ViART: Vision-Based Soft Tactile Sensing for Autonomous Robotic Vehicles

Navigating and interacting in a narrow space, especially where visual and range sensors do not work, is challenging for autonomous robotic vehicles. In such a scenario, collisions with obstacles and other vehicles are inevitable; thus, effective proximity detection systems are required for safe robot navigation and interactions. This article proposes an innovative design of vision-based soft tactile sensing system for autonomous robotic vehicles (ViART). ViART has a silicone rubber barrel-shaped skin. A set of markers interspaced around the inner surface equator of the skin is monitored by a fish-eye camera installed at one end of the barrel. Displacements of these markers are measured to perceive the physical interaction of tactile sensor and surrounding objects. Contact angle and force of the tactile sensor were estimated and evaluated through a series of experiments, achieving a mean absolute error of 1.12$^{\circ }$ and 0.12 N, respectively. We demonstrated the capability of a developed sensing system with experiments of multiple autonomous mobile robots without visual feedback, showing its capability to navigate in challenging environments, such as narrow spaces, clustered obstacles, and a mix of static and dynamic obstacles. Moreover, with these experiments, we reveal how unique characteristics of the skin including 360$^{\circ }$ multicontact detection and force measurement help leveraging the capacities of obstacle avoidance and navigation of multiagent robotic systems. Tactile-based multicontact sensing capacity provides information on the relative velocity for the robot to avoid obstacles efficiently, whereas tactile-based force estimation enables the robot to avoid obstacles by pushing obstacles away. We expect this contribution may pave a way to implement soft tactile sensing for robust, efficient, safe interaction, and navigation of autonomous vehicles in restricted environments.