A
licenseNot graded
qualityC
maintenanceWorld-first unified MCP gateway + Control Barrier Function (CBF-QP) safety engine for agentic robotics, enabling LLMs to control physical robots with mathematically proven safety.
1
MIT
genpark-spatial-obstacle-pointcloud-voxel-slicer-skill
by Alpha-Park
Enables ROS2-based robotic systems to consume 2.5D occupancy grids computed from LiDAR/depth point clouds, supporting micro-agent and embodied control integration.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-spatial-obstacle-pointcloud-voxel-slicer-skill
π GenPark MCP Hub Showcase β’ π¦ GenPark Official Website β’ π Documentation
π Overview & Capability
genpark-spatial-obstacle-pointcloud-voxel-slicer-skill is a deterministic, zero-dependency Python skill engineered for autonomous edge robotics, embodied kinematics telemetry, ROS2 communication, and actuator safety supervision.
Executive Capability: Edge LiDAR/depth point cloud voxel slicer computing 2.5D occupancy grids (Isaac Sim / Livox)
β‘ Key Highlights & Value
π Zero External
pipDependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.π Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, ROS2 micro-agents, and embodied control swarms.
π― Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
π Low Latency: Sub-millisecond execution overhead tailored for real-time robotic telemetry control loops.
Related MCP server: genpark-spatial-obstacle-pointcloud-voxel-slicer-skill
ποΈ Architecture & Workflow
graph LR
Sensors([π€ Robotic Hardware Actuators / IMU / LiDAR]) -->|Telemetry Signals| MCP[β‘ MCP Server / ROS2 Micro Edge]
MCP --> Client[π οΈ Embodied Control Kernel]
Client --> Processing[π§ Kinematics & Thermal Sentinel Pipeline]
Processing --> Safety[π‘οΈ Safety Margins & Actuator Setpoints]
Safety --> Controller([βοΈ Low-Level Motor Controllers])π Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import SpatialObstaclePointcloudVoxelSlicerClient
client = SpatialObstaclePointcloudVoxelSlicerClient()
result = client.slice_pointcloud_voxels()
print(result)π Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-spatial-obstacle-pointcloud-voxel-slicer-skill": {
"command": "python",
"args": ["/path/to/genpark-spatial-obstacle-pointcloud-voxel-slicer-skill/mcp_server.py"]
}
}
}π Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
β Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of 1,200+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about embodied robotics frameworks at GenPark AI.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
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Sub-5ms topological 4D associative memory coprocessor for LLMs and multi-agent swarms.
Related MCP Servers
- FlicenseNot gradedqualityBmaintenanceEnables edge LiDAR/depth point cloud processing into 2.5D occupancy grids for robotics safety and control, with native MCP integration for telemetry and actuator supervision.8-
- AlicenseNot gradedqualityBmaintenanceEnables autonomous robotic systems to perform real-time 3D occupancy voxel mapping, raycast-based Bayesian occupancy updates, and sphere collision checking through standard MCP tools.7MIT
- AlicenseNot gradedqualityNot gradedmaintenanceExposes a zero-dependency MCP server that ingests depth-sensor, joint-encoder, and tactile-array telemetry to deliver real-time spatial intelligence, trajectory generation, and safety-guarded control vectors for autonomous robots. It tracks shear-to-normal force ratios, detects micro-vibration slip onset, and adapts grasp force dynamically so agents can prevent object slip during manipulation.7MIT
