The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments. We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations.

We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor's requests and, if needed, transparently routes them to other robots for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, latency requirements, and multi-hop, periodic, bandwidth-limited network communications; the algorithm can minimize overall energy usage or maximize the reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license.

We introduce a fast approximate stability analysis into an automated dry stacking procedure. Evaluating structural stability is essential for any type of construction, but especially challenging in techniques where building elements remain distinct and do not use fasteners or adhesives. Due to the irregular shape of construction materials, autonomous agents have restricted knowledge of contact geometry, which makes existing analysis tools difficult to deploy. In this paper, a geometric safety factor called kern is used to estimate how much the contact interface can shrink and the structure still be feasible, where feasibility can be checked efficiently using linear programming. We validate the stability measure by comparing the proposed methods with a fully simulated shaking test in 2D. We also improve existing heuristics-based planning by adding the proposed measure into the assembly process.

We describe a method for automatically building structures from stacked, irregularly shaped objects. This is a simplified model for the problem of building dry stacked structures (i.e. no mortar) from found stones. Although automating such construction methods would be ideally suited for disaster areas or remote environments, currently such structures need to be built by skilled masons. No practical methods for automating the assembly planning process are known. The problem is challenging since each assembly action can be drawn from a continuous space poses for an object and several local geometric and physical considerations strongly affect the overall stability. We show that structures that are built following a stacking order for perfect bricks can accommodate a limited amount of irregularity, however, their performance degrades quickly when objects deviate from their ideal shape. We present a strategy for stacking irregular shapes that first considers geometric and physical constraints to find a small set of feasible actions and then further refines this set by using heuristics gathered from instructional literature for masons. The proposed method of choosing assembly actions allows construction with objects that contain a significant amount of variation.