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1. DEC-LOS-RRT: Decentralized Path Planning for Multi-robot Systems with Line-of-sight Constrained Communication

   https://doi.org/10.1109/CCTA48906.2021.9659247  

   Tuck, Victoria; Pant, Yash Vardhan; Seshia, Sanjit A.; Sastry, S. Shankar (August 2021, 2021 IEEE Conference on Control Technology and Applications (CCTA))

   Decentralized planning for multi-agent systems, such as fleets of robots in a search-and-rescue operation, is often constrained by limitations on how agents can communicate with each other. One such limitation is the case when agents can communicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods that guarantee safety is difficult in this case, as agents that are occluded from each other might not be able to communicate until it’s too late to avoid a safety violation. In this paper, we develop a decentralized planning method that explicitly avoids situations where lack of visibility of other agents would lead to an unsafe situation. Building on top of an existing Rapidly exploring Random Tree (RRT)-based approach, our method guarantees safety at each iteration. Simulation studies show the effectiveness of our method and compare the degradation in performance with respect to a clairvoyant decentralized planning algorithm where agents can communicate despite not being in LOS of each other. 

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2. Consider the Human Work Experience When Integrating Robotics in the Workplace

   https://doi.org/10.1109/HRI.2019.8673139  

   Welfare, Katherine S.; Hallowell, Matthew R.; Shah, Julie A.; Riek, Laurel D. (January 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI))

   Worldwide, manufacturers are reimagining the future of their workforce and its connection to technology. Rather than replacing humans, Industry 5.0 explores how humans and robots can best complement one another's unique strengths. However, realizing this vision requires an in-depth understanding of how workers view the positive and negative attributes of their jobs, and the place of robots within it. In this paper, we explore the relationship between work attributes and automation goals by engaging in field research at a manufacturing plant. We conducted 50 face-to-face interviews with assembly-line workers (n=50), which we analyzed using discourse analysis and social constructivist methods. We found that the work attributes deemed most positive by participants include social interaction, movement and exercise, (human) autonomy, problem solving, task variety, and building with their hands. The main negative work attributes included health and safety issues, feeling rushed, and repetitive work. We identified several ways robots could help reduce negative work attributes and enhance positive ones, such as reducing work interruptions and cultivating physical and psychological well-being. Based on our findings, we created a set of integration considerations for organizations planning to deploy robotics technology, and discuss how the manufacturing and HRI communities can explore these ideas in the future. 

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3. Multi-Agent Path Planning Under Observation Schedule Constraints

   https://doi.org/10.1109/IROS45743.2020.9340747  

   Yang, Ziqi; Tron, Roberto (October 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i.e., detect the malicious actions of compromised agents using other members of the group. In particular, the proposed solution finds multi-agent paths on discrete spaces combined with a set of mutual observations at specific locations to detect robots with significant deviations from the preordained routes. In this paper, we develop a planner that works on continuous configuration spaces while also taking into account similar spatio-temporal constraints. In addition, the planner allows for more general tasks that can be formulated as arbitrary smooth cost functions to be specified. The combination of constraints and objectives considered in this paper are not easily handled by popular path planning algorithms (e.g., sampling-based methods), thus we propose a method based on the Alternating Direction Method of Multipliers (ADMM). ADMM is capable of finding locally optimal solutions to problems involving different kinds of objectives and non-convex temporal and spatial constraints, and allows for infeasible initialization. We benchmark our proposed method on multi-agent map exploration with minimum-uncertainty cost function, obstacles, and observation schedule constraints. 

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4. 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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5. An obstacle-interaction planning method for navigation of actuated vine robots

   https://doi.org/10.1109/ICRA40945.2020.9196587  

   Selvaggio, M.; Ramirez, L. A.; Naclerio, N. D.; Siciliano, B.; Hawkes, E. W. (May 2020, IEEE International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   The field of soft robotics is grounded on the idea that, due to their inherent compliance, soft robots can safely interact with the environment. Thus, the development of effective planning and control pipelines for soft robots should incorporate reliable robot-environment interaction models. This strategy enables soft robots to effectively exploit contacts to autonomously navigate and accomplish tasks in the environment. However, for a class of soft robots, namely vine-inspired, tip-extending or "vine" robots, such interaction models and the resulting planning and control strategies do not exist. In this paper, we analyze the behavior of vine robots interacting with their environment and propose an obstacle-interaction model that characterizes the bending and wrinkling deformation induced by the environment. Starting from this, we devise a novel obstacle-interaction planning method for these robots. We show how obstacle interactions can be effectively leveraged to enlarge the set of reachable workspace for the robot tip, and verify our findings with both simulated and real experiments. Our work improves the capabilities of this new class of soft robot, helping to advance the field of soft robotics. 

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