Multi-Agent Spatial Predictive Control with Application to Drone Flocking

We introduce Spatial Predictive Control (SPC), a technique for solving the following problem: given a collection of robotic agents with black-box positional low-level controllers (PLLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the PLLC. Using a simulation environment, we show that SPC outperforms Potential Field Controllers, a related class of controllers, on the drone flocking problem. We also show that SPC works on real hardware, and is therefore able to cope with the potential sim-to-real transfer gap. We demonstrate its performance using as many as 16 Crazyflie 2. 1 drones in a number of scenarios, including obstacle avoidance.

Authors

• Andreas Brandstätter
• Scott A. Smolka
• Scott D. Stoller
• Ashish Tiwari 0001
• Radu Grosu

Keywords

• Computational modeling
• Cost function
• Robot sensing systems
• Hardware
• Stability analysis
• Robustness
• Sensors
• Spatial Control
• Time Step
• Target Location
• Model Plant
• Distributed Control
• Number Of Scenarios
• Obstacle Avoidance
• Position Of Agent
• Environmental Observations
• Real Hardware
• Robotic Agents
• Performance Metrics
• Local System
• Maximum Distance
• Simulation Experiments
• Control Input
• Local Function
• Start Position
• Gradient Direction
• Model Predictive Control
• Robot Operating System
• Global Navigation Satellite System
• Hardware Experiments
• Multi-agent Systems
• Sensor Noise