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This paper presents a framework for going from specifications to implementations of decentralized control strategies for multi-robot systems. In particular, we show how the use of Embedded Graph Grammars (EGGs) provides a tool for characterizing local interaction and control laws. This paper highlights some key implementation aspects of the EGG formalism, and develops and discusses experimental results for a hexapod-based multi-robot system, as well as a multi-robot system of wheeled robots.

Drought and salt stress are major abiotic stress that inhibit plants growth and development, here we report a plasma membrane intrinsic protein ZmPIP1;1 from maize and identified its function in drought and salt tolerance in Arabidopsis. ZmPIP1;1 was localized to the plasma membrane and endoplasmic reticulum in maize protoplasts. Treatment with PEG or NaCl resulted in induced expression of ZmPIP1;1 in root and leaves. Constitutive overexpression of ZmPIP1;1 in transgenic Arabidopsis plants resulted in enhanced drought and salt stress tolerance compared to wild type. A number of stress responsive genes involved in cellular osmoprotection in ZmPIP1;1 overexpression plants were up-regulated under drought or salt condition. ZmPIP1;1 overexpression plants showed higher activities of reactive oxygen species (ROS) scavenging enzymes such as catalase and superoxide dismutase, lower contents of stress-induced ROS such as superoxide, hydrogen peroxide and malondialdehyde, and higher levels of proline under drought and salt stress than did wild type. ZmPIP1;1 may play a role in drought and salt stress tolerance by inducing of stress responsive genes and increasing of ROS scavenging enzymes activities, and could provide a valuable gene for further plant breeding.

We present successful control strategies for dynamically stable robots that avoid low ceilings and other vertical obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle dimensions, our method uses a sequential composition of IOlinearized controllers and applies stochastic optimization to automatically compute the best controller gains and references, as well as the times for switching between the different controllers. We demonstrate this system through numerical simulations, validation in a physics-based simulation environment, as well as on a novel two-wheeled platform. The results show that the generated control strategies are successful in mission planning for this challenging problem domain and offer significant advantages over hand-tuned alternatives.

This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.