TY - GEN
T1 - Hierarchical Multi-objective planning
T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014
AU - Dennis Ding, Xuchu
AU - Englot, Brendan
AU - Pinto, Alessandro
AU - Speranzon, Alberto
AU - Surana, Amit
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/22
Y1 - 2014/9/22
N2 - We propose a hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning problem across different layers, leveraging the difference in spatial and temporal scales of the mission objectives. We show that this framework facilitates contingency management under unanticipated events. Interaction between the various layers requires consistent model abstractions and common message semantics. To satisfy these requirements, we adopt a generic knowledge-based architecture that is independent from a specific application domain. We show a specific instance of our framework using a Constrained Markov Decision Process (CMDP) planner at the higher level and a Multi-Objective Probabilistic Roadmap (MO-PRM) planner at the lower level. The resulting planning system is tested in a realistic scenario where an agent is tasked with a mission in a large urban threat rich environment under dynamic uncertain conditions. The mission specification includes a Linear Temporal Logic (LTL) formula that defines the desired behaviors, a list of metrics to be optimized and a list of constraints on time, resources and probability of mission success.
AB - We propose a hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning problem across different layers, leveraging the difference in spatial and temporal scales of the mission objectives. We show that this framework facilitates contingency management under unanticipated events. Interaction between the various layers requires consistent model abstractions and common message semantics. To satisfy these requirements, we adopt a generic knowledge-based architecture that is independent from a specific application domain. We show a specific instance of our framework using a Constrained Markov Decision Process (CMDP) planner at the higher level and a Multi-Objective Probabilistic Roadmap (MO-PRM) planner at the lower level. The resulting planning system is tested in a realistic scenario where an agent is tasked with a mission in a large urban threat rich environment under dynamic uncertain conditions. The mission specification includes a Linear Temporal Logic (LTL) formula that defines the desired behaviors, a list of metrics to be optimized and a list of constraints on time, resources and probability of mission success.
UR - http://www.scopus.com/inward/record.url?scp=84929180159&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929180159&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907400
DO - 10.1109/ICRA.2014.6907400
M3 - Conference contribution
AN - SCOPUS:84929180159
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3735
EP - 3742
BT - Proceedings - IEEE International Conference on Robotics and Automation
Y2 - 31 May 2014 through 7 June 2014
ER -