Anticipation enhanced behavior-based robotics using integrated system dynamics

2017 Spring.Includes bibliographical references.Behavior-based robotics specifies behavior as the interaction between the task, environment, and agent with specific capabilities that creates a successful behavior to attain task achievement. Observed task achieving behavior is confirmed and validated by a prespecified performance criteria. For behavior-based robotics, conditions in the niche environment are directly matched to and cue behavior choice that yields task achievement by the robot agent. A minimalist approach attains this behavior choice from only a few possible scenarios for the niche environment and a simple associated response. Previous work in behavior-based robotics has been generally limited to a reactive response to environmental conditions, with little or no notion of looking ahead to potential successful future outcomes. Focus on the notion of anticipation provides a novel addition to the task achieving behavior-based robotics approach. Anticipation is the formulation of suitable processes to manifest behavior from a small set of feasible scenarios in the near future before the outcome is certain. Anticipation results in successful behavior beyond mere reactions to niche conditions that leads to desired task achievement with expected perceived immediate or later reward based on suitable fitness matched to the niche. The approach to add anticipation developed a formal system dynamics model to represent previously known behavior archetypes, extended them with the notion of anticipation, and enhanced the system dynamics operation. Simulation of a robot instance using anticipation for wall following, called the TOURIST, was conducted to gain insight into behaviors that would be observable in a real world natural system. Simulation of the TOURIST robot with anticipation built into the archetype programming illustrated the advantages of including the notion of anticipation. Anticipation allows a TOURIST robot agent to travel a smoother path and make choice of small increments in behavior change that produce more desired longer term responses. With anticipation, numerous small adjustments are made that require less energy than large spins of the SEEK behavior, so only one third of the SEEK behaviors occur, and thus wastes less energy and time. Also with anticipation, the TOURIST makes twice as many cycles of the area at the same speed and in the same time, so a broader range of area is covered and can more readily perceive any dynamic changes in the overall arena. The methods and insights were added to a real world robot instance, and the benefits of anticipation were observed to occur. A specific metric, ANNum, was developed for describing operation of the TOURIST robot. Greater metric values were found with anticipation on, reflecting more behavior responsiveness to the niche per unit time when anticipation was used. In conclusion, anticipation enhances robotic performance by manifesting task achieving behavior that is properly matched to a specific niche condition. Anticipation extends beyond the merely reactive behavior previously used in behavior-based robotics by acting like a funnel or channel to guide the behavior choice to match a specific niche. The observed behavior choice is manifest before the outcome is realized and certain to occur. As a practical result, the robot agent is able to make many smaller adjustments earlier and faster with better chance for desired outcome than would be observed without anticipation. It circumvents repeated larger adjustments that waste more resources and take more time for task achievement. Such enhanced anticipation behavior avoids obstructions and potential destructive paths or motion, and is more able to achieve tasks such as to find objects and move along walls with minimal effort. Thus, anticipation that is added to robot architecture improves behavior choices to realize desired task achievement. Future work could add anticipation to real world practical automation and robotics to further test the improved operation with anticipation. In summary, anticipation observed in a robot agent should act before the outcome is known, make timely small adjustments toward a goal, and appear as if the future were known ahead of time