Multirepresentation, Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment

Abstract

A key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I‐AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I‐AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real‐time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR‐MHA*) search‐based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I‐AUV performing a Valve‐Turning intervention in a water tank. To this aim, the AUV was upgraded with an in‐house‐developed laser scanner to gather three‐dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR‐MHA* motion planner used this octomap to plan, in real time, collision‐free trajectories. To achieve the accuracy required to complete the task, a vision‐based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual‐servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose‐coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.

Sensor‐driven autonomous underwater inspections: A receding‐horizon RRT‐based view planning solution for AUVs

“…The employed mapping strategy resorts to the occupancy grid mapping paradigm (Burgard et al, 2005). Born as a robust representation of the surrounding environment, occupancy grid mapping (Moravec & Elfes, 1985) has encountered several marine robotics applications in the context of collision checking and obstacle/collision avoidance (Youakim et al, 2020), mapping (Franchi, Bucci, et al, 2020; Teixeira et al, 2016), planning (J. D. Hernández et al, 2019; Vidal et al, 2020), and navigation with planning (Ho et al, 2018; Pairet et al, 2020; Sodhi et al, 2019).…”

Online Mapping and Motion Planning Under Uncertainty for Safe Navigation in Unknown Environments

“…Nonetheless, even for scenarios rich in features, there are always some residual uncertainties. More recently, online motion planning frameworks have been developed to empower a mobile robot to compute navigation actions in unexplored environments while accounting for the system's motion capabilities, e.g., [11], [21], [27]- [29], [68], [76], [80]. These approaches, however, do not cope with any source of uncertainty and employ ad hoc heuristics that lack quantified safety guarantees.…”

Evaluation of excavation motion sequence for hydraulic excavators based on extraction of excavation style and phase

“…However, the following problems restrain the achievement of fully autonomous hydraulic excavation: Problem (A) The tasks that hydraulic excavators are required to perform are quite diverse, but most studies focus on simple digging tasks. Problem (B) Because the reaction of the excavation targets (soil, sand, and rock) shows nondeterministic behavior, the quantitative measurement of their state is difficult (Fukui et al, 2017); therefore, popular robot motion planning techniques (Elbanhawi & Simic, 2014; Reid et al, 2020; Wan et al, 2021; Youakim et al, 2020) cannot be applied. Problem (C) The excavation procedures can change depending on the experience and preference of skilled operators.…”