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Meeko Oishi

2 accepted papers

2017

Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

ICRA 2017poster

Identifying collision-free paths over long time windows in environments with stochastically moving obstacles is difficult, in part because long-term predictions of obstacle positions typically have low fidelity, and the region of possible obstacle occupancy is typically large. As a result, planning…

Cited by 25SourceScholar
2015

Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments

ICRA 2015poster

Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel t…

Cited by 170SourceScholar