  A mixed aerial and ground robot team, which includes both unmanned ground
vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for
disaster rescue, social security, precision agriculture, and military missions.
However, team capability and corresponding configuration vary since robots have
different motion speeds, perceiving ranges, reaching areas, and resilient
capabilities to the dynamic environment. Due to heterogeneous robots inside a
team and the resilient capabilities of robots, it is challenging to perform a
task with an optimal balance between reasonable task allocations and maximum
utilization of robot capability. To address this challenge for effective mixed
ground and aerial teaming, this paper developed a novel teaming method,
proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide
ground and aerial cooperation by considering the best alignments between robot
capabilities, task requirements, and environment conditions. Mix-RL largely
exploits robot capabilities while being aware of the adaption of robot
capabilities to task requirements and environment conditions. Mix-RL's
effectiveness in guiding mixed teaming was validated with the task "social
security for criminal vehicle tracking".

Robotics;Artificial Intelligence