The Linear Temporal Logic MissiOn Planning (LTLMoP) toolkit is a software package designed to assist in the rapid development, implementation, and testing of highlevel robot controllers. In this toolkit, structured English and Linear Temporal Logic are used to write high-level reactive task specifications, which are then automatically transformed into correct robot controllers that can be used to drive either a simulated or a real robot. LTLMoP's modular design makes it ideal for research in areas such as controller synthesis, semantic parsing, motion planning, and human-robot interaction.

The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field.We present a modular robot system capable of autonomously completing highlevel tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware, and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task.The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world. arXiv:1709.05435v2 [cs.RO] 13 Dec 2018Modular self-reconfigurable robot (MSRR) systems are composed of repeated robot elements (called modules) that connect together to form larger robotic structures, and can self-reconfigure, changing the connective arrangement of their own modules to form different structures with different capabilities. Since the field was in its nascence, researchers have presented a vision that promised flexible, reactive systems capable of operating in unknown environments. MSRRs would be able to enter unknown environments, assess their surroundings, and self-reconfigure to take on a form suitable to the task and environment at hand [1]. Today, this vision remains a major motivator for work in the field [2].Continued research in MSRR has resulted in substantial advancement. Existing research has demonstrated MSRR self-reconfiguring, assuming interesting morphologies, and exhibiting various forms of locomotion, as well as methods for programming, controlling, and simulating modular robots [1,3,4,5,6,7,8,9,10,11,12,13,14,15]. However, achieving autonomous operation of a self-reconfigurable robot in unknown environments requires a system with the ability to explore, gather information about the environment, consider the requirements of a high-level task, select configurations whose capabilities match the requirements of task and environment, transform, and perform actions (such as manipulating objects) to complete tasks. Existing systems provide partial sets of these capabilities. Many systems have demonstrated limited autonomy, relying on beacons for mapping [16,17] and human input for high-level decision making [18,19]. Others have demonstrated swarm self-assembly to address basic tasks such as hill-climbing and gap-crossing [20,21]. While these existing systems all represent advancements, none have demonstrated fully autonomous, reactive self-reconfiguration to address high-level tasks.This paper presents a system allowing modular robots to complet...

Abstract-The advantage of modular robot systems lies in their flexibility, but this advantage can only be realized if there exists some reliable, effective way of generating configurations (shapes) and behaviors (controlling programs) appropriate for a given task. In this paper, we present an end-to-end system for addressing tasks with modular robots, and demonstrate that it is capable of accomplishing challenging multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A large design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware.The broader goal of this project is enabling users to address real-world tasks using modular robots. We believe this work represents an important step toward this larger goal.

We present a system enabling a modular robot to autonomously build structures in order to accomplish highlevel tasks. Building structures allows the robot to surmount large obstacles, expanding the set of tasks it can perform. This addresses a common weakness of modular robot systems, which often struggle to traverse large obstacles.This paper presents the hardware, perception, and planning tools that comprise our system. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the environment to make obstacles traversable. A high-level planner reasons about the task, robot locomotion capabilities, and environment to decide if and where to augment the environment in order to perform the desired task. We validate our system in hardware experiments.

The advantage of modular self-reconfigurable robot systems is their flexibility, but this advantage can only be realized if appropriate configurations (shapes) and behaviors (controlling programs) can be selected for a given task. In this paper, we present an integrated system for addressing high-level tasks with modular robots, and demonstrate that it is capable of accomplishing challenging, multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A large design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. This paper build on earlier work by the authors [10], extending the original system to include environmentally adaptive parametric behaviors, which integrate motion planners and feedback controllers with the system.