The Texas-based, self-driving forklift startup Fox Robotics has raised $9 million in venture capital funding for its vehicles that automatically unload trailers, and will use the backing to ramp up production and meet growing demand, the firm said.
The “series A” funding round was led by Menlo Ventures, with additional participation from Eniac Ventures, La Famiglia, SignalFire, Congruent Ventures, and AME Cloud Ventures. It brings Fox's total funding to over $13 million.
"We are proud to back the team at Fox Robotics," said Mark Siegel, a partner at Menlo Ventures, who will now join Fox’s board of directors. "The company's value proposition is clear: Their full-stack solution for self-driving forklifts that can increase workplace productivity 200 to 300%. They bring huge efficiency to the supply chain."
Fox enters a field with several established vendors, such as Vecna Robotics, AutoGuide Mobile Robots, and Seegrid, as well as many traditional lift truck vendors. In addition, producers of automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) have made great strides in recent years at building intelligent, self-driving vehicles for warehouse applications.
But according to Fox, its forklifts are more flexible, more capable, and safer than current AGVs. The company says its vehicles can be installed and start running in a new warehouse in less than a day, compared to the weeks or months required for other warehouse automation solutions. The firm’s vehicles use artificial intelligence (AI) for real-time detection of pallets, trailers, and obstacles, instead of relying on pre-programmed routes or fixed locations.
That combination of technologies can cut forklift operating costs in half, says Fox, which aspires to automate 20% of the 1.5 million forklifts sold annually. "The market for warehouse automation is huge and growing,” Fox Robotics CEO Charles DuHadway said in a release. “The future of warehouse automation isn't fixed automation systems that cost several hundred million dollars. It's mobile robots that are low-cost, flexible, and can be deployed incrementally and quickly.”
