TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning (2203.12830v2)

Abstract: Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces.

• The paper presents TIGRIS, which uses informed sampling to effectively navigate high-dimensional search spaces for enhanced data collection.
• It integrates a novel edge reward mechanism that improves information gain by 18% while addressing sensor and budget constraints.
• The study demonstrates practical applications, such as fixed-wing UAV missions, paving the way for advanced robotic informative path planning.

An Analysis of TIGRIS: A Sampling-based Algorithm for Informative Path Planning

The paper "TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning" by Moon, Chatterjee, and Scherer addresses the critical challenge of informative path planning (IPP) in high-dimensional search spaces faced by autonomous robotic systems. The authors introduce TIGRIS, a novel algorithm that extends conventional sampling-based planning methods to improve efficiency and effectiveness in collecting informative data under budgetary constraints.

TIGRIS is fundamentally designed to overcome limitations associated with high-dimensional search spaces and non-trivial sensor constraints, which often result in NP-hard and PSPACE-hard problems for IPP. The paper specifically highlights two key contributions that enhance the informative path planning process: informed sampling within a reduced configuration space and the innovative incorporation of edge rewards to more accurately estimate path information gain while adhering to path budget constraints.

In contrast to traditional random sampling techniques, TIGRIS employs informed sampling that leverages prior knowledge, thus prioritizing exploration of paths with high potential rewards. This focused sampling strategy enables the algorithm to generate more informative paths more rapidly, particularly crucial in expansive or high-complexity environments. The paper quantitatively demonstrates TIGRIS's superior performance compared to a baseline sampling-based planner, with a noted average improvement of 18.0% in information gain across various test scenarios.

The authors detail an example use case involving a fixed-wing unmanned aerial vehicle (UAV) equipped with a forward-facing camera tasked with finding multiple objects across a large area. They introduce a novel edge cost formulation that includes edge rewards, arguing its necessity despite the additional computational overhead. This approach effectively accounts for the continuous collection of information along the path, which is critical in accurately evaluating potential information gain compared to node-only evaluations.

In terms of implementation, the authors discuss the adaptation of Dubins paths for state connectivity and the use of informed sampling within grid cell spaces. To optimize the likelihood of reducing entropy in high-reward areas, TIGRIS applies a reward function that accounts for both true and false positive rates derived from sensor models, crucial when dealing with perception systems such as cameras.

The research implications are significant as TIGRIS presents a practical and generalized framework that can be applied in diverse, large-scale environments. Its informed sampling mechanism and edge reward consideration offer a promising direction for developing IPP strategies in robotics, particularly as autonomous systems become more prevalent in complex data-gathering missions. Future research areas suggested by the authors include real-time re-planning capabilities and optimizing path locality, which could further bolster TIGRIS's applicability in dynamically changing or uncertain environments.

The contribution of TIGRIS is a step toward bridging the gap between theoretical development and real-world applicability in robotic information gathering, providing a robust method for improving efficiency and utility in autonomous path planning tasks across various domains. The release of an open-source version of TIGRIS helps ensure that further advancements can be built upon this foundation, promoting further exploration in the development of sophisticated IPP systems.