From: AIPS 1998 Proceedings. Copyright © 1998, AAAI (www.aaai.org). All rights reserved. Causal Models of Mobile Service Robot Behavior Michael Beetz and Henrik Grosskreutz University of Bonn,Dept. of ComputerScience IIl, Roemerstr. 164, D-53117Bonn, Germany, email: beetz, [email protected] Abstract a priori unknownand varying. Theseflexible plans vi- olate assumptionsunderlying most action models used Temporalprojection, the process of predicting what for planning-- in particular the assumptionsabout the will happenwhen a robot executesits plan, is essen- irrelevance of the temporalstructure of actions and the tial for autonomousservice robots to successfullyplan exclusion of interferences betweenconcurrent actions. their missions. This paper describes a causal model of the behaviorexhibited by the mobilerobot RHINO Forestalling a wide range of behavior flaws typical whenrunning concurrent reactive plans for performing for service robots requires the planner to makeuse of office deliveryjobs. Themodel represents aspects of modelsthat represent continuous processes, exogenous robot behaviorthat cannotbe representedby mostac- events, interferences betweenconcurrent behavior, in- tion modelsused in AIplanning: it representsthe tem- poral structure of continuouscontrol processes, several complete information, and passive sensors. modesof their interferences, and variouskinds of un- This paper describes a certainty. This enhancedexpressiveness enables XFRM modelfor predicting the be- (McD92;BM94), a robot planningsystem, to predict, havior generated by a robot and thereforeforestall, variouskinds of behaviorflaws controller for office delivery includingmissed deadlines whilst exploitingincidental opportunities. Theproposed causal modelis experi- jobs. The controller is de- mentallyvalidated using the robot and its simulator. signed for robust and efficient executionof delivery plans on the autonomousmobile robot i} Introduction l:h-IINO (see Fig. 1), an Rwl Temporalprojection, the process of predicting what n21 robot. The causal model Fig. 1: RHINO will happenwhen a robot executes its plan, is essential represents various kinds of uncertainty, the temporal for autonomousservice robots to successfully plan their structure, and concurrentexecution of continuouscon- missions. For the projection of their plans robots must trol processes, and thereby enables the transforma- have causal modelsthat represent the effects of their tional planning system XF~M(McD92; BM97) to fore- actions. Most AI planning systems use fairly simple stall a variety of behavior flaws (BM94). modelsof their actions and restrict themselvesto work To predict possible execution scenarios quickly and on plans that are partially orderedsets of actions. to keep the representation of the scenarios concise Unfortunately, in autonomousrobot control we of- the causal modelsof continuous control processes are ten cannot consider plans as partially ordered sets of (partly) generated and revised during the temporal atomic actions without thwarting manyopportunities projection of the processes. This allows the projec- for improvingthe robots’ behavior through planning. tion algorithm to use causal models tailored for the The actions of robots have extents in both space and surrounding plan and its state of execution. These time and the informationavailable for planningis defi- context-specific causal modelspredict only those state cient. The actions’ extent and dependenceon time re- transitions caused by the continuous processes that quire planning systems to predict whenactions are ex- might affect the course of plan execution. ecuted, howlong they take, and howthey overlap with Weuse RHINO’Snavigation behavior as our princi- concurrent actions. Therestrictedness, unreliability, ple examplefor sensor-driven, concurrent control pro- and inaccuracy of sensors and effectors requires that cesses. Navigation behavior has several advantages planning systems computeflexible plans and are able overother kindsof controlprocesses: first., it’s difficult. to reason through contingencies whoselikelihoods are to imagine having a sophisticated planner for mobile Copyright© 1998 American A,~sociafion for Artificial Intelligence (www.am.org). All rights resexved. Beetz 163 From: AIPS 1998 Proceedings. Copyright © 1998, AAAI (www.aaai.org). All rights reserved. robot applications without having an adequate model construct WITH-POLICY. WITH-POLICY P B lneans "execute of navigation behavior; second, navigation is one of the primary activity B such that, the execution satisfies the best understood capabilities of mobile robots; and the policy P." Policies arc concurrent processes that third, navigation is a suitable means for experimentally run while the primary activity is active and interrupt validating the causal models of concurrent continuous the primary if necessary. processes. Events that require RHINOto perform actions such In this paper wc proceed as follows. The next se.c- as "passing a door" are handled through fluents, pro- tion describes the plans of the office delivery robot that grazn variables that signal changes of their values are to be projected. The rule language for specifying and thereby enable control threads to react to asyn- causal models of actions is sketched in the subsequent chronous events. For instance, the RPL statement section. The section on modeling RmNo’s behavior WHENEVERF B is an endless loop that executes B when- applies the rule language to model the base navigation ever the fluent. F gets the value "true." WA~T-FORF, plans and the events that occur in the environment. another control abstraction, blocks a thread of control Finally, we give some experimental resu[ts on the accu- until the lluent F becomestrue. rateness of the symbolic predictions of robot behavior. Whena delivery gets interrupted because the robot has detected that the door to A-120 is open, that op- Plans of an Office Delivery Robot portunity has a side effect: it movesthe robot into the We use structured r~:active plans (SRPs) to specify office A-120. The interrupted delivery plan has there- how the delivery robot is to respond to sensory input fore to be replanned before it can be continued. in order to accomplish its jobs. Theyarc written ill RPL This in mind, we implement the navigation routine (Reactive Plan Language) (Mc.Dgl), a LisP-like robot as a loop that generates and executes base navigation control language with conditionals, loops, local vari- plans until the robot has arrived at its destination. ables, processes, and subroutines. RPLprovides several Interrupts are handled ~’ terminating the current it- high-level concepts (interrupts, monitors) to synchro- eration of the loop and starting the next iteration in nize parallel actions, makeplans reactive, and so on. which a new navigation plan starting from RmNo’s new position is generated (BBFC98). Structured Reactive Delivery Plans To illustrate the advantages of high-level plans that Base Navigation Plans specify concurrent reactive behavior, we sketch a plan Base navigation plans are also specified as concurrent that specifies howR.I[mo (see Fig. 1) is to deliver mail reactive RPLplans. Figure 3 pictures such a navigation to the rooms A-120, A-113, A-121, and A-110. Ini- plan: that is automatically generated for a given desti- tially, the planner asked the robot to perform the de- nation by the SRP’s navigation planner. The plan con- liveries in the order A-120, A-113, A-121, and A-11{}. sists of two components. The first specifies a sequence However, because the room A-120 is closed the cor- of target points (the locations indexed by the numbers responding delivery cannot be completed. Therefore, 1 to 5 in Figure 3) to be reached by the robot. Tim the planning system revises the overall plan such that navigation between the target points is accomplished the robot is to accomplish the delivery for A- [20 as an by a standard path planner (TBB+98). opportunity. In other words, the robot will interrupt its current delivery to deliver the mail to A-120 (s~, Fig. 2) if the delivery can be completed. WITH-POLICY WHENEVERPASSING A DOOR A-II0 ESTIMATE DOOR ANGLE WITH-POLICY SEQ_ WAIT-FOR OPEN?(A-120) DELIVER MAIL TO Dieter 1. GO-TO(A-113) 2. GO-TO(A-121) BEFORE10:30 A-121’-- 3. GO-TO(A-110) Figure 2: Office delivery plan Constraints such as "whenever the robot passes a door it estimates the opening angle of the door using Fig. 3: Topological navigation plml its laser range finders" and opportunities such as "com- ’[’he second componentspecifics in detail when and plete the delivery to room A-120 as soon as you learn howthe robot is to adapt its travel modesas it follows the office is open," which are necessary for carrying out the navigation path (FBT97). In many indoor environ- the jobs opportunistically, are specified using the RPL ments it is advantageous to adapt the driving strategy 164 Robotics and Agents From: AIPS 1998 Proceedings. Copyright © 1998, AAAI (www.aaai.org). All rights reserved. to the surroundings: to drive carefully (and therefore An execution scenario represents how the execution slowly) within offices because offices are cluttered, to of a robot controller might go, that is, how the envi- switch off the sonars when driving through doorways ronment changes as the structured reactive controller (to avoid crosstalk between the sonars), and to drive gets executed. A timeline is a linear sequence of events quickly in the hallways. This part of the plan is de- and their results. Timelines represent