504 research outputs found
Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection
In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments
In this work we focus on improving the efficiency and generalisation of
learned navigation strategies when transferred from its training environment to
previously unseen ones. We present an extension of the residual reinforcement
learning framework from the robotic manipulation literature and adapt it to the
vast and unstructured environments that mobile robots can operate in. The
concept is based on learning a residual control effect to add to a typical
sub-optimal classical controller in order to close the performance gap, whilst
guiding the exploration process during training for improved data efficiency.
We exploit this tight coupling and propose a novel deployment strategy,
switching Residual Reactive Navigation (sRRN), which yields efficient
trajectories whilst probabilistically switching to a classical controller in
cases of high policy uncertainty. Our approach achieves improved performance
over end-to-end alternatives and can be incorporated as part of a complete
navigation stack for cluttered indoor navigation tasks in the real world. The
code and training environment for this project is made publicly available at
https://sites.google.com/view/srrn/home.Comment: Accepted as a conference paper at ICRA2020. Project site available at
https://sites.google.com/view/srrn/hom
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