In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.
An effective way to create modular and complex assembly systems is to define a set of assembly primitives, and adopt a higher level logic that switches between them in order to obtain a desired assembly behaviour. For instance, in @cite_0 , petri nets are used to model transitions between different discrete steps of an assembly problem while an automatic assembly planner was proposed in @cite_22 , where each assembly task is executed by a suitable controller; switching between states is made when a set of limit conditions is reached. More recently, @cite_26 employs the concept of function blocks for robotic assembly. Each primitive assembly task is encapsulated in a block, every one of which with its own set of techniques for the successful assembly execution. A complete assembly task is divided in these primitives, and petri nets are used to model the transitions between different assembly operations.
@cite_0 @cite_26 @cite_22
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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. 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. Much recent work in robot learning from demonstration has focused on automatically segmenting continuous task demonstrations into simpler, reusable primitives. However, strong assumptions are often made about how these primitives can be sequenced, limiting the potential for data reuse. We introduce a novel method for discovering semantically grounded primitives and incrementally building and improving a finite-state representation of a task in which various contingencies can arise. Specifically, a Beta Process Autoregressive Hidden Markov Model is used to automatically segment demonstrations into motion categories, which are then further subdivided into semantically grounded states in a finite-state automaton. During replay of the task, a data-driven approach is used to collect additional data where they are most needed through interactive corrections, which are then used to improve the finite-state automaton. Together, this allows for intelligent sequencing of primitives to create novel, adaptive behavior that can be incrementally improved as needed. We demonstrate the utility of this technique on a furniture assembly task using the PR2 mobile manipulator.