Automatic Planning of Manufacturing Processes using ... · Automatic Planning of Manufacturing Processes using Spatial Construction Plan Analysis and Extensible Heuristic Search

Automatic Planning of Manufacturing Processes using SpatialConstruction Plan Analysis and Extensible Heuristic Search

Ludwig Nagele, Andreas Schierl, Alwin Hoffmann and Wolfgang ReifInstitute for Software & Systems Engineering, University of Augsburg, 86159 Augsburg, Germany

Keywords: Manufacturing Planning, Construction Plan Analysis, Heuristic Planning.

Abstract: When automating small-batch manufacturing processes, the time spent for process planning and robot pro-gramming becomes more important. This paper proposes an automated process including construction plananalysis, process planning and execution to reduce the amount of manual work required. The process startsby analyzing the structure of the desired product and deriving required process step results, then uses heuristicsearch to find possible production steps and task assignments, and concludes by simulating or executing theresulting production plan. The approach is evaluated on a case study with a simulated robot automaticallybuilding different LEGO R© DUPLO R©structures starting from a 3D model defining the desired product.

1 INTRODUCTION

When new products transfer from design to produc-tion, not only the supply of resource materials hasto be ensured but also manufacturing processes haveto be defined that consist of many single productionsteps. When automating production with robots toreduce cost, a suitable robot cell configuration needsto be provided, robots have to be programmed andteached, end-effectors such as welding tongs are to beintegrated. Additionally, proper flow of productionparts through the manufacturing process is necessary.

Thus, before a product finally comes to produc-tion, a lot of time-consuming work has to be per-formed by engineers, technicians and programmers.While this effort usually is profitable for high lotsizes, the process and program definition for smallerlot sizes or even single individual product manufac-turing forms an enormous challenge to the producer.

In this paper, we present a modular approach forautomatic planning of manufacturing processes aim-ing for speeding up the entire development process upto the final robot program definition. In a first step, anengineer’s construction plan is parsed and analyzedto generate a structural model. From this model, pos-sible process definitions and their order are derivedwhich describe manipulator independent manufactur-ing steps. Appropriate robots are assigned to eachprocess step in order to form an executable actionas part of the final program. Besides, techniques areused for optimizing both planning time and the result.

Figure 1: Simulated KUKA KR 6 robot and SchunkWSG 50 gripper performing LEGO brick placement.

The approach is suitable for production of high lotsizes just as for manufacturing unique products.

As a main contribution, the paper presents an ap-proach for spatial analysis of a construction blueprintresulting a detailed structure model of the product.Furthermore, the work introduces a technique for de-riving an optimized order of process steps necessaryfor manufacturing a given structure model.

After an overview of other approaches to performautomatic planning of manufacturing tasks in sec-tion 2, an introduction to the case study with LEGO R©

DUPLO R© is given in section 3 (see Fig. 1). Section 4presents the elements and steps of the approach in de-tail, followed by a description of the heuristics usedfor finding proper sequences of process steps and forplanning with appropriate robots. As an evaluationof the approach, its application to the LEGO domain

576Nägele, L., Schierl, A., Hoffmann, A. and Reif, W.Automatic Planning of Manufacturing Processes using Spatial Construction Plan Analysis and Extensible Heuristic Search.DOI: 10.5220/0006861705760583In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018) - Volume 2, pages 576-583ISBN: 978-989-758-321-6Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

for building structures is presented in section 6. Ex-perimental results from a survey of different heuristicvariants for the LEGO example are discussed in sec-tion 7 before the work concludes in section 8.

2 RELATED WORK

Manufacturing and assembly with robots is still anongoing topic in research. Some approaches con-centrate on discrete planning techniques to solve al-location of resources and task assignment to spe-cific robots. For this, the STRIPS planner developedin Stanford (Fikes and Nilsson, 1971) and the Plan-ning Domain Definition Language (PDDL) by Mc-Dermott et al. (1998) which bases on STRIPS arecommonly used. In PDDL, physical entities are speci-fied as objects and domain-specific predicates are de-fined which hold situational properties of these ob-jects. These predicates are used to describe an initialstate and a goal specification of the planning prob-lem. With actions that are defined with preconditionsand are used to manipulate a situation by influencingthe value of predicates, the planning problem can besolved. However, our planning domain is highly de-pendent on geometric reasoning with pre- and post-conditions calculated during planning time which isdifficult to express in PDDL.

In the european project SkillPro (Pfrommer et al.,2015), skills are used to describe functionality ofavailable resources. Together with manufacturing re-quirements of the final product, executable processactions can be generated. While actions can be dy-namically orchestrated and integrated into the manu-facturing process at runtime, in our project we want tofocus on an overall optimization of the entire manu-facturing process considering products’ as well as re-sources’ characteristics and performances.

In the last few years a trend has evolved to con-sider motion planning together with classical taskplanning for robot based manufacturing. One ap-proach by Kaelbling and Lozano-Perez (2011) pro-poses stepwise planning and execution of successiveactions in order to reduce complexity. Planning anddecomposition of tasks is done only in limited depth.The intermediate results are then executed and plan-ning on task level is subsequently continued based onthe new situation. In a further work, Levihn et al.(2013) introduce concepts for foresight with a beliefstate and reconsideration as improvements to theirfirst approach. For our project, we plan to integratethe concept of interleaved execution while planning,but use simulation techniques instead.

Many approaches exist which try to bring prede-

fined process steps to execution on a robot cell. Butresearchers also pay attention to automatic definitionof process steps by raw construction models or 3Ddata. In an approach where KUKA youbots are usedto assemble an IKEA furniture, Knepper et al. (2013)present a geometric preplanning strategy to extracta final assembly configuration (blueprint) from theform and quantity of available parts. In a second step,a symbolic planner is used to find a sequence of oper-ations to assemble the blueprint. The presented sep-aration of blueprint analysis, operation sequence ex-amination and finally execution will be a paradigm forour project.

In their work about an offline programming plat-form for automatic programming of manufacturingtasks in the domain of carbon fiber-reinforced poly-mers (CFRP), Nagele et al. (2015) present techniquesto derive executable robot programs from an initialCAD model of the final product. The CAD model isparsed and decomposed into a hierarchical task repre-sentation with pre- and postconditions for each leaf.The concept of task contribution units is introducedin a further work by Macho et al. (2016) which al-lows for dynamic planning of each task basing on aset of various exchangeable modules. Such a mod-ule can provide generic calculation functionality, ap-ply domain-specific knowledge or involve interactionwith a human expert. While this offline programmingplatform is mainly focused on the domain of CFRPand the presented planning problem is almost sequen-tial, the idea of parsing and analyzing some kind ofblueprint is taken as inspiration for our project.

3 CASE STUDY

Building up and programming manufacturing systemsoften includes generic challenges on the one hand anddomain-specific problems on the other hand. As acase study, an application in the domain of construc-tion toy LEGO R© DUPLO R© is presented. Its inter-changeable and often symmetric raster-sized brickskeep domain-specific problems simple and allow forfocusing on generic challenges of automatized pro-duction design.

In our first scenario, we use a base plate of 24×24studs, 13 colored bricks with dimensions of 2× 2studs and 13 bricks with a raster of 2×4 studs. As aninitial setup, all bricks are randomly placed planarlyon the base plate as shown in Fig. 2 with a minimumdistance of one raster width. The goal – the manufac-turing product – is a yellow house topped with a redroof, having a space for an entry door and a windowarea on all three other sides (see Fig. 2).

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Figure 2: Initial setup (left) and goal structure (right) forbuilding up a LEGO house.

For manipulation, a KUKA KR 6 robot with amounted Schunk WSG 50 gripper is used. Both ac-tuators, as well as all LEGO parts, are integrated inthe Robotics API (Angerer et al., 2013) which allowsfor object-oriented behavior programming and pro-vides visualization by an inbuilt engine. For evalu-ating conceptual and algorithmic results of this paper,the inbuilt simulation feature of the Robotics API isused to execute actuator behavior (see Fig. 1).

4 APPROACH

One main challenge in automation of manufactur-ing is to bring a construction plan (3D model, writ-ten script, etc.) to a stable, efficient and at bestcost-optimal manufacturing program for a robot cell.There are several development phases and decisionswhich all influence the final result with respect toquality, resource efficiency and performance of bothproduct and production system. We propose an ex-tensible module based approach which is meant toease this task by automatic programming of such pro-cesses. Different modules can be contributed to pro-vide specific knowledge and computation, rangingfrom automation expertise for programming robotsand process expertise regarding manipulation and ma-terial behavior, over to detailed knowledge about thespecific application domain. The presented approachsplits the development of manufacturing programsinto four parts: structure analysis, process planning,task assignment and simulation and execution.

4.1 Structure Analysis

The overall goal of structure analysis is to extract asmuch information out of a given initial construction

plan as possible. We assume that a construction plandescribes all parts to be assembled as well as furtherinformation such as their relative positions to eachother or applied treatments and processes. We namesuch a piece of information Attribute, which describesone or relates multiple parts of the product. Examplesfor attributes are that a product is painted or that mul-tiple parts are plugged or glued together.

Figure 3: LEGO diamond structure with four attributes.

Fig. 3 shows a simple structure which is an expres-sive showcase for general planning challenges. Four2× 2 bricks are assembled in a diamond-like shape.The four connection lines illustrate attributes describ-ing which bricks are in a stacked relationship to eachother and which transformation between the respec-tive bricks is applied.

Attributes are called executionally equivalent ifthey cannot be established independently. Assumingall three lower bricks in Fig. 3 are already stacked byattributes 3 and 4. A following placing of the top brickwould result in simultaneously establishing attribute 1and 2. An independent establishment of only one ofthese attributes is not possible. The property of execu-tional equivalence is not valid in general on a specificset of attributes but depends on the particular setting:When attributes 2 and 3 are already established, theset of attributes 1 and 4 emerges to be executionallyequivalent. However, with attributes 1 and 3 beingestablished, attributes 2 and 4 are not executionallyequivalent due to their contradicting stacking direc-tion relating to their joint (blue) brick.

To automatically derive attributes from an ini-tial construction plan, we propose Analyzer Mod-ules which take a construction plan and return a setof attributes. They automatically compute attributesfrom existing properties or relationships, for exam-ple through geometrical inspection. Multiple analyzermodules can be provided for general purposes anddifferent domains. For instance, one analyzer mod-ule determines parts which need to be bolted togetherwhile another one identifies glued parts and calculatesminimal needed adhesive strength using an internalstatic analysis. Once analyzer modules are availablefor all properties and relationships of certain domain,they result a complete set of attributes when appliedto a construction plan of the respective domain.

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A data structure describing which attributes are es-tablished and which are not at a certain moment iscalled Situation. When manufacturing a product, atleast two situations are known: a goal situation con-taining all established attributes to form the final prod-uct, and an initial situation which describes the setupbefore assembly begins. Here, a set of attributes de-scribes initial placements of parts, for example.

Analyzer modules as well as types of attributes aregenerally contributed by application domain expertswho know about technical details, what kinds of at-tribute types exist and how they can be identified in aplain construction plan. The result of structure anal-ysis is an initial situation describing start setup and agoal situation which analyzer modules extracted outof a construction plan.

4.2 Process Planning

To transition an initial situation to a goal situation, de-tailed process descriptions (Process Actions), whichrepresent abstract robot behavior, and their order aresearched for. They furthermore indicate which at-tributes are established or removed when executingthe behavior. For this, Skill Modules are introducedwhich create particular process actions that are appli-cable in a given situation. Depending on a concreteskill module implementation multiple alternative pro-cess actions are conceivable for one situation: If asituation describes three bricks placed on the floor, agrasping skill module provides three grasping actions– one for each brick. At this point, alternative pro-cess steps are introduced which later allow for overallvariability exploration and optimization.

Skill modules and their respective process actionsare contributed by process experts who have funda-mental knowledge about process steps which can betaken in specific situations. As a result of processplanning, skill modules provide possible process ac-tions which can be performed in specific situations.

4.3 Task Assignment

Process actions describe behavior from the product’spoint of view. From automation view, process actionsneed to be performed by concrete manipulators likerobots, grippers or tools. For defining the behaviorof process actions, skill modules use generic behav-ior interfaces. These can later be instantiated by con-crete manipulators whose capabilities are then usedto perform the desired action. This concept is takenfrom the Robotics API where actuator and sensor in-terfaces are used to abstract robot actions (e.g. PTP,LIN, GRASP, MEASURE) from the underlying ma-

nipulators. With concrete manipulators assigned, askill module can calculate a new situation that resultswhen executing the process action in a given situa-tion. When a product is being grasped, the gripper isalso part of the resulting situation.

The basic purpose of task assignment is to findproper constellations of manipulators which can beused to perform a process action’s task based on itsbehavior descriptions. In a flexible multi-functionalrobot cell like the one described by Angerer et al.(2015), multiple robots – or even teams of robots –might be applicable to perform the same task. Thissearch is done by Manipulator Modules which findall manipulators or teams appropriate for the behaviorinterfaces of the process action. In the current stage,alternatives of manipulators are provided, but there isno concrete strategy or criteria for selection yet.

Process actions describe their task without know-ing the actuator effectively used. This allows to injectconcrete robots and to evaluate the appropriatenesswhile planning. Manipulator modules are contributedby process and automation experts. The step of taskassignment deals with resource (manipulator) alloca-tion and assignment as well as with finally providingconcrete execution plans.

4.4 Simulation and Execution

Once specific manipulators are assigned to a processaction, it can be executed in a given situation. Thepresented approach uses Robotics API activities (An-gerer et al., 2013) to execute process actions withrobots. The framework allows for both inbuilt simula-tion enhanced with a graphical 3D visualization (seeFig. 1) as well as execution with real robots on itsRobot Control Core (Vistein et al., 2014). For productparts being correctly positioned and visualized, thechanges performed by process actions are transferredto the world model of the Robotics API by translat-ing attributes to geometrical or logical properties sup-ported by the framework.

The concepts presented in this work are evaluatedin simulation only. However, the Robotics API al-lows for a transition to real execution. For this, theexact positions of resources, robots and parts need tobe reflected back into the world model. In general,processes furthermore need testing and adjustmentsof process parameters by automation experts.

5 HEURISTIC SEARCH

For defining an overall execution plan, several stepshave to be taken using the aspects described in the


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Analyzer Module

ConstructionPlan

Current Situation

Skill Action Goal SituationInitial Situation Skill ActionProcessAction

Current SituationSituation

SkillModule

ManipulatorModule

Figure 4: Overview of steps for planning, modules and intermediate artifacts.

previous sections, each offering a variety of optionsto continue with. Figure 4 gives an overview of thesteps, modules and artifacts involved when searchingfor an overall process plan. In detail, following algo-rithmic steps have to be taken:1. Find the goal situation by applying all analyzer

modules to the construction plan. Provide initialsituation from a description of the initial setup.Choose initial situation as current situation.

2. Apply all skill modules to current situation andderive all possible process actions.

3. For each process action: Use manipulator mod-ules to find all possible sets of manipulator in-stances which satisfy the process action’s neededbehavior interfaces.

4. For each set of manipulator instances: Assign itto the process action and derive its resulting situ-ation. Iteratively repeat (2) through (4) with thenew situation as current situation until goal situa-tion is reached.

5. Execute each step of the found path using the cor-responding manipulators and respectively updatethe world model after performing process actions.As hinted by steps (2) and (3), an exponential

number of options evolves due to alternative pro-cess actions for each situation and different manip-ulators for each single process action. To find anoptimal process plan, we formulate an optimizationproblem for the A-Star algorithm (Hart et al., 1968).A-Star uses sets of nodes, weighted directed edgesand cost-estimations for nodes to iteratively searchfor a cost-minimal path from an initial to a desiredgoal node. In the presented approach, situations actas nodes, with initial and goal situation as initial andgoal nodes. Starting with initial situation, A-Star re-quests all edges to proceed with. For this, all possibleprocess actions basing on initial situation are calcu-lated. Each process action is combined with all possi-ble manipulator sets which are applicable for execu-tion. Such a combination of process action and ma-nipulators is used as edge in A-Star. Based on their

weights, edges are taken by A-Star and a subsequentsituation is computed to continue with. The weightof an edge can be determined as the expense of theprocess action and the cost of manipulators used, forexample, whereas expected remaining cost of a situa-tion to reach goal situation needs to be estimated.

Cost estimation plays a fundamental role for A-Star. The performance is highly dependent on the cal-culated weights of transitions and the expected dis-tances from nodes to the goal, which might be esti-mated exactly in rarest cases only. If overestimated,the result is not guaranteed to be cost-optimal. If un-derestimated, a cost-optimal solution is guaranteedbut the search may take exhaustive time. Our ap-proach estimates costs for situations based on theirattributes: The cost of a situation is the number ofattributes which still have to be established plus thenumber of attributes which need to be removed in or-der to reach goal situation. The actual weight of a pro-cess action transitioning from a situation to anotheris calculated as difference between the costs of bothsituations, but with a minimum of 1. This preventsweight-neutral process actions with no effect from be-ing performed and leads to a cost-underestimation ofsituations which guarantees cost-optimal solutions.

6 APPLICATION TO LEGO

The planning approach is applied to the domain ofLEGO and as a use case, a house as shown in Fig. 2 isbuilt automatically using the presented concepts. Inthis domain, three kinds of attributes suffice to de-scribe situations for the approach:

• A base plate is statically placed in the world by aFixpointAttribute which describes its position.

• LegoPlacementAttributes between two bricks de-scribe that these are plugged together with a spe-cific relative transformation.

• A GraspAttriute denotes a brick which is graspedby the gripper, as well as its grasping position.


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While the spatial positions of bricks are alreadygiven for both initial and goal setup (see Fig. 2), ini-tial and goal situations with attributes need to be com-puted using analyzer modules. Being LEGO domainexperts, the authors created a LegoBrickAnalyzer-Module which geometrically checks each pair ofbricks for being in a plugged relationship. For eachpair with an adequate spatial distance and rotation, aLegoPlacementAttribute is generated. In the diamondexample, this analyzer module identifies the four at-tributes depicted in Fig. 3.

To determine possible manipulation steps, skillmodules along with manipulator instances are used.For LEGO and a robot cell consisting of a robot anda gripper, two kinds of skill modules are relevant:• PickupSkillModule provides process actions for

grasping and picking up each brick which is free-standing in a given situation. After executionof this process action, existing LegoPlacement-Attributes of the brick are removed and a Grasp-Attribute between brick and gripper is establishedin the resulting situation.

• PlaceSkillModule provides process actions forplacing an already grasped brick on the targetposition derived from the goal situation. Theresulting situation contains new LegoPlacement-Attributes and is relieved from the GraspAttribute.As a design-decision to this example, the gripper

can only grasp one brick at once instead of clusters ofbricks. In a first guess, a simple heuristic might builda LEGO structure strictly processing layer by layerfrom bottom to top starting at the base plate. Thisstraightforward approach is capable in most cases, butdue to specific intermediate structure setups, dead-lock situations might occur: Assuming a final prod-uct with overhanging parts, these bricks will neverbe plugged from the downside. As another exam-ple, bricks needed for the first layer can be blockedby other bricks which will however not be removed.

To overcome such deadlock situations, a morecomplex heuristic is proposed for which all possi-ble immediate steps are reviewed and rejected wherenecessary. Both PickupSkillModule and PlaceSkill-Module precisely check geometrical and structuralsuitability for picking up or placing a brick. In par-ticular, all attributes modified in one step are checkedfor required executional equivalence, and collisionsare identified and excluded, e.g. when grasping aclosely surrounded brick with hence no valid grasp-ing point. Furthermore, a situation is rejected if itwill eventually lead to a deadlock situation. As-suming the diamond example in Fig. 3 with first at-tribute 3 and then attribute 1 established. The re-sulting “C”-like construction excludes the blue brick

from ever being inserted since its two attributes 2and 4 are not executionally equivalent. To identifysuch eventual deadlocks in the situation where theybecome inevitable, for example when establishing at-tribute 1, look-aheads are performed which translatebricks and attributes into graphs and analyze futureattributes between sub-graphs for executional equiv-alence in a depth-first search. Such early deadlockpredictions are expensive but cut down on the statespace significantly. As a further improvement, onlyestablished attributes that are transitively connectedto a FixpointAttribute in both, current and goal situ-ation, contribute to the cost estimation of a situation.This adjustment privileges bottom-up assembly (likethe simple first guess does) but does not exclude otherstrategies. Thus, deadlock situations induced by thebottom-up strategy are avoided while speeding up theplanning algorithm for building LEGO products.

7 EXPERIMENTAL RESULTS

For both LEGO structures, diamond and house, pro-cesses have been planned with the presented approachand with modules as described in the previous sec-tion. All experiments have been run on a Win-dows machine with an Intel(R) Core(TM) i7-7600U(2.80GHz) and 24 GB RAM. The heuristic search andall modules have been executed by a Java Virtual Ma-chine within a single-threaded context. Table 1 givesa summary of all measured values. For the diamondexample, 10,000 experiments have been performed –all resulting with 8 process steps. The shortest ex-periment investigated 12 states in A-Star to find a re-sult, on average 15.91 states are explored. To plan thehouse, the calculation time of one state increases bya factor of almost 14 compared to the diamond. Thereason is the higher amount of bricks which leads tolonger analysis time to find possible next steps andtime needed for collision checks. The house exam-

Table 1: Results of experiment runs for diamond and house.

Diamond HouseBricks 4 26

Base Plate 1 1Test Runs 10,000 100

Result Steps 8 52Avg. Time 0.88 ms 280.81 ms

(Std. Deviation) (± 0.79 ms) (± 74.06 ms)Investigated States 15.91 368.21

(Min - Max) (12 - 18) (182 - 738)Time per State 0.055 ms 0.763 ms


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Table 2: Tests with different enabled aspects of the heuristic: For each kind of experiment, test runs, average and standarddeviation time, average investigated states with min and max and average time per state is measured.

SingleGrasp MultiGrasp MultiRobot No-Deadl.-w/ BottomUp w/o BottomUp w/ BottomUp w/o BottomUp Prediction

Experiment Nr. #1 #2 #3 #4 #5 #6Test Runs 100 100 100 100 100 1

Avg. Time 0.281 s 368.477 s 0.389 s 1.005 s 9.112 s > 1,800.0 s(Std. Deviation) (± 0.074 s) (± 214.223 s) (± 0.137 s) (± 0.432 s) (± 5.598 s) -

Investigated States 368.21 519,948.70 418.77 956.55 1,427.42 > 1.1M(Min - Max) (182 - 738) (8.58k - 1.06M) (208 - 752) (294 - 2,598) (556 - 3,173) -

Time per State 0.763 ms 0.709 ms 0.930 ms 1.051 ms 6.384 ms *

ple has furthermore been used to evaluate differentaspects of the heuristic with 100 test runs each. Ta-ble 2 compares six different experiments each withdifferent sets of activated features. All terminatingexperiments returned a sequence of 52 process steps:Picking up 26 bricks and placing 26 bricks.

Two experiments have been performed withSingleGrasp which allows grasping of only one brickat once. The first one uses the heuristic as described inthe previous section preferring bottom-up assembly.After 0.281 seconds with a standard deviation time of74 milliseconds a valid process plan was found.

With bottom-up preference being disabled, exper-iment #2 exhibits an exploded set of states, leadingto an overall longer time to find a valid result. Inthe house example, parts of the house which belongto the upper part might already be assembled on thebase plate but can not be grasped in a later step. As aninteresting fact, the average time per state decreasesslightly. This is because there is no computationwhether a randomly picked brick fits into the bottom-up strategy.

Two further experiments have been performedwith MultiGrasp which allows for picking up andplacing whole structures of LEGO instead of just sin-gle bricks. Parts of the product (the roof of the house,for example) can be assembled at another location –even before underlying parts are finished. Also here,both variants are tested, starting with bottom-up pref-erence being enabled in experiment #3. All measuredvalues seem similar to these of experiment #1. Dueto preferred bottom-up strategy, partial assembly ofparts does not occur in practice.

As a combination of disabled bottom-up and en-abled grasping of multiple bricks, experiment #4clearly shows that the large state space of a free (nonbottom-up) strategy – where the order of steps is ran-domly chosen – can be compensated by the graspingof structures. The grasping of whole brick structuresallows for assembly of different product parts inde-pendently and combining them afterwards.

Experiment #5 uses two robots and grippers, allother variants left to default (#1). The introductionof an alternative manipulator causes a significant in-crease in time needed to find a result. This is mainlycaused by the huge number of states to be investi-gated, owing to the fact that both robots can graspeach LEGO brick – in parallel and in all combina-tions. A further conspicuousness is stated by the over-proportional standard deviation time of this experi-ment, which can be explained by the variety of alter-natives and the diversity of investigated states. Sometest runs seem to find a valid trace in a very early trywhile others keep searching with stagnating guesses.

When early deadlock prediction is deactivated(see section 6), many states and paths are exploredalthough an eventual deadlock may be certain. Exper-iment #6 (No-Deadl.-Prediction) is aimed to show theeffects of this deactivation, but owing to the huge in-crease of state space no test run did terminate withinthe first half an hour. Instead, they finally ended upwith memory exhaustion at about 1.1 million states.After about 20 minutes, each test run caused the Javagarbage collector to continuously occupy CPU whilesignificantly slowing down the overall test execution.Owing to this influence, no valid measurement fortime per state can be made.

Besides an immense number of investigated statesin the last experiment, a low average time per state isexpected due to the deactivation of the prediction al-gorithm. Situations are not expensively investigatedin detail to discover whether a deadlock will be un-avoidable. As a scientific finding, even exhaustivealgorithms which essentially increase time per statecan be profitable and drastically reduce overall plan-ning time as long as they reduce the state space sig-nificantly. A comparison of experiment #2 (bottom-up disabled) and experiment #1 (single-grasp andbottom-up) confirms this observation by the increasedtime per state but drastically reduced overall time.


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8 CONCLUSIONS ANDOUTLOOK

In this paper we introduced an approach for automaticplanning of manufacturing processes to build prod-ucts with robots. It bases on modules which aim forintegrating expert knowledge and at the same timeseparate different domain expertises such as automa-tion, process and application into independent com-ponents. The approach contains a heuristic searchbased on cost estimations to find a suitable manu-facturing program. Here, the modules contribute todifferent steps in order to solve the overall problem:They allow for deriving a structural model from anengineer’s construction plan of a goal product and areused to calculate processes to establish the structure.Furthermore, modules can plan concrete robot actionsto perform the overall manufacturing of the final prod-uct. We applied the presented concept to the domainof LEGO and used it to plan the manufacturing of aLEGO house with a robot. It has been evaluated withdifferent variations of heuristic properties.

As further work, we plan to improve the perfor-mance of the heuristic search, for example by re-ducing the state space through geometric reachabilityanalysis between manipulator and object. Especiallywhen planning with multiple manipulators, a strategyfor the selection of alternative manipulators for eachprocess action might be included into the approach.As a major future research question, we will inves-tigate strategies for parallel execution of tasks usingindependent manipulators. The goal is to make plan-ning for multiple robots in a multi-functional robotcell feasible without the rapidly growing planningtime seen in the multi-robot evaluation results of ourwork. In a further step, a second use case in a differ-ent domain – such as CFRP – will be investigated todemonstrate that the presented concepts can be gener-ically applied to other domains and in real world.

ACKNOWLEDGEMENTS

This work is partly funded by the German ResearchFoundation (DFG) under the TeamBotS grant.

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