Autonomous Mobile Robots CPE 470/670 Lecture 8 Instructor: Monica Nicolescu

CPE 470/670 - Lecture 82 Review Control Architectures Languages for robot control Computability Organizing principles Architecture selection criteria Reactive control

CPE 470/670 - Lecture 83 Reactive Control Reactive control is based on tight (feedback) loops connecting a robot's sensors with its effectors Purely reactive systems do not use any internal representations of the environment, and do not look ahead –They work on a short time-scale and react to the current sensory information Reactive systems use minimal, if any, state information

CPE 470/670 - Lecture 84 Collections of Rules Reactive systems consist of collections of reactive rules that map specific situations to specific actions Analog to stimulus-response, reflexes –Bypassing the “brain” allows reflexes to be very fast Rules are running concurrently and in parallel Situations –Are extracted directly from sensory input Actions –Are the responses of the system (behaviors)

CPE 470/670 - Lecture 85 Mutually Exclusive Situations If the set of situations is mutually exclusive:  only one situation can be met at a given time  only one action can be activated Often is difficult to split up the situations this way To have mutually exclusive situations the controller must encode rules for all possible sensory combinations, from all sensors This space grows exponentially with the number of sensors

CPE 470/670 - Lecture 86 Complete Control Space The entire state space of the robot consists of all possible combinations of the internal and external states A complete mapping from these states to actions is needed such that the robot can respond to all possibilities This is would be a tedious job and would result in a very large look-up table that takes a long time to search Reactive systems use parallel concurrent reactive rules  parallel architecture, multi-tasking

CPE 470/670 - Lecture 87 Incomplete Mappings In general, complete mappings are not used in hand- designed reactive systems The most important situations are trigger the appropriate reactions Default responses are used to cover all other cases E.g.: a reactive safe-navigation controller If left whisker bent then turn right If right whisker bent then turn left If both whiskers bent then back up and turn left Otherwise, keep going

CPE 470/670 - Lecture 88 Example – Safe Navigation A robot with 12 sonar sensors, all around the robot Divide the sonar range into two zones –Danger zone: things too close –Safe zone: reasonable distance to objects if minimum sonars 1, 2, 3, 12 < danger-zone and not-stopped then stop if minimum sonars 1, 2, 3, 12 < danger-zone and stopped then move backward otherwise move forward This controller does not look at the side sonars

CPE 470/670 - Lecture 89 Example – Safe Navigation For dynamic environments, add another layer if sonar 11 or 12 < safe-zone and sonar 1 or 2 < safe-zone then turn right if sonar 3 or 4 < safe-zone then turn left The robot turns away from the obstacles before getting too close The combinations of the two controllers above  collision-free wandering behavior Above we had mutually-exclusive conditions

CPE 470/670 - Lecture 810 Action Selection In most cases the rules are not triggered by unique mutually-exclusive conditions –More than one rule can be triggered at the same time –Two or more different commands are sent to the actuators!! Deciding which action to take is called action selection Arbitration: decide among multiple actions or behaviors Fusion: combine multiple actions to produce a single command

CPE 470/670 - Lecture 811 Arbitration There are many different types of arbitration Arbitration can be done based on: a fixed priority hierarchy –rules have pre-assigned priorities a dynamic hierarchy –rules priorities change at run-time learning –rule priorities may be initialized and are learned at run- time, once or continuously

CPE 470/670 - Lecture 812 Multi-Tasking Arbitration decides which one action to execute To respond to any rule that might become triggered all rules have to be monitored in parallel, and concurrently If no obstacle in front  move forward If obstacle in front  stop and turn away Wait for 30 seconds, then turn in a random direction Monitoring sensors in sequence may lead to missing important events, or failing to react in real time Reactive systems must support parallelism –The underlying programming language must have multi- tasking abilities

CPE 470/670 - Lecture 813 Designing Reactive Systems How to can we put together multiple (large number) of rules to produce effective, reliable and goal directed behavior? How do we organize a reactive controller in a principled way? The best known reactive architecture is the Subsumption Architecture (Rod Brooks, MIT, 1985)

CPE 470/670 - Lecture 814 Vertical v. Horizontal Systems Traditional (SPA): sense – plan – act Subsumption:

CPE 470/670 - Lecture 815 Biological Inspiration The inspiration behind the Subsumption Architecture is the evolutionary process : –New competencies are introduced based on existing ones Complete creatures are not thrown out and new ones created from scratch –Instead, solid, useful substrates are used to build up to more complex capabilities

CPE 470/670 - Lecture 816 The Subsumption Architecture Principles of design –systems are built from the bottom up –components are task-achieving actions/behaviors (avoid-obstacles, find-doors, visit-rooms) –components are organized in layers, from the bottom up –lowest layers handle most basic tasks –all rules can be executed in parallel, not in a sequence –newly added components and layers exploit the existing ones

CPE 470/670 - Lecture 817 Subsumption Layers First, we design, implement and debug layer 0 Next, we design layer 1 –When layer 1 is designed, layer 0 is taken into consideration and utilized, its existence is subsumed (thus the name of the architecture) –As layer 1 is added, layer 0 continues to function Continue designing layers, until the desired task is achieved level 2 level 1 level 0 sensorsactuators

CPE 470/670 - Lecture 818 Suppression and Inhibition Higher layers can disable the ones below –Avoid-obstacles can stop the robot from moving around Layer 2 can either: –Inhibit the output of level 1 or –Suppress the input of level 1 The process is continued all the way to the top level AFSM inputsoutputs suppressor inhibitor I s level 2 level 1 level 0 sensorsactuators

CPE 470/670 - Lecture 819 Subsumption Language and AFSMs The original Subsumption Architecture was implemented using the Subsumption Language It was based on finite state machines (FSMs) augmented with a very small amount of state (AFSMs) AFSMs were implemented in Lisp AFSM inputsoutputs suppressor inhibitor I s

CPE 470/670 - Lecture 820 Subsumption Language and AFSMs Each behavior is represented as an augmented finite state machine (AFSMs) Stimulus (input) or response (output) can be inhibited or suppressed by other active behaviors An AFSM can be in one state at a time, can receive one or more inputs, and send one or more outputs AFSMs are connected communication wires, which pass input and output messages between them; only the last message is kept AFSMs run asynchronously AFSM inputsoutputs suppressor inhibitor I s collide halt sonar

CPE 470/670 - Lecture 821 Networks of AFSMs Layers represent task achieving behaviors –Wandering, avoidance, goal seeking Layers work concurrently and asynchronously A Subsumption Architecture controller, using the AFSM-based programming language, is a network of AFSMs divided into layers Convenient for incremental system design level 2 level 1 level 0

CPE 470/670 - Lecture 822 Wandering in Subsumption Brooks ‘87

CPE 470/670 - Lecture 823 Layering in AFSM Networks Layers modularize the reactive system Bad design: –putting a lot of behaviors within a single layer –putting a large number of connections between the layers, so that they are strongly coupled Strong coupling implies dependence between modules, which violates the modularity of the system If modules are interdependent, they are not as robust to failure In Subsumption, if higher layers fail, the lower ones remain unaffected

CPE 470/670 - Lecture 824 Module Independence Subsumption has one-way independence between layers –With upward independence, a higher layer can always use a lower one by using suppression and inhibition Two-way independence is not practical –No communication between layers is possible Do we always have to use these wires to communicate between parts of the system?

CPE 470/670 - Lecture 825 Using the World How can you sequence activities in Subsumption? Coupling between layers need not be through the system itself (i.e., not through explicit communication wires) It could be through the world. How?

CPE 470/670 - Lecture 826 Collecting Soda Cans Herbert collected empty soda cans and took them home Herbert’s capabilities –Move around without running into obstacles –Detect soda cans using a camera and a laser –An arm that could: extend, sense if there is a can in the gripper, close the gripper, tuck the arm in

CPE 470/670 - Lecture 827 Herbert Look for soda cans, when seeing one approach it When close, extend the arm toward the soda can If the gripper sensors detect something close the gripper If can is heavy, put it down, otherwise pick it up If gripper was closed tuck the arm in and head home The robot did not keep internal state about what it had just done and what it should do next: it just sensed!

CPE 470/670 - Lecture 828 More on Herbert There is no internal wire between the layers that achieve can finding, grabbing, arm tucking, and going home However, the events are all executed in proper sequence. Why? Because the relevant parts of the control system interact and activate each other through sensing the world

CPE 470/670 - Lecture 829 World as the Best Model This is a key principle of reactive systems & Subsumption Architecture: –Use the world as its own best model! If the world can provide the information directly (through sensing), it is best to get it that way, than to store it internally in a representation (which may be large, slow, expensive, and outdated)

CPE 470/670 - Lecture 830 Subsumption System Design What makes a Subsumption Layer, what should go where? There is no strict recipe, but some solutions are better than others, and most are derived empirically How exactly layers are split up depends on the specifics of the robot, the environment, and the task

CPE 470/670 - Lecture 831 Designing in Subsumption Qualitatively specify the overall behavior needed for the task Decompose that into specific and independent behaviors (layers) Determine behavior granularity Ground low-level behaviors in the robot’s sensors and effectors Incrementally build, test, and add

CPE 470/670 - Lecture 832 Genghis (MIT) Walk over rough terrain and follow a human (Brooks ’89) –Standup Control leg’s swing position and lift –Simple walk –Force balancing Force sensors provide information about the ground profile –Leg lifting: step over obstacles –Obstacle avoidance (whiskers) –Pitch stabilization –Prowling –Steered prowling

CPE 470/670 - Lecture 833 The Nerd Herd (MIT) Foraging example (Matarić ’93) –R1 robots (IS robotics) Behaviors involved: –Wandering –Avoiding –Pickup –Homing

CPE 470/670 - Lecture 834 Tom and Jerry (MIT) fsdf Tom Jerry

CPE 470/670 - Lecture 835 Pros and Cons Some critics consider the lack of detail about designing layers to be a weakness of the approach Others feel it is a strength, allowing for innovation and creativity Subsumption has been used on a vast variety of effective implemented robotic systems It was the first architecture to demonstrate many working robots

CPE 470/670 - Lecture 836 Benefits of Subsumption Systems are designed incrementally –Avoid design problems due to the complexity of the task –Helps the design and debugging process Robustness –If higher levels fail, the lower ones continue unaffected Modularity –Each “competency” is included into a separate layer, thus making the system manageable to design and maintain –Rules and layers can be reused on different robots and for different tasks

CPE 470/670 - Lecture 837 Readings F. Martin: Chapter 5 M. Matarić: Chapter 10