Multirepresentation, Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment. Issue 6 (9th February 2020)

• Record Type: Journal Article
• Authors: Youakim, Dina; Cieslak, Patryk; Dornbush, Andrew; Palomer, Albert; Ridao, Pere; Likhachev, Maxim
• Is Part Of: Journal of field robotics. Volume 37:Issue 6(2020)
• Page Start: 925
• Page End: 950
• Publication Date: 2020-02-09
• Subjects: mobile manipulation -- motion planning -- search‐based planning -- underwater robotics -- unknown environment; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21923
• Languages: English
• ISSNs: 1556-4959

Abstract

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.