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
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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. This paper addresses the problem of finding robot configurations to grasp assembly parts during a sequence of collaborative assembly operations. We formulate the search for such configurations as a constraint satisfaction problem (CSP). Collision constraints in an operation and transfer constraints between operations determine the sets of feasible robot configurations. We show that solving the connected constraint graph with off-the-shelf CSP algorithms can quickly become infeasible even for a few sequential assembly operations. We present an algorithm which, through the assumption of feasible regrasps, divides the CSP into independent smaller problems that can be solved exponentially faster. The algorithm then uses local search techniques to improve this solution by removing a gradually increasing number of regrasps from the plan. The algorithm enables the user to stop the planner anytime and use the current best plan if the cost of removing regrasps from the plan exceeds the cost of executing those regrasps. We present simulation experiments to compare our algorithm's performance to a naive algorithm which directly solves the connected constraint graph. We also present a real robot system which uses the output of our planner to grasp and bring parts together in assembly configurations. We present a decentralized, scalable approach to assembling a group of heterogeneous parts into different products using a swarm of robots. While the assembly plans are predetermined, the exact sequence of assembly of parts and the allocation of subassembly tasks to robots are determined by the interactions between robots in a decentralized fashion in real time. Our approach is based on developing a continuous abstraction of the system derived from models of chemical reactions and formulating the strategy as a problem of selecting rates of assembly and disassembly. These rates are mapped onto probabilities that determine stochastic control policies for individual robots, which then produce the desired aggregate behavior. This top-down approach to determining robot controllers also allows us to optimize the rates at the abstract level to achieve fast convergence to the specified target numbers of products. Because the method incorporates programs for assembly and disassembly, changes in demand can lead to reconfiguration in a seamless fashion. We illustrate the methodology using a physics-based simulator with examples involving 15 robots and two types of final products.