3D Reactive Control and Frontier-Based Exploration for Unstructured Environments
This addresses the problem of reliable autonomous navigation for robots in unstructured environments, representing an incremental improvement in planning methods.
The paper tackles long-range robotic navigation in cluttered environments by proposing a two-layer planning architecture that combines map-based frontier exploration with reactive obstacle avoidance, demonstrating feasibility and robustness in field deployments with a micro-aerial vehicle in a warehouse.
The paper proposes a reliable and robust planning solution to the long range robotic navigation problem in extremely cluttered environments. A two-layer planning architecture is proposed that leverages both the environment map and the direct depth sensor information to ensure maximal information gain out of the onboard sensors. A frontier-based pose sampling technique is used with a fast marching cost-to-go calculation to select a goal pose and plan a path to maximize robot exploration rate. An artificial potential function approach, relying on direct depth measurements, enables the robot to follow the path while simultaneously avoiding small scene obstacles that are not captured in the map due to mapping and localization uncertainties. We demonstrate the feasibility and robustness of the proposed approach through field deployments in a structurally complex warehouse using a micro-aerial vehicle (MAV) with all the sensing and computations performed onboard.