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A Hybrid Active Global Localisation Algorithm for Mobile Robots

机译:移动机器人的混合主动全局定位算法

摘要

Localisation is one of the most important tasks to be accomplished in order to realize the complete autonomy of a mobile robot. In this paper, a new strategy for global localisation is proposed. Applying this method a robot is able to safely initialise its position or relocalise itself in case of recovery of pose tracking failure. The algorithm presented adopts a hybrid approach. First a particle filter is used to generate hypotheses on the possible pose supposing that no movements are allowed to avoid collisions. Thereafter safe trajectories are planned and executed to reduce the remaining ambiguities while the hypotheses are monitored and validated by a set of parallel extended Kalman filters. The novelty of this approach stands on the ability to generate the pose hypotheses without any feature-based knowledge. As a consequence, a landmark-based description of the environment is no longer required for the algorithm execution.
机译:为了实现移动机器人的完全自治,本地化是要完成的最重要的任务之一。本文提出了一种新的全球本地化策略。应用此方法,机器人可以安全地初始化其位置,或者在恢复姿势跟踪失败的情况下自行重新定位。提出的算法采用混合方法。首先,使用粒子过滤器在可能的姿势上生成假设,假设不允许运动以避免碰撞。此后,计划并执行安全轨迹以减少剩余的歧义,同时通过一组并行扩展卡尔曼滤波器对假设进行监视和验证。这种方法的新颖之处在于无需任何基于特征的知识即可生成姿势假设的能力。结果,算法执行不再需要基于地标的环境描述。

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