Adaptive EV Charging Connector Insertion
Shows a Flexiv robot positioning an EV charging plug at a vehicle charge port, using camera-guided alignment and compliant insertion to handle the connector geometry.
The sequence highlights mobile charging automation where repeatable approach, force control, and safe contact with the vehicle are critical.
Flexiv
Adaptive Robotics
Use case
ev charging
Category
Automotive And Mobility
Key capability
force control, vision guidance, mobile manipulation
Storyboard
What the video shows
The storyboard shows a Flexiv robot positioning an EV charging plug at a vehicle charge port, using camera-guided alignment and compliant insertion to handle the connector geometry.
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Step 1
Prepare the workcell, fixture, part, or target surface shown in the storyboard frames.
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Step 2
Locate and align the robot or tool for ev charging.
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Step 3
Execute the task with force control and monitored robot motion.
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Step 4
Confirm the placement, contact path, inspection result, or finished surface before repeating the cycle.
Challenge
Why this task is difficult
Adaptive EV Charging Connector Insertion requires repeatable execution in automotive and mobility, where alignment, controlled contact, and process consistency can be difficult to maintain manually.
Value
Operational value
The sequence highlights mobile charging automation where repeatable approach, force control, and safe contact with the vehicle are critical.
Deployment layer
How Robita AI helps
Robita AI turns this kind of Flexiv demonstration into a deployment plan: we assess the manual workflow, define the tooling and fixture assumptions, validate the robot capability, and map the pilot path from first test to production rollout. For automotive and mobility applications, that means connecting the visible robot motion to practical questions like cycle time, safety, operator handoff, data capture, and integration with the existing workstation.
Complexity reduction
How Flexiv force control reduces complexity
Flexiv force control lets the robot adapt during contact instead of relying only on exact position commands. That reduces the need for heavy custom mechanics, perfectly rigid fixtures, and long exception programming because the robot can feel insertion, pressure, and surface contact while it works.