows for planning and learning
● Internal world model
● Can perform very complex tasks
● Not very robust
● Slow
Programming
Perception
Knowledge DecisionsReasoning
Actions
World
Model Building Decision Making
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Intelligent Control Systems – Hybrid Systems 1
● Hybrid Systems
● Combination of systems
● Reactive systems for short term reactions and low level controls
● Deliberative systems for planning and coordination
Programming
Perception
Knowledge DecisionsReasoning
Actions
World
Model Building Decision Making
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Intelligent Control Systems – Hybrid Systems
● Oftentimes organized in three layers
● Planning layer handles long time action plans
● Sequencing divides long term goals into smaller steps
● Controlling translates those steps into actual actuator commands
● Layers operate in parallel and independently
● Low layers can fail and report failure to higher layers
● Higher layers tend to use deliberative approaches
● Lower layers tend to use reactive approaches
Planning
Sequencing
Controlling
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Overview
● Introduction
● Motivation● History of Autonomous Cars
● DARPA Grand Challenge
● History● Rules
● Controlling Autonomous Cars
● MIT Talos Overview● Intelligent Control Systems● Controlling Talos
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MIT Talos – Control System Architecture
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MIT Talos – IPC Infrastructure
● LCM – lightweight communications and marshaling
● Minimalist system for real-time applications
● Developed specifically for Talos
● Based on UDP-Multicast
● Publish/subscribe message-passing model
● Logging made extremely easy
● Freely available
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MIT Talos – Control System Architecture
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MIT Talos – Navigator 1
● Highest level of abstraction
● General planning component● Route planning
● Intersection handling (precedence, crossing, merging)
● Passing
● Blockage replanning
● Turn signaling
● Failsafe timers
● Inputs: MDF, lane information, vehicle pose
● Outputs: goals for motion planner● Short term goals within 40-50m range
● Goal is moved according to the high level intentions
● Timing of the goal-setting used to control motion
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MIT Talos – Navigator 2
● Reevaluates situation at 2 Hz● Dynamic replanning comes “for free”
● Passing● Goal remains unchanged
● Checks if other lane exists and is free
● Allows the motion planner to use the other lane
● Two timers for global problem solving:● Failsafe timer
– Progressively sets and unsets global failsafe states
– Failsafe states progressively relax security constraints● Blockage time
– Determines traffic jams and roadblocks
– Only works in two-lane roads where a u-turn is possible
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MIT Talos – Control System Architecture
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MIT Talos – Drivability Map 1
● Interface to perceptual data
● Influenced by the failsafe states set by the navigator
● Input: Sensory data
● Output: A map, indicating the feasibility of certain paths for the motion planner
● Contains:● Infeasible regions
● Restricted regions
● High cost regions
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MIT Talos – Drivability Map 2
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MIT Talos – Control System Architecture
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MIT Talos – Motion Planner
● Short term path planning
● Input: RNDF goals and situational data from the Navigator, Drivability Map
● Output: Path and Speed commands for the Controller
● Output is sent at 10 Hz
● Rapidly-exploring Random Tree● Generate semi-random waypoints
● Iterate over those waypoints
● Generate a trajectory using closed-loop dynamics
● Check the trajectory for feasibility
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MIT Talos – Control System Architecture
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MIT Talos - Controller
● Controls the vehicle
● Generates gas, brake, steering and gearshift commands
● Two Controllers
● Pure-Pursuit controller for steering● Two different controllers for forward and reverse
steering● Proportional-Integral controller for speed● Steering lookahead is based on current commanded
speed● Commanded speed is based on vehicle location
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References● Leonard, J., How, J., Teller, S. et al. A perception-driven autonomous urban vehicle. In: Journal
of Field Robotics, 25 (2008) Nr. 10, p. 727-774
● DARPA, DARPA Urban Challenge Website, http://www.darpa.mil/grandchallenge/index.asp (2007)
● Team MIT (2007), Technical Report – DARPA Urban Challenge, http://www.darpa.mil/grandchallenge/TechPapers/MIT.pdf (2007)
● Stenzel, R.: Steuerungsarchitekturen für autonome mobile Roboter, Aachen, RWTH, Dissertation, 2002.
● Wikipedia: DARPA Grand Challenge. (2009, December 30) http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
● Wikipedia: Autonome mobile Roboter. (2009, November 21) http://de.wikipedia.org/wiki/Autonome_mobile_Roboter
● Wikipedia: VaMP. (2009, December 14) http://en.wikipedia.org/wiki/VaMP
● ARGO Project Homepage, http://www.argo.ce.unipr.it/ARGO/english/
● Univ.-Prof. Dr.-Ing. Ernst Dieter Dickmanns, Forschungsbericht 1.10.1998 bis 30.9.2002, http://www.unibw.de/rz/dokumente/public/getFILE?fid=bs_999528 (2002)
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Thank you for your attention!
