Seegrid Corporation, a leader in self-driving industrial vehicles for material handling, today announced that it closed a $52 million growth equity financing round, bringing the company’s total funding to date to more than $150 million. The investment round previously was announced when lead investor G2VP funded the first $25 million of the round. The company had the right to top off the round, which was oversubscribed, resulting in a total round of $52 million with additional funding from leading technology and robotics investors.

Seegrid intends to use the additional capital to increase the size of its workforce to deliver best-in-class automation solutions for its customers, who already have more than 3 million autonomous miles of Seegrid vision guided vehicles in production use. The investment also will accelerate new product development and new product introductions. Seegrid also is considering potential strategic acquisitions.

“With this investment, we will continue to increase our market share, solidify our position as market leader and further disrupt the manufacturing, warehousing and logistics industries,” said Rock. “And, most importantly, the continued support from G2VP and our new investors enables us to accelerate new product innovations and address customer needs in this rapidly changing climate.”

“We are very pleased with the level of interest and financing to support Seegrid’s growth,” said Steven F. Kaplan, Chairman of the Board of Seegrid. “The combination of our cash positive and profitable operations and our very strong balance sheet, with well over $50 million of cash and no debt, position Seegrid to continue to lead the autonomous material handling vehicle industry and help companies automate their operations.”

According to Seegrid, it leads the automated guided vehicle (AGV) and autonomous mobile robot (AMR) industry category by pairing robust, flexible, and reliable technology with unmatched service and support. The company’s proprietary navigation technology uses cameras, sophisticated algorithms and machine learning to navigate in dynamic environments.