We present hardware, perception, and planning tools that enable a modular robot to autonomously deploy passive structures to reach otherwise-inaccessible regions of the environment to accomplish high-level tasks. 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 identified features to expand the reachable space of the robot. A high-level planner reasons about the high-level task, robot locomotion capabilities, and the environment to decide if and where to augment the environment in order to perform the desired task. These autonomous, perception-driven augmentation tools extend the adaptive capabilities of modular robot systems.
present hardware and algorithms for building amorphous ramps in unstructured environments by depositing foam with a tracked mobile robot @cite_1 @cite_6 . Amorphous ramps are built in response to the environment to allow a small mobile robot to surmount large, irregularly shaped obstacles. Our work is similar in spirit, but places an emphasis on autonomy and high-level locomotion and manipulation tasks rather than construction.
@cite_1 @cite_6
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We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise robust adaptive global properties. We illustrate the algorithm in both the single robot and multi-robot case via simulation and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype. This paper introduces a novel and general real-time adaptive motion planning (RAMP) approach suitable for planning trajectories of high-DOF or redundant robots, such as mobile manipulators, in dynamic environments with moving obstacles of unknown trajectories. The RAMP approach enables simultaneous path and trajectory planning and simultaneous planning and execution of motion in real time. It facilitates real-time optimization of trajectories under various optimization criteria, such as minimizing energy and time and maximizing manipulability. It also accommodates partially specified task goals of robots easily. The approach exploits redundancy in redundant robots (such as locomotion versus manipulation in a mobile manipulator) through loose coupling of robot configuration variables to best achieve obstacle avoidance and optimization objectives. The RAMP approach has been implemented and tested in simulation over a diverse set of task environments, including environments with multiple mobile manipulators. The results (and also the accompanying video) show that the RAMP planner, with its high efficiency and flexibility, not only handles a single mobile manipulator well in dynamic environments with various obstacles of unknown motions in addition to static obstacles, but can also readily and effectively plan motions for each mobile manipulator in an environment shared by multiple mobile manipulators and other moving obstacles. We present a locally reactive algorithm to construct arbitrary shapes with amorphous materials. The goal is to provide methods for robust robotic construction in unstructured, cluttered terrain, where deliberative approaches with pre-fabricated construction elements are difficult to apply. Amorphous materials provide a simple way to interface with existing obstacles, as well as irregularly shaped previous depositions. The local reactive nature of these algorithms allows robots to recover from disturbances, operate in dynamic environments, and provides a way to work with scalable robot teams. Traditional motion planning approaches for multilegged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or nonconvex optimization with intractable computation time. In this letter, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions, and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a prespecified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where nonconvexity and flat terrain assumptions might lead to suboptimal or unstable plans. Our method increases the motion robustness while keeping a low computation time.