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The 2021 Champlain Towers South Condominiums collapse in Surfside, Florida, resulted 98 deaths. Nine people are thought to have survived the initial collapse, and might have been rescued if rescue workers could have located them. Perhaps, if rescue workers had been able to use robots to search the interior of the rubble pile, outcomes might have been better. An improved understanding of the environment in which a robot would have to operate to be able to search the interior of a rubble pile would help roboticists develop better suited robotic platforms and control strategies. To this end, this work offers an approach to characterize and visualize the interior of a rubble pile and conduct a preliminary analysis of the occurrence of voids. Specifically, the analysis makes opportunistic use of four days of aerial imagery gathered from responders at Surfside to create a 3D volumetric aggregated model of the collapse in order to identify and characterize void spaces in the interior of the rubble. The preliminary results confirm expectations of small number and scale of these interior voids. The results can inform better selection and control of existing robots for disaster response, aid in determining the design specifications (specifically scale and form factor), and improve control of future robotic platforms developed for search operations in rubble. 

3D representations of geographical surfaces in the form of dense point clouds can be a valuable tool for documenting and reconstructing a structural collapse, such as the 2021 Champlain Towers Condominium collapse in Surfside, Florida. Point cloud data reconstructed from aerial footage taken by uncrewed aerial systems at frequent intervals from a dynamic search and rescue scene poses significant challenges. Properly aligning large point clouds in this context, or registering them, poses noteworthy issues as they capture multiple regions whose geometries change over time. These regions denote dynamic features such as excavation machinery, cones marking boundaries and the structural collapse rubble itself. In this paper, the performances of commonly used point cloud registration methods for dynamic scenes present in the raw data are studied. The use of Iterative Closest Point (ICP), Rigid - Coherent Point Drift (CPD) and PointNetLK for registering dense point clouds, reconstructed sequentially over a time- frame of five days, is studied and evaluated. All methods are compared by error in performance, execution time, and robustness with a concluding analysis and a judgement of the preeminent method for the specific data at hand is provided. 

Abstract Conventional approaches in prescribing controls for locomoting robots assume control over all input degrees of freedom (DOFs). Many robots, such as those with non-holonomic constraints, may not require or even allow for direct command over all DOFs. In particular, a snake robot with more than three links with non-holonomic constraints cannot achieve arbitrary configurations in all of its joints while simultaneously locomoting. For such a system, we assume partial command over a subset of the joints, and allow the rest to evolve according to kinematic chained and dynamic models. Different combinations of actuated and passive joints, as well as joints with dynamic elements such as torsional springs, can drastically change the coupling interactions and stable oscillations of joints. We use tools from nonlinear analysis to understand emergent oscillation modes of various robot configurations and connect them to overall locomotion using geometric mechanics and feedback control for robots that may not fully utilize all available inputs. We also experimentally verify observations and motion planning results on a physical non-holonomic snake robot 

Robot system models often have difficulty allowing for direct command over all input degrees of freedom if the system has a large number of imposed constraints. A snake robot with more than three links and a nonholonomic wheel on each link cannot achieve arbitrary configurations in all of its joints simultaneously. For such a system, we assume partial command over a subset of the joints, and allow the rest to evolve according to kinematic chained and dynamic models. Different combinations of commanded and passive joints, as well as the presence of dynamic elements such as torsional springs, can drastically change the coupling interactions and stable oscillations of the joints. We use the oscillation modes that emerge to inform feedback controllers that achieve desired overall locomotion of the robot.