SSS: A Hybrid Architecture Applied to Robot Navigation Jonathan H. Connell IBM T.J. Watson Research Center Review Paper By Kai Xu What’s this?

SSS: Servo, Subsumption, Symbolic –Servo: Control component –Subsumption: Deal with Multi-task –Symbolic: complicated task, like routing/building internal model

Background conventional servo-systems(SPA) –Inner model is not accurate enough –Only good for simple tasks multi-agent reactive controllers –i.e. subsumption, behavior-based system –Fast response, but slow sample rate, discrete space state-based symbolic AI systems ? Can we combine them?

Architecture of SSS

Components Servo Layer –continuous time and continuous space Behavior-based Layer –Has special purpose recognizers –Response to certain situation Symbolic Layer –Triggered by certain events and then response

Interface Servo and Behavior-based Layer –Command interface setpoints –Sensor Interface Matched filter Behavior-based and Symbolic Layer –Command interface Turn on/off, Parameterize modules –Sensor Interface Event triggering Contingency table

Task of Robot Map the environment Navigate itself during moving(self-adaptibility) –compensating for the variability of the environment Solution: use coarse map –Avoid loop: knowing when the robot has arrived back in a place it has been before Solution: memorize the location

Tactical Navigation Implemented in Servo&Subsumption layer servo layer – consists of two velocity controllers, one for translation and one for rotation –Wall-following Subsumption layer –two tactical navigation modules based on odometry. Slows or stops the robot when approaching the destination Control the average heading of the robot

Strategic Navigation Implemented in Symbolic Layer Maintaining a coarse map –Landmarks: for IR to detect in long distance –Paths and their relation&length: For routing, f.g. spreading activation algorithm Not happened very often, only when some event happen, f.g. reach an intersection

An Item in Contingency table (do-until-return –(setq recognizers (check-situations)) –(cond ( – (and (near-distance? recognizers) (aligned-okay? recognizers) (left-opening? recognizers) ) (inc-heading! 90) (new-travel! 564) (return recognizers)) –((beyond-distance? recognizers) (inc-heading! 180) (new-travel! 48) (return recognizers)) –((no-progress? recognizers) (disable! stay-aligned) (enable! scan-for-escape) (return recognizers)) –(t nil)))

Experiment What might happen to the sensor So What to do?

Experimental results What is felt by robot What is built in mind

Solution Using landmarks –Locate itself dynamically –Avoid errors accumulation –Reduce the requirement of accuracy and stability of the lower layers more flexible Don’t need precise time or speed control Don’t need precise motor or steer control

Comparison ATLANTIS & AuRA –Similarities: three layers with increasing deliberative abilities Interface between layers –Differences No global objective in subsumption layer in SSS No internal state in subsumption layer in SSS Symbolic still paticipate the control system directly, but only for those important events

Conclusion Advantages: –Sensor the world dynamically, more accurate –Make use of landmark, more stable –Finish the task by a coarse map, more practical Problems: –Highly rely on Landmarks –Need prior knowledge

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