Proficiency Constrained Multi-Agent Reinforcement Learning for
Environment-Adaptive Multi UAV-UGV Teaming
- URL: http://arxiv.org/abs/2002.03910v3
- Date: Tue, 29 Jun 2021 14:46:56 GMT
- Title: Proficiency Constrained Multi-Agent Reinforcement Learning for
Environment-Adaptive Multi UAV-UGV Teaming
- Authors: Qifei Yu, Zhexin Shen, Yijiang Pang and Rui Liu
- Abstract summary: Mixed aerial and ground robot teams are widely used for disaster rescue, social security, precision agriculture, and military missions.
This paper developed a novel teaming method, proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide ground and aerial cooperation.
Mix-RL exploits robot capabilities while being aware of the adaptions of robot capabilities to task requirements and environment conditions.
- Score: 2.745883395089022
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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".
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