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1. Autonomous Modification of Unstructured Environments with Found Material

   https://doi.org/10.1109/ICRA40945.2020.9197372  

   Thangavelu, Vivek; da Silva, Maira Saboia; Choi, Jiwon; Napp, Nils (May 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments. We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations. 

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2. Robotics, Automation, and Control

   https://doi.org/10.1061/9780784482438.034  

   Kim, Daeho; Goyal, Ankit; Newell, Alejandro; Lee, SangHyun; Deng, Jia; Kamat, Vineet R. (June 2019, Semantic Relation Detection Between Construction Entities to Support Safe Human-Robot Collaboration in Construction)

   Construction robots have drawn increased attention as a potential means of improving construction safety and productivity. However, it is still challenging to ensure safe human-robot collaboration on dynamic and unstructured construction workspaces. On construction sites, multiple entities dynamically collaborate with each other and the situational context between them evolves continually. Construction robots must therefore be equipped to visually understand the scene’s contexts (i.e., semantic relations to surrounding entities), thereby safely collaborating with humans, as a human vision system does. Toward this end, this study builds a unique deep neural network architecture and develops a construction-specialized model by experimenting multiple fine-tuning scenarios. Also, this study evaluates its performance on real construction operations data in order to examine its potential toward real-world applications. The results showed the promising performance of the tuned model: the recall@5 on training and validation dataset reached 92% and 67%, respectively. The proposed method, which supports construction co-robots with the holistic scene understanding, is expected to contribute to promoting safer human-robot collaboration in construction. 

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3. Contact Localization for Transparent Robots using Velocity Constraints

   Wang, Sean J.; Bhatia, Ankit; Mason, Matthew T.; Johnson, Aaron M. (January 2020, Proceedings of Dynamic Walking 2020)

   Robots operating in unstructured environments must localize contact to detect and recover from failure. For example, Fig. 1 shows a Minitaur robot that must localize where it has unexpectedly contacted the stair’s edge so that it can properly step over it. We propose a kinematic method for proprioceptive contact localization using velocity measurements. The method is validated on two planar robots, the quadrupedal Minitaur and the DD Hand gripper, and compared to other state of the art proprioceptive methods. We further show that the method can be extended to spatial robots by fusing the candidate contact points over time with a particle filter. 

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4. A Reconfigurable Hardware Library for Robot Scene Perception

   https://doi.org/10.1145/3508352.3561110  

   Liu, Yanqi; Opipari, Anthony; Jenkins, Odest Chadwicke; Bahar, R. Iris (October 2022, ACM)

   Perceiving the position and orientation of objects (i.e., pose estimation) is a crucial prerequisite for robots acting within their natural environment. We present a hardware acceleration approach to enable real-time and energy efficient articulated pose estimation for robots operating in unstructured environments. Our hardware accelerator implements Nonparametric Belief Propagation (NBP) to infer the belief distribution of articulated object poses. Our approach is on average, 26× more energy efficient than a high-end GPU and 11× faster than an embedded low-power GPU implementation. Moreover, we present a Monte-Carlo Perception Library generated from high-level synthesis to enable reconfigurable hardware designs on FPGA fabrics that are better tuned to user-specified scene, resource, and performance constraints. 

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5. Autonomous Visual Assistance for Robot Operations Using a Tethered UAV

   https://doi.org/10.1007/978-981-15-9460-1_2  

   Xiao, X.; Dufek, J.; Murphy, R.R. (January 2021, Field and Service Robotics. Springer Proceedings in Advanced Robotics)

   Ishigami G., Yoshida K. (Ed.)

   This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments. Robotic tele-operation in remote environments is difficult due to the lack of sufficient situational awareness, mostly caused by stationary and limited field-of-view and lack of depth perception from the robot’s onboard camera. The emerging state of the practice is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the onboard sensors by providing an external viewpoint. However, problems exist when using a tele-operated visual assistant: extra manpower, manually chosen suboptimal viewpoint, and extra teamwork demand between primary and secondary operators. In this work, we use an autonomous tethered aerial visual assistant to replace the secondary robot and operator, reducing the human-robot ratio from 2:2 to 1:2. This visual assistant is able to autonomously navigate through unstructured or confined spaces in a risk-aware manner, while continuously maintaining good viewpoint quality to increase the primary operator’s situational awareness. With the proposed co-robots team, tele-operation missions in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments could benefit from reduced manpower and teamwork demand, along with improved visual assistance quality based on trustworthy risk-aware motion in cluttered environments. 

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