This paper studies the problem of real-time routing in a multi-autonomous robot enhanced network at uncertain and vulnerable tactical edge. Recent network protocols, such as opportunistic mobile network routing protocols, engaged social network in communication network that can increase the interoperability by using social mobility and opportunistic carry and forward routing algorithms. However, in practical harsh environment such as a battlefield, the uncertainty of social mobility and complexity of vulnerable environment due to unpredictable physical and cyber-attacks from enemy, would seriously affect the effectiveness and practicality of these emerging network protocols. This paper presents a GT-SaRE-MANET (Game Theoretic Situation-aware Robot Enhanced Mobile Ad-hoc Network) routing protocol that adopt the online reinforcement learning technique to supervise the mobility of multi-robots as well as handle the uncertainty and potential physical and cyber attack at tactical edge. Firstly, a set of game theoretic mission oriented metrics has been introduced to describe the interrelation among network quality, multi-robot mobility as well as potential attacking activities. Then, a distributed multi-agent game theoretic reinforcement learning algorithm has been developed. It will not only optimize GT-SaRE-MANET routing protocol and the mobility of multi-robots online, but also effectively avoid the physical and/or cyber-attacks from enemy by using the game theoretic mission oriented metrics. The effectiveness of proposed design has been demonstrated through computer aided simulations and hardware experiments.