This paper presents a novel control algorithm for robotic manipulators in unstructured environments using proximity sensors partially distributed on the platform. The proposed approach exploits arrays of multi zone Time-of-Flight (ToF) sensors to generate a sparse point cloud representation of the robot surroundings. By employing computational geometry techniques, we fuse the knowledge of robot geometric model with ToFs sensory feedback to generate whole-body motion tasks, allowing to move both sensorized and non-sensorized links in response to unpredictable events such as human motion. In particular, the proposed algorithm computes the pair of closest points between the environment cloud and the robot links, generating a dynamic avoidance motion that is implemented as the highest priority task in a two-level hierarchical architecture. Such a design choice allows the robot to work safely alongside humans even without a complete sensorization over the whole surface. Experimental validation demonstrates the algorithm effectiveness both in static and dynamic scenarios, achieving comparable performances with respect to well established control techniques that aim to move the sensors mounting positions on the robot body. The presented algorithm exploits any arbitrary point on the robot surface to perform avoidance motion, showing improvements in the distance margin up to 100 mm, due to the rendering of virtual avoidance tasks on non-sensorized links.

翻译：本文提出了一种新颖的控制算法，用于非结构化环境中机器人机械臂的控制，该算法利用部分分布于平台上的近场传感器。所提出的方法采用多区域飞行时间传感器阵列，生成机器人周围环境的稀疏点云表示。通过运用计算几何技术，我们将机器人几何模型知识与飞行时间传感器的反馈信息相融合，以生成全机身运动任务，使得传感器覆盖及未覆盖的连杆均能响应不可预测事件（如人体运动）而产生运动。具体而言，所提算法计算环境点云与机器人连杆之间的最近点对，生成动态避障运动，并将其作为两级分层架构中的最高优先级任务予以执行。这种设计选择使得机器人即使未实现整个表面的完全传感器覆盖，也能安全地与人类协同工作。实验验证表明，该算法在静态和动态场景下均有效，其性能与那些旨在移动机器人本体上传感器安装位置的成熟控制技术相当。所提出的算法利用机器人表面上的任意点来执行避障运动，由于在未覆盖传感器的连杆上渲染了虚拟避障任务，其距离裕度最高可提升100毫米。