Fully Differentiable Adaptive Informative Path Planning

Kalvik Jakkala, Srinivas Akella

Abstract:

Autonomous robots can survey and monitor large
environments. However, these robots often have limited computational
and power resources, making it crucial to develop an efficient
and adaptive informative path planning (IPP) algorithm.
Such an algorithm must quickly adapt to environmental data to
maximize the information collected while accommodating path
constraints, such as distance budgets and boundary limitations.
Current approaches to this problem often rely on maximizing
mutual information using methods such as greedy algorithms,
Bayesian optimization, and genetic algorithms. These methods
can be slow and do not scale well to large or 3D environments.
We present an adaptive IPP approach that is fully differentiable,
significantly faster than previous methods, and scalable to
3D spaces. Our approach also supports continuous sensing
robots, which collect data continuously along the entire path,
by leveraging streaming sparse Gaussian processes.
Benchmark results on two real-world datasets demonstrate
that our approach yields solutions that are on par with or
better than baseline methods while being up to two orders
of magnitude faster. Additionally, we showcase our adaptive
IPP approach in a 3D space using a system-on-chip embedded
computer with minimal computational resources. Our code is
available in the SGP-Tools Python library with a companion
ROS 2 package for deployment on ArduPilot-based robots.