A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies

Area of Science:

• Robotics and Artificial Intelligence
• Swarm Intelligence
• Mission Planning and Optimization

Background:

• Swarm robotic systems with limited localization require collaborative motion-control strategies for multi-task missions.
• Existing mission-planning methods for swarms divide robots into workers and support roles, optimizing sequentially, leading to suboptimal plans.
• Current approaches optimize worker robot plans first, then use rule-based methods for support robots, failing to achieve swarm-level optimality.

Purpose of the Study:

• To present a novel mission-planning methodology that concurrently optimizes plans for both worker and support robots in swarm systems.
• To improve overall swarm mission execution performance by addressing the limitations of sequential planning strategies.
• To develop a pre-implementation estimator for predicting performance improvements achievable with the proposed methodology.

Main Methods:

• A five-stage concurrent optimization methodology: division-of-labor, task-allocation, worker robot path-planning, movement-concurrency, and movement-allocation.
• Simultaneous optimization of all planning stages to find optimal variables for worker and support robots.
• Development of a machine learning-based pre-implementation estimator to forecast performance gains.

Main Results:

• The proposed concurrent methodology significantly enhances swarm mission execution performance by nearly 40% over sequential methods.
• The concurrent approach effectively plans for simultaneous facilitation of multiple independent worker robot group movements.
• The pre-implementation estimator demonstrated high accuracy, achieving an estimation error of less than 5%.

Conclusions:

• Concurrent optimization of worker and support robot plans is crucial for efficient swarm mission execution.
• The novel methodology offers a more effective approach to swarm mission planning, applicable to various collaborative strategies.
• The developed estimator provides a valuable tool for justifying computational resources for advanced mission planning.