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Motion Planning for Retrieval Agents

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. Motion Planning for Agents.

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Motion Planning for Retrieval Agents

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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 node’s attached edges. Similar to ant colony optimization, guarantees all nodes eventually get visited • The weight of the chosen edge is increased

  7. Algorithms • Exploring Strategy • AgentVar Node nextNode; • AgentVar Edge edge; • edge = node.probMinEdge(); • nextNode = edge.end; • agent.goal = nextNode.position; • edge.weight = edge.weight+1;

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

  9. 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;

  10. Velocity-Aligned Local Coordinate Space Side Vector Forward Vector

  11. 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

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

  13. 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;

  14. Steering Behaviors • Seeking (targeting a goal) • Separation (from other agents) • Obstacle Avoidance

  15. Seeking Steering Behavior • desired_velocity = normalize (target - position) * max_speed • steering = desired_velocity - velocity

  16. Separation Steering Behavior • Agents considers agents in its neighborhood • Neighborhood is characterized by distance (defines when two agents are nearby) and angle (defines agent’s field of view) • Agents outside neighborhood are not considered

  17. 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

  18. Separation Steering Behavior

  19. 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

  20. Obstacle Avoidance Steering

  21. 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

  22. Demo

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