Probabilistic control from time-bounded temporal logic specifications in dynamic environments

The increasing need for real time robotic systems capable of performing tasks in changing and constrained environments demands the development of reliable and adaptable motion planning and control algorithms. This paper considers a mobile robot whose performance is measured by the completion of temporal logic tasks within a certain period of time. In addition to such time constraints, the planning algorithm must also deal with changes in the robot's workspace during task execution. In our case, the robot is deployed in a partitioned environment subjected to structural changes in which doors shift from open to closed and vice-versa. The motion of the robot is modeled as a Continuous Time Markov Decision Process and the robot's mission is expressed as a Continuous Stochastic Logic (CSL) temporal logic specification. An approximate solution to find a control strategy that satisfies such specifications is derived for a subset of probabilistic CSL formulae. Simulation and experimental results are provided to illustrate the method.

Authors

• Ana I. Medina Ayala
• Sean B. Andersson
• Calin Belta

Keywords

• Robots
• Planning
• Probabilistic logic
• Dynamics
• Markov processes
• Switches
• Heuristic algorithms
• Dynamic Environment
• Temporal Logic
• Temporal Logic Specifications
• Probabilistic Control
• Workspace
• Path Planning
• Markov Decision Process
• Mobile Robot
• Robot Motion
• Planning Algorithm
• Probabilistic Model
• Optimal Control
• Transit Time
• Time Complexity
• Transition Probabilities
• Exponential Distribution
• Indoor Environments
• Set Of Regions
• Optimal Policy
• Time Of Occurrence
• Regional Environment
• Model Checking
• Robots In Environments
• Continuous-time Markov Chain
• Switching Events
• Exit Rate
• RFID Tags
• Optimal Operation
• Value Iteration
• Event Intervals