Active Classification: Theory and Application to Underwater Inspection

We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods.Comment: 16 page

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

Data acquisition and view planning for 3-D modeling tasks

In this paper we address the joint problems of automated data acquisition and view planning for large-scale indoor and outdoor sites. Our method proceeds in two distinct stages. In the initial stage, the system is given a 2-D map with which it plans a minimal set of sufficient covering views. We then use a 3-D laser scanner to take scans at each of these views. When this planning system is combined with our mobile robot, it automatically computes and executes a tour of these viewing locations and acquires the views with the robot's onboard laser scanner. These initial scans serve as an approximate 3-D model of the site. The planning software then enters a second stage in which it updates this model by using a voxel-based occupancy procedure to plan the next best view. This next best view is acquired, and further next best views are sequentially computed and acquired until a complete 3-D model is obtained. Results are shown for Fort Jay on Governors Island in the City of New York and for the church of Saint Menoux in the Bourbonnais region of France

Asymptotically optimized multi-surface coverage path planning for loco-manipulation in inspection and monitoring

Regular inspection and monitoring of aging assets are crucial to safe operation in industrial facilities, with remote robotic monitoring being a particularly promising approach for asset inspection. However, vessels, pipework, and surfaces to be monitored can follow complex 3D surfaces, and frequently no 3D as-built models exist. In this paper, we present an end-to-end solution that uses an optimization method for coverage path planning of multiple complex surfaces for mobile robot manipulators. The system includes a two-layer hierarchical structure of optimization: mission planning and motion planning. The surface sequence is optimized with a mixed-integer linear programming formulation while motion planning solves a whole-body optimal control problem considering the robot as a floating-base system. The loco-manipulation system automatically plans a full-coverage trajectory over multiple surfaces for contact-based non-destructive monitoring after unrolling the 3D-mesh region-of-interest selected from the user interface and projects it back to the surface. Our pipeline aims at offshore asset inspection and remote monitoring in industrial applications, and is also applicable in manufacturing and maintenance where area coverage is critical. We demonstrate the generality and scalability of our solution in a variety of robotic coverage path planning applications, including for multi-surface asset inspection using a quadrupedal manipulator

View planning for automated site modeling

We present a systematic method for constructing 3-D models of large outdoor sites. The method is designed for a mobile robot platform and incorporates automated acquisition of scanned data as well as automated view planning and model construction. In our modeling process, we first use a preliminary view or set of preplanned views to yield an initial, approximate, 3-D model of the target structure. Then, we update this model by using a voxel-based procedure to plan and acquire the next best view. This updating is repeated sequentially until an accurate and complete 3-D model is finally obtained. The method was successfully tested on a portion of the Columbia University campus

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government