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New RRT* framework plans robotic missions with budget constraints

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Reward-Guided RRT*, a novel framework for robotic missions that jointly selects rewarding locations and plans feasible motions within budget constraints. This method incrementally builds a mission through task-selection-and-motion-planning stages, directly in the continuous state space, without needing a precomputed task-transition graph. Experiments demonstrate that Reward-Guided RRT* achieves comparable solutions to VNS-PRM* with fewer sampled states and can incorporate vehicle kinematic constraints, as shown in simulations with a car-like robotic platform. AI

IMPACT This research could lead to more efficient and budget-conscious autonomous robotic missions.

RANK_REASON This is a research paper detailing a new algorithm for robotic planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RRT* framework plans robotic missions with budget constraints

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This is a research paper detailing a new algorithm for robotic planning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vin\'icius Ara\'ujo de Oliveira, Gabriel Ferreira Mendes Louren\c{c}o, Paulo L. J. Drews-Jr, Armando Alves Neto, Douglas G. Macharet ·

    Reward-Guided RRT* for Budgeted Task and Motion Planning

    arXiv:2610.35289v1 Announce Type: cross Abstract: Robotic missions involving spatially distributed tasks often require jointly selecting rewarding locations and planning feasible motions under limited travel budgets. This paper proposes Reward-Guided RRT*, a new sampling-based fr…

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