robot motion planning: approaches and research issues
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Robotics and Artificial Intelligence Laboratory Indian Institute of Information Technology, Allahabad. Robot Motion Planning: Approaches and Research Issues. Rahul Kala. Problem Solving in Mobile Robotics. Environment. Data Collection. Environment Understanding. Localization. - PowerPoint PPT PresentationTRANSCRIPT
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Robot Motion Planning: Approaches and Research Issues
Rahul Kala
12th June, 2014rkala.inIIIT Allahabad
ROBOTICS AND ARTIFICIAL INTELLIGENCE LABORATORYINDIAN INSTITUTE OF INFORMATION TECHNOLOGY,
ALLAHABAD
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Problem Solving in Mobile Robotics
Environment
Data Collection
Environment
Understanding
Sensor Fusion
Map buildingLocalization
Planning Control Manipulation
R. Tiwari, A. Shukla, R. Kala (2013) Intelligent Planning for Mobile Robotics:Algorithmic Approaches, IGI Global Publishers,Hershey, PA.
rkala.inRobot Motion PlanningIIIT Allahabad
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PlanningStrategi
c Plannin
gMilesto
ne Plannin
g Path
Planning
Obstacle
Avoidance
Control
Abstration
R. Tiwari, A. Shukla, R. Kala (2013) Intelligent Planning for Mobile Robotics:Algorithmic Approaches, IGI Global Publishers,Hershey, PA.
rkala.inRobot Motion PlanningIIIT Allahabad
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Problem Definition
rkala.inRobot Motion PlanningIIIT Allahabad
Start
Goal
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Objective
Travel Time
Travel Speed
Travel Distance
Fuel Economy
Passenger Comfort
Clearance
Smoothness
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Research Issues
Large offline/onlin
e computation
Holonomicity
Unstructured
environment
Sensing/control errors
Single/limited
obstacle/robot
environments
Congested environment
sNarrow
CorridorsDynamic
Environment
A priori known
environmentWide maps
Trap-prone environment
sHuman
Assistance
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Base Algorithms
Algorithms
Deliberative
Graph Search Based
A*
Sampling Based
PRM RRT
Optimization Based
Genetic Algorithm
Reactive
Fuzzy Logic
Artificial Potential
Fields
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Pros and Cons: Graph search based
Pros• Resolution
Optimal• Resolution
Complete
Cons• Time
Complexity• Discrete
states• Discrete
action sets • Holonomicit
y*
Research • Dynamic A*
(D*)• Any theta
A*• ε optimal A*
* Can be controlled with a different modeling. Not implemented in the codes givenrkala.inRobot Motion PlanningIIIT
Allahabad
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Pros and Cons: PRM
Pros• Probabilistica
lly Optimal• Probabilistica
lly Complete• Reasonable
Computation time
Cons• Narrow
corridor problem
• Roadmap generation not for dynamic environments
• Holonomicity
Research
• Lazy PRM• Vision based
PRM• K-
connectivity PRM
• PRM without cycles
• Obstacle based sampling
• Suited to non-holonomicity
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Pros and Cons: RRT
Pros• Probabilistic
ally Complete
• Near real time performance
Cons• Narrow
corridor problem
• Not optimal• Voronoi bias• Practically
not complete
Research• RRT-Connect• Graph based• Local trees• Obstacle
based sampling
• Exploration in partially known environments
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Pros and Cons: Genetic Algorithm
Pros• Probabilistica
lly Complete• Probabilistica
lly Optimal
Cons• Narrow
corridor problem
• Computationally Expensive
• Practically not complete
Research• Shorten
Operator• Variable
Length Chromosome
• Multi-objective optimization
• Memetic Computation
• Lazy collision checker
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Pros and Cons: Reactive Methods
Pros• Real time• Can
accommodate uncertainties
Cons• Not
optimal• Not
complete• Trap prone
Research• Training
methods• Input
modeling• Heuristic
decision making
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And some ‘hybrids’
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A* and Fuzzy
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R. Kala, A. Shukla, R. Tiwari (2010) Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning. Artificial Intelligence Review, 33(4): 275-306.
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A ‘better’ Genetic Algorithm
R. Kala, A. Shukla, R. Tiwari (2011) Robotic Path Planning using Evolutionary Momentum based Exploration. Journal of Experimental and Theoretical Artificial Intelligence, 23(4): 469-495.
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Variable Length
IndividualSoft
Mutation
Hard Mutation Elite
Insert Repair
Shorten
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Genetic Algorithm + Genetic Algorithm
R. Kala, A. Shukla, R. Tiwari (2010) Dynamic Environment Robot Path Planning using Hierarchical Evolutionary Algorithms. Cybernetics and Systems, 41(6): 435-454.
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Hierarchical A*
R. Kala, A. Shukla, R. Tiwari (2011) Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness. Neurocomputing, 74(14-15): 2314-2335. rkala.inRobot Motion PlanningIIIT
Allahabad
Multi Resolution Graph Representation
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Hierarchical A*
R. Kala, A. Shukla, R. Tiwari (2011) Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness. Neurocomputing, 74(14-15): 2314-2335. rkala.inRobot Motion PlanningIIIT
Allahabad
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2-layered Dynamic Programming
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R. Kala, A. Shukla, R. Tiwari (2012) Robot Path Planning using Dynamic Programming with Accelerating Nodes. Paladyn Journal of Behavioural Robotics, 3(1): 23-34.
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And all this extended to
Multi-Robotics
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A* + GA
R. Kala (2013) Multi-Robot Motion Planning using Hybrid MNHS and Genetic Algorithms. Applied Artificial Intelligence, 27(3): 170-198.
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Rapidly-exploring Random Graphs
R. Kala (2013) Rapidly-exploring Random Graphs: Motion Planning of Multiple Mobile Robots. Advanced Robotics, 27(14): 1113-1122.
rkala.inRobot Motion PlanningIIIT Allahabad
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Coordination using Local Optimization
R. Kala (2014) Coordination in Navigation of Multiple Mobile Robots. Cybernetics and Systems, 45(1): 1-24.
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Coordination using Local Optimization
R. Kala (2014) Coordination in Navigation of Multiple Mobile Robots. Cybernetics and Systems, 45(1): 1-24.
rkala.inRobot Motion PlanningIIIT Allahabad
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Coordination using A* + Fuzzy
R. Kala (2014) Navigating Multiple Mobile Robots without Direct Communication. International Journal of Intelligent Systems, DOI: 10.1002/int.21662 [Accepted, In Press].
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Thank You
Complex Mobile Navigation and Manipulation
IIIT Allahabad
rkala.ingcnandi.co.nr