An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments (1909.09548v2)

Abstract: The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.

• The paper proposes a sampling-based method that continuously refines a living trajectory tree to optimize both local and global path planning.
• It employs a single objective function to eliminate two-stage mode switching, thereby enhancing efficiency over traditional approaches.
• Demonstrated in indoor exploration and 3D reconstruction, the method achieves superior coverage and accuracy in dynamic, unknown settings.

Overview of a Sampling-based Method for Online Informative Path Planning

The paper "An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments," addresses the challenges of path planning in dynamic and previously unexplored environments by introducing a novel RRT*-inspired algorithm. In autonomous robotics, particularly for mobile robots like Micro Aerial Vehicles (MAVs), the capacity to chart informative paths on-the-go is crucial for effectively navigating and mapping unknown territories.

Summary of the Proposed Method

The authors advance a sampling-based methodology that stems from the RRT* algorithm, which is popular in motion planning for ensuring optimal and asymptotically efficient trajectory finding. Typically, sampling-based informative path planning methods can become ensnared in local minima, and regularly fail to account for the broader global context. This paper resolves these issues by keeping the entire trajectory tree alive, continuously expanding and refining it as new information becomes available. This ensures an ongoing adaptation to environmental changes, securing both local refinement and global coverage.

Key to this capability is the rewiring of nodes within the trajectory tree to better reflect utility as perceived from the robot's current position. The adoption of a single objective function to dictate both local and global maneuvering precludes the need for two-stage processes often employed by current state-of-the-art methods, which are prone to inefficiencies due to frequent mode switching.

Application and Implementation

The efficacy of this approach is demonstrated through two applications: autonomous indoor exploration, and 3D reconstruction utilizing a Truncated Signed Distance Field (TSDF) framework. For these tasks, the authors propose a tailored gain and cost-utility formulation, attuned to efficient path planning in environments with uncertain sensory data and state estimation inaccuracies.

The paper detailed in the paper portrays comparative analyses with existing methods in simulated environments. These comparisons underscore the proposed algorithm's superiority in ensuring comprehensive coverage and achieving high accuracy in 3D reconstructions, enabled by its holistic handling of utility maximization and computation resource optimization.

Implications and Future Directions

The implications of this research extend toward expanding the horizon for autonomous operations in various fields, including search and rescue missions, inspection tasks, and mapping for infrastructure development. The open-source availability of the algorithm is expected to proliferate its application and foster developments within the robotics research community.

Looking ahead, the flexibility afforded by maintaining a living trajectory tree could be leveraged into more complex, multi-agent scenarios where cooperative task completion is necessary. Furthermore, with advances in computational hardware and algorithmic efficiencies, the approach could be extended to unlock real-time and large-scale applications. This would facilitate greater autonomy in robotics, potentially utilizing further integrated environmental feedback loops to dynamically adjust objectives during operation.

The proposed TSDF-based gain and the efficiency-inspired global normalization value equation represent notable advancements with potential cross-functional applications in precision-heavy autonomous tasks. This foundational work opens pathways for researchers to explore additional cost formulations and utility metrics aimed at optimizing various aspects of robotic path planning under uncertainty.