Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation.
IEEE International Conference on Robotics and Automation(2020)
Abstract
Progress in autonomous mobile robotics has seen significant advances in the development of many algorithms for motion control and path planning. However, robust performance from these algorithms can often only be expected if the parameters controlling them are tuned specifically for the respective robot model, and optimised for specific scenarios in the environment the robot is working in. Such parameter tuning can, depending on the underlying algorithm, amount to a substantial combinatorial challenge, often rendering extensive manual tuning of these parameters intractable. In this paper, we present a framework that permits the use of different navigation actions and/or parameters depending on the spatial context of the navigation task. We consider the respective navigation algorithms themselves mostly as a "black box", and find suitable parameters by means of an iterative optimisation, improving for performance metrics in simulated environments. We present a genetic algorithm incorporated into the framework, and empirically show that the resulting parameter sets lead to substantial performance improvements in both simulated and real-world environments in the domain of agricultural robots.
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Key words
simulated environments,genetic algorithm,resulting parameter sets,substantial performance improvements,agricultural robots,context dependant iterative parameter optimisation,robust robot navigation,autonomous mobile robotics,motion control,path planning,robust performance,robot model,parameter tuning,underlying algorithm,substantial combinatorial challenge,extensive manual tuning,navigation actions,spatial context,navigation task,respective navigation algorithms,iterative optimisation,performance metrics
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