Motion Planning for Retrieval Agents

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Motion Planning for Retrieval Agents

• Relevant Papers
• Better Group Behaviors using Rule-Based Roadmaps
• O. Burchan Bayazit, Jyh-Ming Lien, and Nancy M.
  Amato
• Steering Behaviors for Autonomous Characters
• Craig W. Reynolds

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Motion Planning for Agents

• Objective Given a known workspace and a
  pre-computed roadmap that covers the space
• Visit every node and edge at least once
• Retrieve all scattered particles

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Assumptions

• Agents have no prior knowledge of particle
  locations
• Agents have omni-directional line of sight vision
  capabilities (not constrained by distance or
  angle)
• There is no direct communication between agents
  but they communicate indirectly by encoding
  information in the roadmap
• Global information
• Node Positions
• Geometry and location of obstacles

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Roadmap

• Roadmap contains a set of nodes from which entire
  polygonal workspace is visible
• Nodes represent feasible agent configurations
• Edges represent feasible sub-paths
• Roadmap is adaptive edge weights are updated by
  agents

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Agent Strategies

• Exploring
• Objective is to traverse the roadmap until it
  finds a particle
• Homing
• Objective is to carry a discovered particle to a
  drop-off location

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Algorithms

• Exploring Strategy
• Each agent moves independently
• All edges have equal weights to start with
• At each node, an agent chooses an edge to follow
  based on the weights of the nodes attached
  edges. Similar to ant colony optimization,
  guarantees all nodes eventually get visited
• The weight of the chosen edge is increased

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Algorithms

• Exploring Strategy
• AgentVar Node nextNode
• AgentVar Edge edge
• edge node.probMinEdge()
• nextNode edge.end
• agent.goal nextNode.position
• edge.weight edge.weight1

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Algorithms

• Homing Strategy
• Agent accesses global data to retrieve a path
  from its current position to the drop-off location

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Algorithms

• Homing Strategy
• AgentVar LinkedList path
• AgentVar Node nextNode
• AgentVar Node currentNode
• AgentVar Edge edge
• Path global.getPath(thisPoint, dropOff)
• while(!path.empty())
• nextNode path.getNextNode()
• edge currentNode.getEdge(nextNode)
• agent.goal nextNode.position

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Velocity-Aligned Local Coordinate Space

• Side Vector

• Forward Vector

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Agent Physical Model

• Based on a point mass approximation
• Model Properties
• mass scalar
• position vector
• velocity vector
• max_force scalar
• max_speed scalar
• orientation 2 basis vectors

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Agent Dynamics

• Based on forward Euler integration method
• At each time step behaviorally determined
  steering forces (limited by max_force) are
  applied to model

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Agent Dynamics

• steering_force truncate (steering_direction,
  max_force)
• acceleration steering_force / mass
• velocity truncate (velocity acceleration,
  max_speed)
• position position velocity
• newForward normalize(velocity)
• newSide.x newForward.y
• newSide.y newForward.x

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Steering Behaviors

• Seeking (targeting a goal)
• Separation (from other agents)
• Obstacle Avoidance

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Seeking Steering Behavior

• desired_velocity normalize (target - position)
  max_speed
• steering desired_velocity - velocity

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Separation Steering Behavior

• Agents considers agents in its neighborhood
• Neighborhood is characterized by distance
  (defines when two agents are nearby) and angle
  (defines agents field of view)
• Agents outside neighborhood are not considered

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Separation Steering Behavior

• Compute repulsion force for each agent in
  neighborhood
• repulsionForce (-1/r)normalize
  (neighborPosition position)
• Sum up repulsion forces from all neighbors
• steeringForce repulstionForce

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Separation Steering Behavior
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Obstacle Avoidance Steering

• Implemented when an obstacle lies in the path of
  an agent (range specified by a safe_distance
  parameter)
• Goal is to maintain an imaginary rectangular free
  space in front of the agent
• On encountering an obstacle, agent steers to the
  nearest visible node that lies in its general
  direction

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Obstacle Avoidance Steering
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Combining Behaviors

• At each time step, select (in pre-determined
  sequence) one steering component to apply
• Apply relevant weighting factors, max_force and
  max_speed, to each steering component

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Demo