The definition and rendering of terrain maps
SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
The complexity of robot motion planning
The complexity of robot motion planning
Artificial Intelligence - Special issue on knowledge representation
Temporal difference learning and TD-Gammon
Communications of the ACM
Computer rendering of stochastic models
Communications of the ACM
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
International Journal of Robotics Research
Planning Algorithms
Journal of Field Robotics - Special Issue on Field and Service Robotics
Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles
International Journal of Robotics Research
Journal of Artificial Intelligence Research
Dynamic path planning with multi-agent data fusion: the parallel hierarchical replanner
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Non-parametric Learning to Aid Path Planning over Slopes
International Journal of Robotics Research
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This paper analyses solutions to the time-optimal planning and execution (TOPE) problem, in which the aim is to minimise the total time required for an agent to achieve its objectives. The TOPE process provides a means of adjusting system parameters in real-time to achieve this aim. Prior work by the authors showed that agent-based planning systems employing the TOPE process can yield better performance than existing techniques, provided that a key estimation step can be run sufficiently fast and accurately. This paper describes several real-time implementations of this estimation step. A Monte-Carlo analysis compares the performance of TOPE systems using these implementations against existing state-of-the-art planning techniques. It is shown that the average case performance of the TOPE systems is significantly better than the existing methods. Since the TOPE process can be added to an existing system without modifying the internal processes, these results suggest that similar performance improvement may be obtained in a multitude of robotics applications.