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  1. Free, publicly-accessible full text available May 29, 2024
  2. In this work, we investigate the problem of incrementally solving constrained non-linear optimization problems formulated as factor graphs. Prior incremental solvers were either restricted to the unconstrained case or required periodic batch relinearizations of the objective and constraints which are expensive and detract from the online nature of the algorithm. We present InCOpt, an Augmented Lagrangian-based incremental constrained optimizer that views matrix operations as message passing over the Bayes tree. We first show how the linear system, resulting from linearizing the constrained objective, can be represented as a Bayes tree. We then propose an algorithm that views forward and back substitutions, which naturally arise from solving the Lagrangian, as upward and downward passes on the tree. Using this formulation, In-COpt can exploit properties such as fluid/online relinearization leading to increased accuracy without a sacrifice in runtime. We evaluate our solver on different applications (navigation and manipulation) and provide an extensive evaluation against existing constrained and unconstrained solvers. 
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  3. RACOD is an algorithm/hardware co-design for mobile robot path planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm extension for runahead path exploration. CODAcc uses a novel MapReduce-style hardware computational model and massively parallelizes individual collision checks. RASExp predicts future path explorations and proactively computes its collision status ahead of time, thereby overlapping multiple collision detections. By affording multiple cheap CODAcc accelerators and overlapping collision detections using RASExp, RACOD significantly accelerates planning for mobile robots operating in arbitrary environments. Evaluations of popular benchmarks show up to 41.4× (self-driving cars) and 34.3× (pilotless drones) speedup with less than 0.3% area overhead. While the performance is maximized when CODAcc and RASExp are used together, they can also be used individually. To illustrate, we evaluate CODAcc alone in the context of a stationary robotic arm and show that it improves performance by 3.4×–3.8×. Also, we evaluate RASExp alone on commodity many-core CPU and GPU platforms by implementing it purely in software and show that with 32/128 CPU/GPU threads, it accelerates the end-to-end planning time by 8.6×/2.9×. 
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