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1. Planning for Robotic Dry Stacking with Irregular Stones

   https://doi.org/10.1007/978-981-15-9460-1_23  

   Liu, Y; Choi, J; Napp, N (January 2021, Field and Service Robotics)

   Ishigami, G; Yoshida, K (Ed.)

   The ability to build structures with autonomous robots using only found, minimally processed stones would be immensely useful, especially in remote areas. Assembly planning for dry-stacked structures, however, is difficult since both the state and action spaces are continuous, and stability is strongly affected by complex friction and contact constraints. We propose a planning algorithm for such assemblies that uses a physics simulator to find a small set of feasible poses and then evaluates them using a hierarchical filter. We carefully designed the heuristics for the filters to match our goal of building stable, free-standing walls. These plans are then executed open-loop with a robotic arm equipped with a wrist RGB-D camera. Experimental results show that the proposed planning algorithm can significantly improve the state of the art in robotic dry stacking. 

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2. Approximate Stability Analysis for Drystacked Structures

   Liu, Yifang; Saboia, Maira; Thangavelu, Vivek; Napp, Nils (May 2019, International Conference on Robotics and Automation (ICRA2019))

   We introduce a fast approximate stability analysis into an automated dry stacking procedure. Evaluating structural stability is essential for any type of construction, but especially challenging in techniques where building elements remain distinct and do not use fasteners or adhesives. Due to the irregular shape of construction materials, autonomous agents have restricted knowledge of contact geometry, which makes existing analysis tools difficult to deploy. In this paper, a geometric safety factor called kern is used to estimate how much the contact interface can shrink and the structure still be feasible, where feasibility can be checked efficiently using linear programming. We validate the stability measure by comparing the proposed methods with a fully simulated shaking test in 2D. We also improve existing heuristics-based planning by adding the proposed measure into the assembly process. 

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3. Learning Models for Predictive Adaptation in State Lattices

   Napoli, Michael E.; Biggie, Harel; Howard, Thomas M. (September 2018, 11th International Conference on Field and Service Robotics)

   Approaches to autonomous navigation for unmanned ground vehicles rely on motion planning algorithms that optimize maneuvers under kinematic and environmental constraints. Algorithms that combine heuristic search with local optimization are well suited to domains where solution optimality is favored over speed and memory resources are limited as they often improve the optimality of solutions without increasing the sampling density. To address the runtime performance limitations of such algorithms, this paper introduces Predictively Adapted State Lattices, an extension of recombinant motion planning search space construction that adapts the representation by selecting regions to optimize using a learned model trained to predict the expected improvement. The model aids in prioritizing computations that optimize regions where significant improvement is anticipated. We evaluate the performance of the proposed method through statistical and qualitative comparisons to alternative State Lattice approaches for a simulated mobile robot with nonholonomic constraints. Results demonstrate an advance in the ability of recombinant motion planning search spaces to improve relative optimality at reduced runtime in varyingly complex environments. 

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4. Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance

   Guthrie, James; Kobilarov, Marin; Mallada, Enrique (January 2022, Proceedings of the American Control Conference)

   Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around M convex polytopes over N time steps. However, it requires introducing (2+L)MN additional constraints and LMN additional variables, with L being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outerapproximating the Minkowski sum conditions for collision. Our solution requires only MN constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations. 

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5. Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance

   https://doi.org/10.23919/ACC53348.2022.9867524  

   Guthrie, James; Kobilarov, Marin; Mallada, Enrique (January 2022, Proceedings of the American Control Conference)

   Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around $$M$$ convex polytopes over $$N$$ time steps. However, it requires introducing $(2+L)MN$ additional constraints and $LMN$ additional variables, with $$L$$ being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only $MN$ constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations. 

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