Automatic Parameter Tuning of Motion Planning Algorithms

Motion planning algorithms attempt to find a good compromise between planning time and quality of solution. Due to their heuristic nature, they are typically configured with several parameters. In this paper we demonstrate that, in many scenarios, the widely used default parameter values are not ideal. However, finding the best parameters to optimise some metric(s) is not trivial because the size of the parameter space can be large. We evaluate and compare the efficiency of four different methods (i. e. random sampling, AUC-Bandit, random forest, and bayesian optimisation) to tune the parameters of two motion planning algorithms, BKPIECE and RRT-connect. We present a table-top-reaching scenario where the seven degrees-of-freedom KUKA LWR robotic arm has to move from an initial to a goal pose in the presence of several objects in the environment. We show that the best methods for BKPIECE (AUC-Bandit) and RRT-Connect (random forest) improve the performance by 4. 5x and 1. 26x on average respectively. Then, we generate a set of random scenarios of increasing complexity, and we observe that optimal parameters found in simple environments perform well in more complex scenarios. Finally, we find that the time required to evaluate parameter configurations can be reduced by more than 2/3 with low error. Overall, our results demonstrate that for a variety of motion planning problems it is possible to find solutions that significantly improve the performance over default configurations while requiring very reasonable computation times.

Authors

• José Cano 0001
• Yiming Yang
• Bruno Bodin
• Vijay Nagarajan
• Michael F. P. O'Boyle

Keywords

• Planning
• Tuning
• Optimization
• Bayes methods
• Manipulators
• Mathematical model
• Tuning Parameter
• Path Planning
• Planning Algorithm
• Random Sampling
• Random Forest
• Parameter Space
• Complex Scenarios
• Configuration Parameters
• Planning Time
• Bayesian Optimization
• Default Configuration
• Random Scenario
• Search Space
• Solution Method
• Number Of Objects
• Subset Of Individuals
• Typical Scenario
• Goal State
• Number Of Configurations
• Set Of Metrics
• Tuning Method
• Environmental Categories
• Upper Confidence Bound
• Sampling-based Methods
• Cost Path
• Tuning Algorithm
• Average Execution Time