用微型激光测距传感器实现软体机器人自定位,精度接近传感器自身误差。
Soft Robot Localization Using Distributed Miniaturized Time-of-Flight Sensors
- 在软体机器人末端部署分布式VL53L5CX激光测距传感器,获取多点环境深度数据。
- 实验表明定位误差与传感器测量不确定性相当,达到厘米级精度。
- 适合小型软体机器人在复杂环境中的实时自定位,尤其适用于空间受限场景。
由于柔韧性和适应性,软体机器人可部署于狭窄或复杂的环境中执行任务。在这些场景中,对周围环境的空间感知以及机器人自身的定位能力至关重要。尽管当前主流的定位技术在自动驾驶车辆和行走机器人中已得到充分研究,但其依赖于体积庞大、难以集成到小型软体机器人中的激光雷达或深度传感器。近年来,微型时间飞行(ToF)传感器的发展为小型化、轻量化传感提供了新可能。这类传感器可分布安装在软体机器人本体上,提供多点环境深度数据。然而,其较低的空间分辨率和噪声较大的测量值,给现有高精度定位算法带来了挑战,因这些算法通常基于更密集且更可靠的测量数据。本文采用分布式VL53L5CX ToF传感器,并将其安装在软体机器人末端,探索其在自定位任务中的应用。实验结果表明,软体机器人可有效相对于已知地图实现定位,其定位误差与微型ToF传感器提供的测量不确定性相当。
原文摘要 · Abstract (English)
Thanks to their compliance and adaptability, soft robots can be deployed to perform tasks in constrained or complex environments. In these scenarios, spatial awareness of the surroundings and the ability to localize the robot within the environment represent key aspects. While state-of-the-art localization techniques are well-explored in autonomous vehicles and walking robots, they rely on data retrieved with lidar or depth sensors which are bulky and thus difficult to integrate into small soft robots. Recent developments in miniaturized Time of Flight (ToF) sensors show promise as a small and lightweight alternative to bulky sensors. These sensors can be potentially distributed on the soft robot body, providing multi-point depth data of the surroundings. However, the small spatial resolution and the noisy measurements pose a challenge to the success of state-of-the-art localization algorithms, which are generally applied to much denser and more reliable measurements. In this paper, we enforce distributed VL53L5CX ToF sensors, mount them on the tip of a soft robot, and investigate their usage for self-localization tasks. Experimental results show that the soft robot can effectively be localized with respect to a known map, with an error comparable to the uncertainty on the measures provided by the miniaturized ToF sensors.
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