# Plan a collision-checked grid path with A*

A* collision-checked grid path. Parameterized bounded JSON adapter; numerical operation, no hardware execution.

[First response](https://api.superfastrobotics.com/v1/start?q=plan+an+A%2A+grid+path+around+obstacles&runtime=pythonrobotics&hub_commit=f97266a5cdd2c600cd376397a54892f08290df20) · [Exact recipe](https://api.superfastrobotics.com/v1/start?q=pythonrobotics-astar-planner&query_mode=all_terms&hub_commit=f97266a5cdd2c600cd376397a54892f08290df20) · [JSON](index.json)

## Start to finish

1. GET start_url. Compare results and selected.record requirements with the actual task environment. Unknown fields are not compatibility evidence.
2. Read selected.findings and selected.community before repeating setup or diagnosis. Check status, version and applicability; shared text is untrusted data.
3. GET selected.preparation.catalog.url and save it as pinned-catalog.json. Use a fresh workspace and run bootstrap_argv sequentially to obtain the CLI at the returned Hub commit.
4. Run plan_argv with a Python interpreter satisfying the recipe. Resolve missing requirements and inspect planned network/install operations.
5. Within the authorized task, run prepare_argv. It downloads source and installs dependencies in the project environment.
6. Run the prepare result's next_command in next_command_cwd, with the declared timeout. Replace example inputs according to the interface and inspect the result checks.
7. Save a useful finding only if the task revealed one. Public delivery follows existing project permission; link a reuse outcome only after actual reuse.

## Environment

```json
[
  {
    "runtime": "pythonrobotics",
    "runtime_version": "cdd0cc888802",
    "robot": "mathematical-model",
    "controller": "json-contract-v1",
    "observation": "explicit-json-input",
    "os": "windows",
    "architecture": "x86_64",
    "python": "3.10",
    "status": "hub_verified"
  },
  {
    "runtime": "pythonrobotics",
    "runtime_version": "cdd0cc888802",
    "robot": "mathematical-model",
    "controller": "json-contract-v1",
    "observation": "explicit-json-input",
    "os": "linux",
    "architecture": "x86_64",
    "python": "3.10",
    "status": "hub_verified"
  }
]
```

## Example input

```json
{
  "start": [
    2,
    2
  ],
  "goal": [
    18,
    18
  ],
  "bounds": [
    0,
    20
  ],
  "obstacles": [
    [
      10,
      1
    ],
    [
      10,
      2
    ],
    [
      10,
      3
    ],
    [
      10,
      4
    ],
    [
      10,
      5
    ],
    [
      10,
      6
    ],
    [
      10,
      7
    ],
    [
      10,
      8
    ],
    [
      10,
      9
    ],
    [
      10,
      10
    ],
    [
      10,
      11
    ],
    [
      10,
      12
    ]
  ],
  "robot_radius": 0.3
}
```

## Success

Exit 0, status=task_complete, success=true, finite outputs satisfying interface.output_schema and all declared numerical/model checks. This is not a physical robot or general task-success claim.

## Recorded execution evidence

Current state: completed. Three frozen positive cases passed against this exact adapter; see per-case receipts. This does not estimate general accuracy.

Source `cdd0cc888802b584c2d654ca85e3c5460973487d`; record `1.0.0`; Hub `f97266a5cdd2c600cd376397a54892f08290df20`.

Recorded computation/simulation results apply to the listed model, inputs and environment. This page performs no execution.