Risk-aware path planning using hirerachical constrained Markov Decision Processes

Next generation industrial plants will feature mobile robots (e.g., autonomous forklifts) moving side by side with humans. In these scenarios, robots must not only maximize efficiency, but must also mitigate risks. In this paper we study the problem of risk-aware path planning, i.e., the problem of computing shortest paths in stochastic environments while ensuring that average risk is bounded. Our method is based on the framework of constrained Markov Decision Processes (CMDP). To counterbalance the intrinsic computational complexity of CMDPs, we propose a hierarchical method that is suboptimal but obtains significant speedups. Simulation results in factory-like environments illustrate how the hierarchical method compares with the non hierarchical one.