Using a new hardware implementation of our designs for tunably compliant spine-like tensegrity robots, we show that the NASA Tensegrity Robotics Toolkit can effectively generate and predict desirable locomotion strategies for these many degree of freedom systems. Tensegrity, which provides structural integrity through a tension network, shows promise as a design strategy for more compliant robots capable of interaction with rugged environments, such as a tensegrity interplanetary probe prototype surviving multi-story drops. Due to the complexity of tensegrity structures, modeling through physics simulation and machine learning improves our ability to design and evaluate new structures and their controllers in a dynamic environment. The kinematics of our simulator, the open source NASA Tensegrity Robotics Toolkit, have been previously validated within 1.3% error on position through motion capture of the six strut robot ReCTeR. This paper provides additional validation of the dynamics through the direct comparison of the simulator to forces experienced by the latest version of the Tetraspine robot. These results give us confidence in our strategy of using tensegrity to impart future robotic systems with properties similar to biological systems such as increased flexibility, power, and mobility in extreme terrains.

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Our results show an improvement in NPV for PV-battery systems using the price signals dispatch algorithm described in this paper and quantifies the tradeoffs between three heuristic dispatch algorithms. For the case study, the price signals dispatch algorithm achieved additional utility bill savings over the peak shaving algorithm via savings on energy charges, while allowing a higher average monthly demand charge. To maximize value for a system, the best choice of dispatch algorithm within SAM depends on the utility rate structure. If a high TOU ratio is present with minimal demand charges, manual dispatch is a good choice. Peak shaving performs well for reducing demand charges. Price signals dispatch performs best in cases requiring a balance between these two revenue streams, or cases when battery replacements would be a significant cost.The economic potential of a behind-themeter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

Animals such as cockroaches depend on exploration of unknown environments, and their strategies may inspire robotic approaches. We have previously shown that cockroach behavior, with respect to shelters and the walls of an otherwise empty arena, can be captured with a stochastic state--based algorithm. We call this algorithm RAMBLER, Randomized Algorithm Mimicking Biased Lone Exploration in Roaches. In this work, we verified and extended this model by adding a barrier in the previously used arena and conducted more cockroach experiments. In two arena configurations, our simulated model's path length distribution was similar to the experimental distribution (mean experimental path length 3.4m and 3.2m, mean simulated path length 3.9m and 3.3m). By analyzing cockroach behavior before, along, and at the end of the barrier, we have generalized RAMBLER to address arbitrarily complex 2D mazes. For biology, this is an abstract behavioral model of a decision--making process in the cockroach brain. For robotics, this is a strategy that may improve exploration for goals, especially in unpredictable environments with non--convex obstacles. Generally, cockroach behavior seems to recommend variability in the absence of planning, and following paths defined by walls.