High level software architecture for autonomous mobile robot

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2High level architectureThe key idea of the architecture is task decomposition. Contrary to commonlyused architectures we divide robot tasks into simple subtasks that are inserted intohierarchical tree during robot mission according to current situation observed byrobot sensors. As robot is supposed to operate both indoor and outdoor and performvarious types of missions, there are two levels in the architecture, first onedeals with the mission type, second one with currently performed mission, as denotedon figure 1. Mission level is controlled by Mission manager (MM) – the topcontrol object in the architecture that receives the initial global goal, sets appropriatemission and processes outer requests. Missions are of certain types, regardlessthe environment. Commonly used mission types might be e.g. Motion from locationA to location B on free space, Motion from on the path, Wandering on freespace, Mapping - motion in unknown environment, Manual control, etc.During robots operation there is only one active mission at any time; MM processesthe outer requests by forming mission tree or queue according to the request.For example, initial mission is wandering on the parking lot in front of the building.MM creates appropriate mission, sets it as current and starts. When outer requestarrives, e.g. drive into nearby building to the office, queue of missions iscreated, firstly motion on free space (parking lot) to the road entrance, secondlymotion on the road to the building, finally motion on free space inside.Initial goal definitionOuter requestMission Manager- current mission- mission treeMission- type- status- task managerTask manager- mission- status- current task- tasks treeTask- type- statusFigure 1. Top level decompositionThe decomposition inside each mission is similar; however instead of missionqueue the hierarchical tree of tasks is created. MM is replaced by Task manager(TM), structure existing only once (globally), assigned to currently performedmission. TM holds and services the tree of tasks. Each task represents a simplegoal, commonly used tasks are e.g. Move on path, Move on free space, Localize,Avoid obstacle, Solve trap, Return to base, etc.The task is performed until it is successfully finished or fails. Depending on failurereason the appropriate new task is assigned by TM and inserted into the tasks
3tree (unsuccessfully finished task remains open). Failure reasons do not necessarilyhave to be of high level causes. It could be hardware failure, suddenly appearingobstacle, etc. Reactive model is used for low level, e.g. robot performs emergencystop maneuver when confronted with suddenly appearing obstacle, andmessage is sent to current task, causing it to fail. The flow can be illustrated onfigure 2, where example with obstacle avoidance is given. The robot is moving onthe path (for now we do not care how the path is recognized, how robot steers onthe path, as it is what the task deals with) and task fails as there is obstacle detected(figure 2a). Due to the failure the TM inserts appropriate task into the tree.Avoid obstacle task fails as there is no way how obstacle can be avoided (figure2b). TM now inserts new task into the tree, the Solve trap task (figure 2c). Oncethe trap is solved, the task ends and flow returns to upper task (figure 2d) and thismechanism continues until the motion on the path is restored (figure 2f).Move on pathObstacledetectedMove on pathAvoid obstacleMove on pathAvoid obstacle(a)Obstacle(b) (c)unavoidableSolve trapMove on pathAvoid obstacleMove on pathAvoid obstacleMove on pathContinue on pathObstacle avoidedSolve trap(d)Trap solved(e) (f)Figure 2. Task tree exampleThe limited space does not allow us to go into greater detail in all the tasks, let’stake a look at least into Avoid obstacle task. The flow is shown on figure 3. Thetask first determines whether the obstacle is static or dynamic. In case of static obstaclethe task determines if it can be avoided, if yes the planning algorithm replansthe trajectory and task returns success. In case of dynamic obstacle the taskhas to resolve if it can be avoided, and if yes it must decide whether to replan thetrajectory or use velocity tuning algorithm (“wait” for the obstacle to move away).There are several ways how to build the core of TM, the decision mechanismwhich selects appropriate tasks for insertion in the case of current task failure.Currently we are using simple set of rules that acts as primitive expert system,with low level functions “hard wired” (e.g. in the case of hardware componentfailure the low level functions independently stop the robot immediately, reportthe HW failure to current task in operation, task fails and whole mission isaborted). However the simple set of rules can be replaced with e.g. Bayesian baseddecision maker, etc.
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High level software architecture for autonomous mobile robot

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