Topological Navigation and Path Planning

Transcription

1 Topological Navigation and Path Planning

2 Topological Navigation and Path Planning Based upon points of interest E.g., landmarks Navigation is relational between points of interest E.g., Go past the corner and enter the second doorway on the left Precise metric information not used Approaches are usually based upon graph representations

3 Recall: Difference between Topological and Metric

4 Landmarks Landmarks: One or more perceptually distinctive features of interest on an object or locale of interest Examples: corner, doorway, tree, sign, marker Can be artificial or natural Artificial: placed for the purpose of aiding navigation Natural: existing features not expressly designed for aiding navigation Roboticists avoid artificial landmarks!! Gateways: Special case of landmark, where robot has opportunity to change its overall direction of navigation Examples: intersection of hallways

5 Important Criteria for Landmarks Must be readily recognizable Passive, perceivable over an entire range of time, distinctive globally/locally Landmarks should be plentiful Must support the task dependent activity Can extract what you need from it (orientation, distance, etc.) Must be perceivable from many different viewpoints

6 Questions Should landmarks be distinguishable from each other? Can we use people as landmarks?

7 Example Landmarks

8 Two Categories of Route Representations Relational: most popular graph-style of spatial memory support path-planning Associative: coupling sensing with localization good for retracing known paths

9 Relational Methods Represent world as graph or network of nodes and edges Nodes: represent gateways, landmarks, or goals Edges: represent a navigable path between two nodes; can also have additional information attached (e.g., direction, terrain type, behaviors needed to navigate the path)

10 Multi-Level Spatial Hierarchy (Byun & Kuipers) Metric: distances, directions, shapes in coordinate system Topological: connectivity Landmark definitions, procedural knowledge for traveling

11 Distinctive Place Approach Distinctive place: landmark that robot can detect from nearby region called neighborhood Once robot in the neighborhood, it uses sensors to position itself relative to the landmark local control strategy distinctive place Edge in the relational graph: local control strategy (lcs) Procedure for getting from current node to next node When landmark sensed, hill-climbing used to drive to desired relative position

12 Example of Distinctive Place

13 Example of Local Control Strategies Basic behavior: follow-hall Releasers: look-for-t, look-for-dead-end, look-for-door, lookfor-blue

14 Exercises Create a relational graph for this floorplan Label each edge with the appropriate LCS: mtd, fh Label each node with the type of gateway: de, t, r de3 fh Room 1r1 Room r22 mtd mtd t1 fh t2 fh t3 fh mtd mtd fh de2 r4 Room 3r3 Room 4 de1

15 Distinctive Places: Pros and Cons Advantages: Eliminates navigational errors at each node Robot can build up metric information over multiple trips, since error will average out Supports discovery of new landmarks Disadvantages: Difficult to find good distinctive places Either too numerous, and thus not locally unique Or, too few, and thus hard to find Difficult to define and learn local control strategies

16 Associate Methods Create a behavior that converts sensor observations into direction to go to reach a particular landmark Assume a location or landmark has: Perceptual stability: views from nearby locations look similar Perceptual distinguishability: views far away should look different Associative methods are similar to distinctive place neighborhoods Difference: associative methods use coarse computer vision

17 Visual Homing Partition image into coarse subsections (e.g., 16) Each section measured based on some attribute E.g., edge density, dominant edge orientation, average intensity, etc. Resulting measurements yield image signature Image signature forms a pattern If robot nearby, should be able to determine direction of motion to localize itself relative to the location Visual homing: the use of image signatures to direct robot to specific location

18 Example of Visual Homing

19 Example of Visual Homing (Con t.)

20 QualNav Levitt and Lawton Basic idea: localize robot relative to particular orientation region, or patch of the world Orientation region: Defined by landmark pair boundaries Similar to neighborhood Within an orientation region, all landmarks appear in same relationship Vehicle can directly perceive when it has entered a new orientation region

21 Example of Orientation Regions

22 Example of Orientation Regions (Con t.)

23 Orientation Region Allows robot to create outdoor topological map as it explores the world Allows robot to coarsely localize itself Robot does not have to estimate range to landmarks Using angles to each landmark, it can move to follow desired angles

24 Associative Methods: Pros and Cons Advantages: Tight coupling of sensing to homing Robot does not need to explicitly recognize what a landmark is Enables robots to build up maps as it explores Disadvantages: Require massive storage Brittle in presence of dynamic world when landmarks may be occluded or changed

25 Case Study I Case Study I: topological navigation in hybrid architecture Example of 1994 AAAI Mobile Robot Competition approach of Colorado School of Mines Competition: Given previously unavailable topological map, enable robot to navigate from room to room in test environment within 15 minutes

26 Assumption of the Task The robot is given its starting node, but it is not given the direction it is initially facing relative to the map The topological map is structurally correct, but does not necessarily represent if a corridor or door is blocked Each door is marked with a landmark (such as room number)

27 Path Planning Approach Map entered with 3 node types: Room (R), Hall (H), Foyer (F) Assumed that environment is orthogonal Edges between nodes: N, S, E, W Edges weighted: 3 for segment beginning in foyer, 2 for going from room to room, 1 otherwise Additional node type added: Hd: refers to hall to door connection Path planner eliminates unneeded nodes based on start and goal nodes, then plans shortest path using Dijkstra s single source shortest path

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29 Map of the Environment

30 Abstract Navigation Behaviors To execute path, transition table defines abstract behaviors to be activated

31 Navigation Scripts Used to specify and carry out implied details in a modular and reusable way Navigate-door:

32 Navigation Scripts: Navigate-Hall

33 Navigation Scripts: Navigate-Foyer

34 How to Handle a Blocked Path? When robot detects a blocked condition: it triggers a moveto-goal behavior, which takes it back to the last known location it then updates the information on the map and generates a new plan

35 Case Study I: Lessons Learned Must build abstract navigation behaviors out of robust primitives Quality of the primitive behaviors is important Distance values between nodes can be different if traveling in different directions Metric distances might not be known for all nodes, making it difficult to apply Dijkstra s algorithm

36 Case Study II Topological map building in a behavior-based system Based on work of Mataric (1990) Robot, Toto: Designed using subsumption/behavior language Sensors: a ring of 12 sonar sensors Compass providing 4 bits of bearing

37 Low-Level Behaviors

38 Dynamic Landmark Detection Selection of landmarks Walls, corridors, junk Idea: allow robot to dynamically extract environmental features while it moves, and build up topological map based on features detected Landmark: Hypothesis with high degree of confidence Based only on sonar and compass readings Example landmark: Unilateral short sonar readings, coupled with consistent compass bearing, correspond to increased confidence in a wall landmark

39 Spatial Learning Landmarks stored in graph representation Data structure is linked list Connections determined based on adjacent landmarks Decentralized map representation; each node implemented in a distributed fashion

40 Learning a Map When landmark detected, type and compass bearing are broadcast to entire graph Initially, list of nodes is empty When a node receives a broadcast landmark, it compares its type, bearing and rough position to its own If no node reports a match, new landmark added to graph If match found: Either a loop has been found, or an error has occurred Estimated position is compared to robot s current rough position estimates If positions match within error bounds, assume path has looped

41 Example of Learned Map

42 Path Planning based upon Learned Map Path planning based on wave front propagation through graph Destination node propagates call to its neighbors Eventually, call reaches currently active node Robot travels in direction of wavefront

43 Case Study II: Lessons Learned Map building can be incorporated within subsumption methodology Globally consistent maps can be built in a distributed manner Coarse position estimates are sufficient to disambiguate landmarks in naturally occurring situations Global orientation estimates do not need to be precise or accurate, as long as they are locally consistent over time

44 Case Study III Combination of metric and topological mapping Pre-defined landmarks (e.g., doorways, hall openings, etc.) Build topological map of landmarks, connected with metric information

45 USC s Mapping Approach Example of landmarks detected:

46 USC s Mapping Approach (Con t.) Exploration strategy: Follow corridors Go to unexplored ends of nodes Mapping strategy: Detect and store topological features Correct odometry based on: Topological matches Orthogonality constraints

47 USC Mapping Results

48 Multi-Robot Mapping Experiments from USC

49 Summary of Topological Path Planning Landmarks Gateways Distinctive places Image signatures

50 Representational Issues for Behavioral Systems