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MCP Server Setup

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Linked challenge: Resilient Perception Fusion

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Linked challenge

Resilient Perception Fusion

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Create a skeleton for three MCP servers in Python: one for a generic Lidar, one for a Stereo Camera, and one for an IMU. Each server should expose a 'get_health' and 'get_data' tool.

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Linked challenge

Resilient Perception Fusion

There is a critical need for robotics companies to build sensor-agnostic perception systems. This challenge focuses on building a 'Resilient Perception Layer' that uses Mistral Large 2 for high-level reasoning and Model Context Protocol (MCP) servers to abstract various sensor inputs. Instead of relying on a single expensive Lidar unit, you will build a system that can dynamically switch between stereo vision, solid-state lidar, and ultrasonic sensors based on environmental conditions (e.g., fog, low light, or indoor glare). You will utilize Google Vertex AI to host your reasoning models and deploy MCP servers that act as 'drivers' for different sensor data streams. The goal is to implement a 'Perception Agent' that monitors sensor confidence scores and uses Mistral Large 2 to decide which sensor fusion strategy to employ in real-time, ensuring safe operation of an autonomous mobile base even when primary sensors fail.

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