We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction problems, based on Answer Set Programming. This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan. We prove the soundness and completeness of our formal method with respect to these properties. We introduce a set of challenging robot construction benchmark instances, including bridge building and stack overhanging scenarios, discuss the usefulness of our framework over these instances, and demonstrate the applicability of our method using a bimanual Baxter robot.
In automated manufacturing, assembly plans aim to determine the proper order of assembly operations to build a coherent object. During assembly planning, the goal configuration is well-defined and the problem is generally approached by starting with the goal configuration and working backwards to disassemble all parts. Object stability has also been considered within this context @cite_49 @cite_32 @cite_15 @cite_58 @cite_42 @cite_38 @cite_33 . The assembly planning problem is significantly different from the robotic construction problems: on the one hand, it allows assembly of irregular objects; on the other hand, the goal configuration is pre-determined and solutions are commonly restricted to monotone plans.
@cite_38 @cite_33 @cite_42 @cite_32 @cite_49 @cite_15 @cite_58
2151075362 2521537405 1967234152 2040110103 2044271772 1969196471
The aim of object pile deconstruction is to safely remove elements one by one without compromising stability. The number of combinations of removal sequences increases dramatically with the number of objects and thus testing every combination is intractable in practical scenarios. We model the deconstruction sequencing problem using a disassembly graph, and investigate and discuss search strategies for discovery of stable sequences in an architectural context. We run and compare techniques in a large-scale experiment, on various virtual scenes of architectural models composed of different shapes, sizes and number of elements. Purpose The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence is stable during assembly. They are easy to grasp and robust to motion uncertainty. Design methodology approach The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly and the final pose of the assembly. The output is an optimal assembly sequence, namely, in which order should one assemble the objects, from which directions should the objects be dropped and candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating and searching the possible assembly sequences considering stability, graspability and assemblability qualities. Findings The proposed planner could plan an optimal sequence to guide robots to do assembly using translational motion. The sequence provides initial and goal configurations to motion planning algorithms and is ready to be used by robots. The usefulness of the proposed method is verified by both simulation and real-world executions. Originality value The paper proposes an assembly planner which can find an optimal assembly sequence automatically without teaching of the assembly orders and directions by skilled human technicians. The planner is highly expected to improve teachingless robotic manufacturing. High-level assembly planning systems generate plans for the automated assembly of mechanical products by robots. The sequences to be generated underlie several physical and geometrical constraints, and in addition have to be efficient to increase productivity. The challenges still facing the field are to develop efficient and robust analysis tools, and to develop planners capable of finding optimal or near-optimal sequences rather than just feasible sequences. The presented high level assembly planning system High LAP automatically considers physical and geometrical constraints to generate and to evaluate stable assembly sequences. In this paper we propose a relational assembly model including a CAD description and the specification of features and relations of the assembly components. We use an optional specification of an arbitrary hierarchy of assemblies to speed up and guide the generation of sequences. High LAP evaluates all feasible assembly sequences considering several criteria like separability and manipulability of the generated (sub)assemblies. Furthermore, the necessity of reorientation for a mating operation and parallelism during plan execution are considered. Another important criterion is the stability of the generated (sub)assemblies. Most of the assembly planners developed up to date use heuristical or user-defined criteria to determine assembly stability for plan evaluation. The presented system is the first assembly planning system which automatically determines the range of all stable orientations of an assembly for plan evaluation. Therefore, we introduce a stability metric and an algorithm to calculate all stable orientations of an assembly considering friction. Experimental results are presented to demonstrate the efficiency of our assembly planning system. Abstract This paper presents the application of geometric reasoning to the automatic construction of an assembly partial order from an attributed liaison graph representation of an assembly. The construction is based on the principle of assembly by disassembly and on the extraction of preferred subassemblies. On the basis of accessibility and manipulability criteria, the system first decomposes the given assembly into clusters of mutually inseparable parts. An abstract liaison graph is then generated wherein each cluster of mutually inseparable parts is represented as a supernode. A set of subassemblies is then generated by decomposing the abstract liaison graph into subgraphs, verifying the disassemblability of individual subgraphs, and applying the criteria for selecting preferred subassemblies to the disassemblable subgraphs. The recursive application of this process to the selected subassemblies results in a Hierarchical Partial-Order Graph (HPOG). A HPOG not only provides the precedence relations among assembly operations but also presents the temporal and spatial parallelism for implementing distributed and cooperative assembly. The system is organized under the “cooperative problem solving (CPS)” paradigm. This article describes a fully functional autonomous system with two cooperating robots that disassembles complex Duplo1structures in a restricted environment. The system operates on a table-top world with any number of Duplo structures for which object models have been given. The structures are dis assembled down to individual parts, using basic operations of single part removal, object partitioning, and the addition of stabilizing supports to structures that would otherwise fall over. All aspects are automatically planned, including operation selection, path and grasp planning, and simultaneous cooper ative robot motion. Operations are chosen so as to not create unstable structures and to not risk breakage in areas of low structural integrity. Overall planning is done with a process mechanism that heuristically generates efficient disassembly sequences, without searching a space of all possible operations. Several examples of actual system operation, using real robots, are presented. Abstract In which order can a product be assembled or disassembled? How many hands are required? How many degrees of freedom? What parts should be withdrawn to allow the removal of a specified subassembly? To answer such questions automatically, important theoretical issues in geometric reasoning must be addressed. This paper investigates the planning of assembly algorithms specifying (dis) assembly operations on the components of a product and the ordering of these operations. It also presents measures to evaluate the complexity of these algorithms and techniques to estimate the inherent complexity of a product. The central concept underlying these planning and complexity evaluation techniques is that of a “non-directional blocking graph”, a qualitative representation of the internal structure of an assembly product. This representation describes the combinatorial set of parts interactions in polynomial space. It is obtained by identifying physical criticalities where geometric interferences among parts change. It is generated from an input geometric description of the product. The main application considered in the paper is the creation of smart environments to help designers create products that are easier to manufacture and service. Other possible applications include planning for rapid prototyping and autonomous robots.