As retail and e-commerce demands continue to accelerate, more supply chains are investigating autonomous vehicles. Since its founding in 2017, Gatik’s mission has been to deliver goods safely and efficiently using autonomous trucks. The company focuses on business-to-business, short-haul logistics for the retail industry.
In this RoboBusiness Direct session, Apeksha Kumavat, co-founder and chief engineer at Gatik, will discuss how constraining the operational design domain (ODD) can bring middle-mile autonomous delivery to market quickly and effectively.
Gatik’s fleet of Class 1 to 6 autonomous vehicles moves goods from micro-fulfillment centers and “dark” stores to pick-up points – retail stores, distribution centers and offices – known as the middle mile. The company said they can help customers meet consumer demand for real-time goods and meet an unprecedented expectation for contactless delivery.
With an emphasis on structured autonomy, Gatik operates its vehicles up to seven days a week, 12 hours a day, on known routes with fixed pick-up and drop-off locations. This can expedite the time required to go to market and maximize efficiency and safety.
Kumavat will explain how this approach can reduce the biggest threat to the autonomous revolution — edge cases.
About RoboBusiness Direct
RoboBusiness Direct is an ongoing series of digital events delivered by brightest minds from the leading robotics and automation organizations around the world. The series complements continuing coverage and analysis in Robotics Business Review, a sibling site to The Robot Report.
RoboBusiness Direct is designed to impart to business and engineering professionals the information they need to identify market opportunities, successfully develop and deploy the next generation of commercial robotics systems, and accelerate their businesses.
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About Gatik’s Apeksha Kumavat
Apeksha Kumavat is the co-founder and chief engineer at Gatik, a Palo Alto, Calif.-based based start-up developing autonomous vehicles. Prior to founding Gatik, Kumavat was the perception lead at Ford Motor Co.’s self-driving car unit, where she designed feature-detection methods to assist with online localization and developed perception algorithms for autonomous vehicles.
Kumavat has also held the position of lead machine-learning engineer at Otsaw Digital, where she designed and developed state-of-the-art deep learning networks for autonomous navigation of the O-R3 patrolling robot in non-static outdoor environments.
Kumavat earned her M.S. with a focus on machine/deep learning from Purdue University. She has contributed towards research in areas including machine learning, cognitive science, and robotics.