2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019
DOI: 10.1109/iros40897.2019.8967550
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Abstract: Last-mile delivery systems commonly propose the use of autonomous robotic vehicles to increase scalability and efficiency. The economic inefficiency of collecting accurate prior maps for navigation motivates the use of planning algorithms that operate in unmapped environments. However, these algorithms typically waste time exploring regions that are unlikely to contain the delivery destination. Context is key information about structured environments that could guide exploration toward the unknown goal locatio… Show more

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Cited by 8 publications
(3 citation statements)
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“…While hierarchical planning can be efficient, it is obvious that the quality of the high-level planning problem, or the combination of A, c(a; Γ), and f(a; Γ), dictates the ability of the high-level search process to effectively guide primitive planning. Intuitively, a good choice of representation is one that ensures plan similarity 2 , or that low-cost, feasible plans in P have low-cost, feasible primitive refinements in P. Unfortunately, generating planning representations which exhibit plan similarity can be challenging, and in practice such representations are often hand-designed for specific problems [24], or require significant prior domain knowledge to be generated [12], [15], [16]. Instead, we define a simple A and use online planning results to infer estimates of c(a; Γ) and f(a; Γ) over the course of a multi-query planning trial.…”
Section: A Problem Formulation: Hierarchical Planningmentioning
confidence: 99%
See 1 more Smart Citation
“…While hierarchical planning can be efficient, it is obvious that the quality of the high-level planning problem, or the combination of A, c(a; Γ), and f(a; Γ), dictates the ability of the high-level search process to effectively guide primitive planning. Intuitively, a good choice of representation is one that ensures plan similarity 2 , or that low-cost, feasible plans in P have low-cost, feasible primitive refinements in P. Unfortunately, generating planning representations which exhibit plan similarity can be challenging, and in practice such representations are often hand-designed for specific problems [24], or require significant prior domain knowledge to be generated [12], [15], [16]. Instead, we define a simple A and use online planning results to infer estimates of c(a; Γ) and f(a; Γ) over the course of a multi-query planning trial.…”
Section: A Problem Formulation: Hierarchical Planningmentioning
confidence: 99%
“…Other approaches use techniques such as wavelet decompositions [13] or the information bottleneck [14] to compress known cost maps for multiresolution planning, but do not explicitly capture the structure of the paths agents use when traversing in the environment. Recently, some approaches have turned to learning to generate coarse representations of planning problems based on offline datasets of primitive plans and additional environmental cues, such as semantically segmented overhead images [15] or height maps [16]; however, such additional environmental cues can be challenging to acquire in real-world environments.…”
Section: Introductionmentioning
confidence: 99%
“…The map module has both Convolutional Auto-encoder (CAE) [25], similar to [45], and LSTM components and attempts to fix some issues, e.g. greediness, that are present in the view module (i.e.…”
Section: B Map Modulementioning
confidence: 99%