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Dynamic Task Allocation for Robotic Teams Using Potential Fields

This paper explores how multiple robots divide tasks in dynamic environments. It introduces role assignment strategies and coordination techniques through shared potential fields. By minimizing risks and adapting to changes, robots communicate and bid for roles based on local and broadcasted information. The field potentials guide robots in selecting optimal positions based on various factors like wall proximity, ball orientation, teammate distance, and offensive or defensive biases. Results show coordinated robots perform more efficiently in task allocation. The study suggests potential applications beyond robotic soccer with minor adaptations. Key concepts include dynamic role assignment, shared potential fields, communication strategies, and task distribution in multi-robot settings.

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Dynamic Task Allocation for Robotic Teams Using Potential Fields

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  1. Multi-Robot Dynamic Role Assignment and Coordination Through Shared Potential Fields Mark Holak

  2. The Problem: • How to divide tasks among multiple agents • How does an agent select a task • How do agents reselect a task when the environment changes • Known Risks: • The world is dynamic • Known and unknown agents exist • Unknown agents work to inhibit goals • Possible kidnapping and repositioning • Some objects of interest could become occluded • Solution: • Agents must choose actions that minimize risk while communicating with teammates and adapting to a changing world.

  3. Communication • The Sony AIBO uses a Wi-Fi connection that allows the robot to communicate with other AIBOs • Each robot broadcasts information about its position and status, and in turn receives similar broadcasts from the other robots • Current Position • Ball Position • State Flag This broadcast happens twice a second and must be interpreted at the same rate.

  4. Role Assignment • There are 4 types of roles that this paper recognizes: • Goalie • Static role • Attacker • Only concerns itself with directly engaging the ball and attempting to score • Offensive Supporter • Positions itself relative to the attacker, ball, and opponent's goal • Defensive Supporter • Positions itself relative to the attacker, ball, and its own goal

  5. Role Assignment • Bidding on a role: • A bid is calculated by the angle between the robot-to-ball and ball-to-goal • Bids are calculated using local information and broadcasted information • Based on local calculations, the robot will assume the role if it calculates itself to be the highest bidder. • If the robot looses a bid, it will assume that another robot calculated itself as the winner and will bid on the next role • Once a role is assigned to a robot, that robot maintains the role for a few seconds before relinquishing that role

  6. Role Assignment

  7. Field Potentials • There are 8 different calculations performed to determine the gradients of the field potentials: • Wall – The distance from walls and team’s goal zone • Ball – [OS] Positions that is balanced in relation to the goal, ball, and attacker • Teammate – The robot will prefer locations away from teammates • Forward Bias – [OS] Positions the robot parallel or in front of the ball • Defensive Bias – [DS] Positions the robot closer to the defending goal • Ball Corridor – Robots will look for positions that do not block the attackers potential shot • Block Goal – [DS] Prefer the locations between the ball and defending goal • Side Bias – [OS] The robot should position itself across the field from the primary attacker

  8. Field Potentials

  9. Results The coordinated robots, overall, worked more efficiently than the other two test; however, the difference between the single robot and coordinated robots was not significant enough to consider one more effective then the other.

  10. Questions Could this work be applied to other areas other than robot soccer with minimal changes? Andrew Courter What is Potential Fields in this paper [in plain words]? Songxin Li (Dustin) & Xiang Li

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