201 research outputs found
Neural Potential Field for Obstacle-Aware Local Motion Planning
Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video
Machine Learning For Robot Motion Planning
Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover\u27s problem to more complicated kinodynamic planning problems, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with trade offs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly, semi-automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems
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