Robotic Motion Planning: Bug Algorithms

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BUG 2 More formally• Let q L 0 = q start; i = 1• repeat– repeat• from q L i-1 move toward q goal along the m-line– until goal is reached or obstacle encountered at q H i– if goal is reached, exit– repeat• follow boundary– until q goalis reached or q H iis re-encountered orm-line is re-encountered, x is not q H i , d(x,q goal) < d(q H i ,q goal) and wayto goal is unimpeded– if goal is reached, exit– if q H iis reached, return failure– else• q L i = m• i=i+1• continue16-735, Howie Choset with slides from G.D. Hager and Z. Dodds
head-to-head comparisonor thorax-to-thorax, perhapsDraw worlds in which Bug 2 does better than Bug 1 (and vice versa).Bug 2 beats Bug 1Bug 1 beats Bug 216-735, Howie Choset with slides from G.D. Hager and Z. Dodds
Page 1 and 2: Robotic Motion Planning:Bug Algorit
Page 3 and 4: • Workspace WA Few General Concep
Page 5 and 6: Buginner Strategy“Bug 0” algori
Page 7 and 8: Buginner Strategy“Bug 0” algori
Page 9: Bug ZapperWhat map will foil Bug 0
Page 12 and 13: Bug 1But some computing power!• k
Page 14 and 15: “Quiz”Bug 1 analysisBug 1: Path
Page 16 and 17: How Can We Show Completeness?• An
Page 18 and 19: A better bug?Call the line from the
Page 20 and 21: A better bug?m-line“Bug 2” Algo
Page 22 and 23: A better bug?“Bug 2” Algorithm1
Page 26 and 27: head-to-head comparisonor thorax-to
Page 28 and 29: BUG 1 vs. BUG 2• BUG 1 is an exha
Page 30 and 31: “Quiz”Bug 2 analysisBug 2: Path
Page 32 and 33: A More Realistic Bug• As presente
Page 34 and 35: Intervals of Continuity• Tangent
Page 36 and 37: Minimize Heuristic ExampleAt x, rob
Page 38 and 39: Transition from Motion-to-GoalChoos
Page 40 and 41: Boundary FollowingMove toward the O
Page 42 and 43: Example: Zero Senor Range1. Robot m
Page 44 and 45: Example: Infinite Sensor Range16-73
Page 46 and 47: The Basic Ideas• A motion-to-goal
Page 48 and 49: Implementing Tangent Bug• Basic p
Page 50 and 51: Tactile sensorson/off switchas a lo
Page 52 and 53: Infrared sensors“Noncontact bump
Page 54 and 55: Infrared calibrationenergy vs. dist
Page 56 and 57: walls(obstacles)Sonar effectssonarD
Page 58 and 59: Sonar effects(a) Sonar providing an
Page 60 and 61: Sonar Modelingresponse model (Kuc)s
Page 62 and 63: Laser RangingLIDARSick LaserLIDAR m

Robotic Motion Planning: Bug Algorithms

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