PROBLEMS
Managing and optimizing the movement of multiple autonomous agents, such as drones in aerospace or robots in warehouses , is complex due to numerous constraints (no-fly zones, positioning accuracy, energy limitations). Collision avoidance in fleet or swarm drone missions is critical and computationally challenging. Classical algorithms struggle with the scale and complexity of multi-drone coordination.
SOLUTIONS
Quandela has developed a Quantum Reinforcement Learning (QRL) algorithm, a solution with the potential to provide faster results and increase the number of constraints considered in multi-agent traffic optimization.
BENEFITS
Our solution holds the potential to create more efficient and safer operations in complex environments, from drone fleets to warehouse automation, as well as improved collision avoidance systems and optimized path planning for autonomous agents. This could represent a competitive advantage in the growing markets of drone technologies and warehouse automation potentially increasing operational efficiency and reducing costs. We have successfully tested a proof of concept.