{ "schema_version": "1", "id": "pythonrobotics-dijkstra-planner", "name": "Dijkstra collision-checked grid path", "summary": "Dijkstra collision-checked grid path. Parameterized bounded JSON adapter; numerical operation, no hardware execution.", "capabilities": [ "algorithm", "grid", "executable-json", "source-adaptation" ], "aliases": [ "grid", "Dijkstra collision-checked grid path" ], "source": { "repository": "https://github.com/AtsushiSakai/PythonRobotics", "commit": "cdd0cc888802b584c2d654ca85e3c5460973487d", "paths": [ "ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py", "ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py", "InvertedPendulum/inverted_pendulum_lqr_control.py", "LICENSE", "Localization/extended_kalman_filter/ekf_with_velocity_correction.py", "Localization/extended_kalman_filter/extended_kalman_filter.py", "Localization/unscented_kalman_filter/unscented_kalman_filter.py", "PathPlanning/AStar/a_star.py", "PathPlanning/BSplinePath/bspline_path.py", "PathPlanning/BezierPath/bezier_path.py", "PathPlanning/BidirectionalAStar/bidirectional_a_star.py", "PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py", "PathPlanning/BreadthFirstSearch/breadth_first_search.py", "PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py", "PathPlanning/CubicSpline/cubic_spline_planner.py", "PathPlanning/DepthFirstSearch/depth_first_search.py", "PathPlanning/Dijkstra/dijkstra.py", "PathPlanning/DubinsPath/dubins_path_planner.py", "PathPlanning/DynamicWindowApproach/dynamic_window_approach.py", "PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py", "PathPlanning/PotentialFieldPlanning/potential_field_planning.py", "PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py", "PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py", "PathPlanning/RRT/rrt.py", "PathPlanning/RRT/rrt_with_pathsmoothing.py", "PathPlanning/RRTStar/rrt_star.py", "PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py", "PathTracking/pure_pursuit/pure_pursuit.py", "PathTracking/stanley_control/stanley_control.py", "utils/__init__.py", "utils/angle.py", "utils/plot.py", "ArmNavigation/n_joint_arm_to_point_control/__init__.py", "ArmNavigation/__init__.py", "__init__.py", "Localization/__init__.py", "PathPlanning/__init__.py", "PathPlanning/CubicSpline/__init__.py", "PathPlanning/DubinsPath/__init__.py", "PathPlanning/RRT/__init__.py", "PathPlanning/RRTStar/__init__.py", "PathTracking/__init__.py" ], "archive_url": "https://codeload.github.com/AtsushiSakai/PythonRobotics/tar.gz/cdd0cc888802b584c2d654ca85e3c5460973487d", "archive_sha256": "6d3ccf43be4ae770a9e8dffd463c2561665847df5b7ddf693b01130004dbcee5", "archive_bytes": 12311789, "authors": [ "Atsushi Sakai and PythonRobotics contributors" ] }, "license": { "spdx": "MIT", "url": "https://github.com/AtsushiSakai/PythonRobotics/blob/cdd0cc888802b584c2d654ca85e3c5460973487d/LICENSE", "verified": true, "scope": "Selected PythonRobotics algorithm demonstration source under the pinned MIT license text, Copyright (c) 2016 - now Atsushi Sakai and other contributors. Preserve repository license and each selected file's author notices. This is source-text scope only; dependencies, mathematical references, generated plots and any external assets retain separate terms. GitHub's automated classifier reports NOASSERTION; the MIT identity here comes from the exact reviewed LICENSE bytes, not that classifier." }, "compatibility": [ { "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" } ], "observation": { "required": [ "explicit_json_task_input" ], "privileged_state": false, "description": "All inputs and outputs follow interface v1. Simulation adapters query exact synthetic simulator state; no perception or physical sensor compatibility." }, "requirements": { "python": ">=3.10,<3.11", "packages": [ "contourpy==1.3.2", "cycler==0.12.1", "fonttools==4.65.0", "kiwisolver==1.5.1", "matplotlib==3.9.4", "numpy==1.26.4", "packaging==26.3", "pillow==12.3.0", "pyparsing==3.3.3", "python-dateutil==2.9.0.post0", "scipy==1.13.1", "six==1.17.0" ], "hardware": [ "x86_64 CPU; no GPU or robot required" ], "rendering": "No display. Matplotlib Agg for numerical imports; PyBullet DIRECT for simulation.", "api_keys": [], "network": [ "Pinned source archive once during preparation", "PyPI for declared dependency installation" ], "dependency_lock": "Runtime dependency versions are pinned for the supported Python 3.10 x86_64 CPU environments and checked against execution receipts. Platform-specific wheel hashes are not represented as a universal artifact lock; prepare captures pip freeze." }, "invocation": { "entrypoint": "python run.py", "argv": [ "{python}", "{recipe_dir}/run.py", "--source-dir", "{source_dir}", "--example" ], "inputs": { "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 }, "outputs": { "format": "single JSON object on stdout", "payload_selector": "outputs", "schema": { "type": "object", "required": [ "path_xy", "length_m" ], "additionalProperties": false, "properties": { "path_xy": { "type": "array", "minItems": 2, "maxItems": 2048, "items": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -100.0, "maximum": 100.0 } }, "x-unit": "m", "x-frame": "planar_map_xy", "x-order": [ "x", "y" ], "description": "path_xy; units=m; frame=planar_map_xy; component order=x,y" }, "length_m": { "type": "number", "minimum": 0, "maximum": 100000, "x-unit": "m", "x-frame": "planar_map_xy", "description": "length_m; units=m; frame=planar_map_xy" } } } }, "example": "After prepare, replace --example in next_command with --input /absolute/path/task.json; use --describe for the exact contract without execution." }, "interface": { "version": "1", "kind": "algorithm", "abstraction_level": 0, "execution_domain": "computation", "language": "python", "input_transport": "json_file_or_inline", "output_transport": "json_stdout", "conventions": { "linear_unit": "m", "angular_unit": "rad", "time_unit": "s", "coordinate_frame": "planar_map_xy", "joint_order": "not_used", "quaternion_order": "not_used" }, "input_schema": { "type": "object", "required": [ "start", "goal", "bounds", "obstacles", "robot_radius" ], "additionalProperties": false, "properties": { "start": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": 1, "maximum": 39 }, "x-unit": "m", "x-frame": "planar_map_xy", "x-order": [ "x", "y" ], "description": "start; units=m; frame=planar_map_xy; component order=x,y" }, "goal": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": 1, "maximum": 39 }, "x-unit": "m", "x-frame": "planar_map_xy", "x-order": [ "x", "y" ], "description": "goal; units=m; frame=planar_map_xy; component order=x,y" }, "bounds": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "integer", "minimum": 0, "maximum": 40 }, "x-unit": "m", "x-frame": "planar_map_xy", "description": "bounds; units=m; frame=planar_map_xy" }, "obstacles": { "type": "array", "minItems": 0, "maxItems": 200, "items": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": 0, "maximum": 40 } }, "x-unit": "m", "x-frame": "planar_map_xy", "x-order": [ "x", "y" ], "description": "obstacles; units=m; frame=planar_map_xy; component order=x,y" }, "robot_radius": { "type": "number", "minimum": 0.1, "maximum": 0.6, "x-unit": "m", "x-frame": "planar_map_xy", "description": "robot_radius; units=m; frame=planar_map_xy" } } }, "output_schema": { "type": "object", "required": [ "path_xy", "length_m" ], "additionalProperties": false, "properties": { "path_xy": { "type": "array", "minItems": 2, "maxItems": 2048, "items": { "type": "array", "minItems": 2, "maxItems": 2, "items": { "type": "number", "minimum": -100.0, "maximum": 100.0 } }, "x-unit": "m", "x-frame": "planar_map_xy", "x-order": [ "x", "y" ], "description": "path_xy; units=m; frame=planar_map_xy; component order=x,y" }, "length_m": { "type": "number", "minimum": 0, "maximum": 100000, "x-unit": "m", "x-frame": "planar_map_xy", "description": "length_m; units=m; frame=planar_map_xy" } } }, "preconditions": [ "Inputs use the declared units and frame; no implicit transforms.", "This exact numerical/simulation model is not a physical robot configuration." ], "postconditions": [ "All declared checks must pass and all outputs must be finite.", "Success applies only to this operation and model, not higher-level task completion." ], "failure_codes": [ "invalid_input_or_environment", "execution_failed", "task_failed" ], "composition": { "depends_on": [], "automatic_frame_conversion": false, "automatic_hardware_execution": false }, "output_selector": "outputs", "origin_reference_ids": [], "origin_relationship": "adapted_from; inspect declared model and inputs, not original robot compatibility" }, "completion": { "success_condition": "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.", "timeout_seconds": 30, "failure_modes": [ "Invalid or oversized JSON", "Source checksum mismatch", "Unavailable pinned dependency", "No valid path or postcondition failure", "Execution exceeding the declared bounded algorithm budget" ], "stop_condition": "Caller must terminate the separate adapter process after 30 seconds; adapters bound iterations/scene steps and close simulation clients." }, "preparation": { "recipe_path": "recipes/pythonrobotics-dijkstra-planner", "requirements_file": "recipes/pythonrobotics-dijkstra-planner/requirements.txt", "source_selection": "declared_paths", "install_argv": [ "{python}", "-m", "pip", "install", "--only-binary=:all:", "-r", "{recipe_dir}/requirements.txt" ], "execution_separate": true, "files": { "run.py": "# Generated from tools/recipe_adapters and catalog/executable-conversions.json.\n# Hub adapter: MIT. Adapted upstream example retains its original license and authors.\n\"\"\"Source template for standalone, bounded JSON recipe adapters (MIT, Hub authors).\n\nThe generator embeds this template and a reviewed backend in each published\nrecipe. Importing this template does not import or execute upstream source.\n\"\"\"\nimport argparse\nimport contextlib\nimport hashlib\nimport importlib.util\nimport io\nimport json\nimport math\nimport os\nfrom pathlib import Path\nimport random\nimport sys\n\n\nclass ContractError(ValueError):\n pass\n\n\ndef validate_data(value, schema, path='$'):\n \"\"\"Supported, published JSON Schema subset; bool is never a number.\"\"\"\n kind = schema.get('type')\n checks = {'object': lambda: isinstance(value, dict), 'array': lambda: isinstance(value, list),\n 'number': lambda: type(value) in (int, float) and math.isfinite(value),\n 'integer': lambda: type(value) is int, 'string': lambda: isinstance(value, str),\n 'boolean': lambda: isinstance(value, bool), 'null': lambda: value is None}\n if kind and (kind not in checks or not checks[kind]()):\n raise ContractError(path + ': expected ' + str(kind))\n if 'enum' in schema and value not in schema['enum']:\n raise ContractError(path + ': unsupported value')\n if type(value) in (int, float):\n if not math.isfinite(value) or value < schema.get('minimum', value) or value > schema.get('maximum', value):\n raise ContractError(path + ': outside numeric bounds')\n if isinstance(value, dict):\n if len(value) > 32: raise ContractError(path + ': too many keys')\n for name in schema.get('required', []):\n if name not in value: raise ContractError(path + '.' + name + ': required')\n props = schema.get('properties', {})\n for name, item in value.items():\n if name not in props and schema.get('additionalProperties') is False:\n raise ContractError(path + '.' + name + ': unknown field')\n validate_data(item, props.get(name, {}), path + '.' + name)\n elif isinstance(value, list):\n if not schema.get('minItems', 0) <= len(value) <= schema.get('maxItems', 4096):\n raise ContractError(path + ': invalid array length')\n for i, item in enumerate(value): validate_data(item, schema.get('items', {}), path + '[' + str(i) + ']')\n elif isinstance(value, str) and len(value) > schema.get('maxLength', 4096):\n raise ContractError(path + ': string too long')\n\n\ndef verify_source(root, spec):\n root = Path(root).resolve()\n for name, expected in spec['source_hashes'].items():\n target = root / name\n if target.is_symlink() or not target.resolve().is_relative_to(root) or not target.is_file():\n raise ContractError('Pinned source missing or outside source root: ' + name)\n if hashlib.sha256(target.read_bytes()).hexdigest() != expected:\n raise ContractError('Pinned source checksum changed: ' + name)\n return root\n\n\ndef load_upstream(root, relative):\n \"\"\"Only a generator-pinned path is accepted; never a path from task input.\"\"\"\n target = root / relative\n for directory in [root, root / 'PathPlanning', target.parent]:\n sys.path.insert(0, str(directory))\n module_spec = importlib.util.spec_from_file_location('rsh_upstream', target)\n module = importlib.util.module_from_spec(module_spec)\n module_spec.loader.exec_module(module)\n if hasattr(module, 'show_animation'): module.show_animation = False\n return module\n\n\ndef clean_json(value):\n if hasattr(value, 'tolist'): return clean_json(value.tolist())\n if isinstance(value, dict): return {k: clean_json(v) for k,v in value.items()}\n if isinstance(value, (tuple, list)): return [clean_json(v) for v in value]\n if hasattr(value, 'item'): return clean_json(value.item())\n if isinstance(value, float) and not math.isfinite(value): raise ContractError('Non-finite output')\n return value\n\n\ndef adapter_main(spec, backend):\n parser = argparse.ArgumentParser(description=spec['name'])\n parser.add_argument('--source-dir')\n group = parser.add_mutually_exclusive_group(required=True)\n group.add_argument('--input', type=Path, help='UTF-8 JSON file; no code or paths inside task input')\n group.add_argument('--input-json', help='Inline JSON task input')\n group.add_argument('--example', action='store_true', help='Run the declared example input')\n group.add_argument('--describe', action='store_true', help='Read the contract without importing upstream code')\n args = parser.parse_args()\n try:\n if args.describe:\n print(json.dumps({'skill_id': spec['id'], 'interface': spec['interface'], 'example_input': spec['example']}, allow_nan=False))\n return 0\n if args.input:\n with args.input.open('rb') as stream: raw=stream.read(65537)\n else: raw=args.input_json.encode() if args.input_json else None\n if raw is not None and len(raw) > 65536: raise ContractError('Input exceeds 64 KiB')\n data = json.loads(raw) if raw is not None else spec['example']\n validate_data(data, spec['interface']['input_schema'])\n if not args.source_dir: raise ContractError('--source-dir is required for execution')\n root = verify_source(args.source_dir, spec)\n os.environ['MPLBACKEND'] = 'Agg'\n os.environ.setdefault('OMP_NUM_THREADS', '1')\n random.seed(data.get('seed', 0))\n # Upstream diagnostic prints do not contaminate the machine response.\n with contextlib.redirect_stdout(sys.stderr):\n outputs, checks = backend(root, data, spec)\n outputs, checks = clean_json(outputs), clean_json(checks)\n validate_data(outputs, spec['interface']['output_schema'])\n if not checks or not all(type(v) is bool for v in checks.values()): raise ContractError('Invalid postcondition report')\n ok = all(checks.values())\n print(json.dumps({'schema_version':'1','skill_id':spec['id'],'status':'task_complete' if ok else 'task_failed',\n 'success':ok,'outputs':outputs,'checks':checks,'execution_domain':spec['interface']['execution_domain'],\n 'source_commit':spec['source_commit'],'interface_version':'1','hardware_execution':False}, allow_nan=False, separators=(',',':')))\n return 0 if ok else 1\n except (ContractError, ValueError, OSError, ImportError) as error:\n print(json.dumps({'schema_version':'1','skill_id':spec['id'],'status':'failed','success':False,\n 'reason_code':'invalid_input_or_environment','message':str(error),'hardware_execution':False}, allow_nan=False))\n return 2\n except Exception as error:\n print(json.dumps({'schema_version':'1','skill_id':spec['id'],'status':'failed','success':False,\n 'reason_code':'execution_failed','message':str(error)[:1000],'hardware_execution':False}, allow_nan=False))\n return 1\n\n\n\"\"\"Bounded adapters calling original pinned PythonRobotics functions (MIT).\nUpstream algorithms remain in their own immutable checkout; no main/GUI runs.\n\"\"\"\n\n\ndef segment_distance(point, a, b):\n dx, dy = b[0]-a[0], b[1]-a[1]\n t = max(0., min(1., ((point[0]-a[0])*dx+(point[1]-a[1])*dy)/(dx*dx+dy*dy))) if dx or dy else 0.\n return math.hypot(point[0]-a[0]-t*dx, point[1]-a[1]-t*dy)\n\n\ndef path_length(path):\n return sum(math.dist(a,b) for a,b in zip(path,path[1:]))\n\n\ndef path_checks(path, start, goal, obstacles=None, radius=0., tolerance=1e-6):\n checks={'nonempty':len(path)>1,\n 'start_reached':bool(path) and math.dist(path[0][:2],start[:2])<=tolerance,\n 'goal_reached':bool(path) and math.dist(path[-1][:2],goal[:2])<=tolerance}\n if obstacles is not None:\n checks['supplied_obstacles_clear']=all(segment_distance(o,a,b)+1e-8 >= radius+(o[2] if len(o)>2 else 0.)\n for a,b in zip(path,path[1:]) for o in obstacles)\n return checks\n\n\ndef pythonrobotics_backend(root, d, spec):\n import numpy as np\n np.random.seed(d.get('seed',0))\n m=load_upstream(root,spec['source_path'])\n op=spec['operation']\n if op=='grid':\n lo,hi=d['bounds']\n if lo>=hi or hi-lo>40: raise ContractError('bounds must increase and span at most 40 m')\n boundary=[[x,y] for x in range(lo,hi+1) for y in (lo,hi)] + [[x,y] for y in range(lo,hi+1) for x in (lo,hi)]\n obs=boundary+d['obstacles']\n if any(not lo=d['bounds'][1]: raise ContractError('Bounds must increase')\n if any(o[2]<=0 for o in d['obstacles']): raise ContractError('Obstacle radii must be positive')\n cls=getattr(m,{'rrt':'RRT','rrt_star':'RRTStar','rrt_sobol':'RRTSobol'}[op])\n planner=cls(start=d['start'],goal=d['goal'],obstacle_list=d['obstacles'],rand_area=d['bounds'],\n expand_dis=2.,path_resolution=.1,max_iter=1500,robot_radius=d['robot_radius'])\n path=planner.planning(animation=False) or []\n if path and math.dist(path[-1],d['start'])=hi or hi-lo>40: raise ContractError('Invalid bounds')\n obs=d['obstacles']+[[x,y] for x in range(lo,hi+1) for y in (lo,hi)]+[[x,y] for y in range(lo,hi+1) for x in (lo,hi)]\n m.N_SAMPLE=300\n x,y=m.prm_planning(*d['start'],*d['goal'],[p[0] for p in obs],[p[1] for p in obs],d['robot_radius'],rng=np.random.default_rng(d['seed']))\n path=list(map(list,zip(x,y)))\n if path and math.dist(path[-1],d['start'])=cfg.robot_radius-1e-8 for p in trajectory for o in d['obstacles'])}\n if op in ('ekf','ekf_velocity','ukf'):\n dimension=5 if op=='ekf_velocity' else 4\n x=np.array(d['state']).reshape(dimension,1); cov=np.array(d['covariance']); z=np.array(d['measurement_xy']).reshape(2,1); u=np.array(d['control_v_yawrate']).reshape(2,1)\n if not np.allclose(cov,cov.T,atol=1e-10) or np.linalg.eigvalsh(cov).min()<=0: raise ContractError('Covariance must be symmetric positive definite')\n if op=='ukf': state,covariance=m.ukf_estimation(x,cov,z,u,*m.setup_ukf(4))\n else: state,covariance=m.ekf_estimation(x,cov,z,u)\n return {'state_xy_yaw_v_scale' if op=='ekf_velocity' else 'state_xy_yaw_v':state.reshape(-1),'covariance':covariance}, {\n 'finite_state':bool(np.isfinite(state).all()), 'symmetric_covariance':bool(np.allclose(covariance,covariance.T,atol=1e-8)),\n 'positive_semidefinite_covariance':bool(np.linalg.eigvalsh(covariance).min()>=-1e-8)}\n if op in ('fk','ik'):\n lengths=np.array(d['link_lengths']); angles=np.array(d['joint_angles'])\n if len(lengths)!=len(angles) or any(lengths<=0): raise ContractError('Positive link lengths must match joint count')\n m.N_LINKS=len(lengths)\n if op=='ik':\n if np.linalg.norm(d['target_xy'])>sum(lengths): raise ContractError('Target outside reach')\n m.N_ITERATIONS=1000; angles,solved=m.inverse_kinematics(lengths,angles,np.array(d['target_xy']))\n else: solved=True\n endpoint=m.forward_kinematics(lengths,angles)\n independent=np.array([sum(lengths*np.cos(np.cumsum(angles))),sum(lengths*np.sin(np.cumsum(angles)))])\n checks={'forward_kinematics_agrees':bool(np.linalg.norm(endpoint-independent)<1e-8)}\n if op=='ik': checks['target_residual_below_0_1_m']=bool(solved and np.linalg.norm(endpoint-np.array(d['target_xy']))<.1)\n return {'joint_angles_rad':angles,'end_effector_xy_m':endpoint},checks\n if op=='lqr':\n A,B=m.get_model_matrix(); K,P,eigs=m.dlqr(A,B,np.diag(d['state_cost_diagonal']),np.array([[d['force_cost']]]))\n force=-K@np.array(d['state']).reshape(4,1)\n return {'force_n':float(force.item()),'gain':K.tolist()[0],'closed_loop_spectral_radius':float(max(abs(eigs)))}, {'stable_discrete_linear_model':bool(max(abs(eigs))<1.)}\n if op=='potential':\n if not d['obstacles']: raise ContractError('Potential-field source requires obstacle points')\n x,y=m.potential_field_planning(*d['start'],*d['goal'],[p[0] for p in d['obstacles']],[p[1] for p in d['obstacles']],.5,d['robot_radius'])\n path=list(map(list,zip(x,y)))\n return {'path_xy':path,'length_m':path_length(path)},path_checks(path,d['start'],d['goal'],d['obstacles'],d['robot_radius'],tolerance=.5)\n if op=='smoothing':\n if not all(path_checks(d['path_xy'],d['path_xy'][0],d['path_xy'][-1],d['obstacles'],d['robot_radius']).values()):\n raise ContractError('Input path must clear every supplied inflated obstacle')\n # Upstream shortcut selection remains unchanged. Replace its sampled\n # collision test and off-by-one arclength interpolation at the boundary.\n def exact_clear(first, second, obstacles, robot_radius=0., sample_step=.2):\n return all(segment_distance(o,first,second)>robot_radius+o[2] for o in obstacles)\n def arclength_point(path, distance):\n remaining=distance\n for index,(a,b) in enumerate(zip(path,path[1:])):\n length=math.dist(a,b)\n if length and remaining<=length:\n ratio=remaining/length\n return [a[0]+ratio*(b[0]-a[0]),a[1]+ratio*(b[1]-a[1]),index]\n remaining-=length\n return [path[-1][0],path[-1][1],len(path)-2]\n m.line_collision_check=exact_clear\n m.get_target_point=arclength_point\n path=m.path_smoothing(d['path_xy'],100,d['obstacles'],robot_radius=d['robot_radius'])\n checks=path_checks(path,d['path_xy'][0],d['path_xy'][-1],d['obstacles'],d['robot_radius'])\n checks['not_longer']=path_length(path)<=path_length(d['path_xy'])+1e-8\n return {'path_xy':path,'length_m':path_length(path)},checks\n raise ContractError('Unknown reviewed operation')\n\nSPEC = json.loads('{\"id\":\"pythonrobotics-dijkstra-planner\",\"name\":\"Dijkstra collision-checked grid path\",\"operation\":\"grid\",\"source_path\":\"PathPlanning/Dijkstra/dijkstra.py\",\"source_commit\":\"cdd0cc888802b584c2d654ca85e3c5460973487d\",\"source_hashes\":{\"ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py\":\"0050d72932952f05992c0195bdcc57cfb9dfaa13be81daf9e22c6344a8e5735d\",\"ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py\":\"7ef0e52b7f949b84d82d5ea2824cd3dbc0f60e8e21dd8a9499b407231a310b1d\",\"InvertedPendulum/inverted_pendulum_lqr_control.py\":\"8af1d6540a40866fdb35822e3eea9042a58d43c4df28d4ebd7beec22593d1b55\",\"LICENSE\":\"ca6f10832a25c1cd07fa62872458ca336a6ea83ee2f2236216086bd7a5bbf264\",\"Localization/extended_kalman_filter/ekf_with_velocity_correction.py\":\"58bb755f0f917b07d20dace4750aca16312c19e5f897e7e7d376732acd5e4cfe\",\"Localization/extended_kalman_filter/extended_kalman_filter.py\":\"fa9bb52dbb63e6e959ba4e7bcf20cbe8d6ce69e88453ad0f605a21fc6979a28e\",\"Localization/unscented_kalman_filter/unscented_kalman_filter.py\":\"ec89f8bd08c45d1dca15f41a7313dfa402679e73ad7290293ab7ea2d2fcce94e\",\"PathPlanning/AStar/a_star.py\":\"387293061a8509557bb80a02a0f279b0b2b4629c0189db661a93fee5d9e0b74a\",\"PathPlanning/BSplinePath/bspline_path.py\":\"5d45a5400b684b59ef0e3ed1f780f9ba1ad069e1fde3879abf2f21307b77aa94\",\"PathPlanning/BezierPath/bezier_path.py\":\"4f3bb51aba78a47ab4665b7ccfbbd70acf75607bbdd5f1726a34962ecbc46f5d\",\"PathPlanning/BidirectionalAStar/bidirectional_a_star.py\":\"6870b9bf1666b8d31ba0ffdfe92c9a715dcfdb3a40398e551352a3180da9dc01\",\"PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py\":\"c0536f0296ddefd700b9d754e936949087d822d65901da462971ec2d82a9183c\",\"PathPlanning/BreadthFirstSearch/breadth_first_search.py\":\"960bfbc9e15b81df08d78c8266e8467378f307b5fec9a58b8de2c4bb8feab069\",\"PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py\":\"14d5f7046302cac828ebb9bbd338ef3e06952d79ae112415aa61369901b8af90\",\"PathPlanning/CubicSpline/cubic_spline_planner.py\":\"eb416e3834f5881dbec824729a1ab6010138f49a030ceb29f758b23cc5ed2adc\",\"PathPlanning/DepthFirstSearch/depth_first_search.py\":\"60147610b72c80ce8385f667f4a4e874b60421c5e40f19775f087693248f4afb\",\"PathPlanning/Dijkstra/dijkstra.py\":\"5dceeacad5a3a03245bea9fb9b344252a51a5a1d364aa76d83a8832d975d6574\",\"PathPlanning/DubinsPath/dubins_path_planner.py\":\"07fba67057d900d4c2b7794c9b0818a86342af8a520c914252dec620d94c61eb\",\"PathPlanning/DynamicWindowApproach/dynamic_window_approach.py\":\"985aa9acb1e6de234fa15ec9c83d5a7435ce2d9e22c6f6e308e1ab038141c471\",\"PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py\":\"5b166b9b6a62f1f40e0d018ff509a90d07ce40471f99e2acf2e9f0d5d32de434\",\"PathPlanning/PotentialFieldPlanning/potential_field_planning.py\":\"79bc4c9b5cae8266d3bf4fee05385ea4dd1094f24cdcb09a3b1b4615d3719f01\",\"PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py\":\"7a1ae0ff436bcdf203853449a56761841a57f16a288520d2727284daa9ee3691\",\"PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py\":\"606e96761b7c1590afba5824bf9e3a23c575690a0a16b2256b8a23dae7797272\",\"PathPlanning/RRT/rrt.py\":\"ed8844dba554c6a27cc18137c2214dd066145701216d0aeab974cc4e4fea5d6e\",\"PathPlanning/RRT/rrt_with_pathsmoothing.py\":\"3d5aba05cccd5c065fbdebe24f175cecef3979e924ff637b8afe75ec1387c130\",\"PathPlanning/RRTStar/rrt_star.py\":\"6beeefcc0c8b9755f0257fd442e9b0a35dac1d913fb56a8bf8ce973ba5a20b25\",\"PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py\":\"7b2bc055cd9de93a95d9ed4a000bad049ccc889324600d3736ed4da18f7ea947\",\"PathTracking/pure_pursuit/pure_pursuit.py\":\"d6702311927cb44cbe27546e6e76ba0b7371a6b0715013ac2a40735b299779eb\",\"PathTracking/stanley_control/stanley_control.py\":\"ec426a511821620d798a46a35f0ab64382bd79ff417e0f59fb0c1f7873d8c7a0\",\"utils/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"utils/angle.py\":\"62c3f0fdc76298d772d574b7a4357fded9265d1e0af048d87b5c8ca1a528017a\",\"utils/plot.py\":\"7958fb8403bd3b05550a0356e3aeaeaee194678dfb8a069350a089b0666e8537\",\"ArmNavigation/n_joint_arm_to_point_control/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"ArmNavigation/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"Localization/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathPlanning/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathPlanning/CubicSpline/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathPlanning/DubinsPath/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathPlanning/RRT/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathPlanning/RRTStar/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\"PathTracking/__init__.py\":\"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\"},\"interface\":{\"version\":\"1\",\"kind\":\"algorithm\",\"abstraction_level\":0,\"execution_domain\":\"computation\",\"language\":\"python\",\"input_transport\":\"json_file_or_inline\",\"output_transport\":\"json_stdout\",\"conventions\":{\"linear_unit\":\"m\",\"angular_unit\":\"rad\",\"time_unit\":\"s\",\"coordinate_frame\":\"planar_map_xy\",\"joint_order\":\"not_used\",\"quaternion_order\":\"not_used\"},\"input_schema\":{\"type\":\"object\",\"required\":[\"start\",\"goal\",\"bounds\",\"obstacles\",\"robot_radius\"],\"additionalProperties\":false,\"properties\":{\"start\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":{\"type\":\"number\",\"minimum\":1,\"maximum\":39},\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"x-order\":[\"x\",\"y\"],\"description\":\"start; units=m; frame=planar_map_xy; component order=x,y\"},\"goal\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":{\"type\":\"number\",\"minimum\":1,\"maximum\":39},\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"x-order\":[\"x\",\"y\"],\"description\":\"goal; units=m; frame=planar_map_xy; component order=x,y\"},\"bounds\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":{\"type\":\"integer\",\"minimum\":0,\"maximum\":40},\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"description\":\"bounds; units=m; frame=planar_map_xy\"},\"obstacles\":{\"type\":\"array\",\"minItems\":0,\"maxItems\":200,\"items\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":{\"type\":\"number\",\"minimum\":0,\"maximum\":40}},\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"x-order\":[\"x\",\"y\"],\"description\":\"obstacles; units=m; frame=planar_map_xy; component order=x,y\"},\"robot_radius\":{\"type\":\"number\",\"minimum\":0.1,\"maximum\":0.6,\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"description\":\"robot_radius; units=m; frame=planar_map_xy\"}}},\"output_schema\":{\"type\":\"object\",\"required\":[\"path_xy\",\"length_m\"],\"additionalProperties\":false,\"properties\":{\"path_xy\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2048,\"items\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":{\"type\":\"number\",\"minimum\":-100.0,\"maximum\":100.0}},\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"x-order\":[\"x\",\"y\"],\"description\":\"path_xy; units=m; frame=planar_map_xy; component order=x,y\"},\"length_m\":{\"type\":\"number\",\"minimum\":0,\"maximum\":100000,\"x-unit\":\"m\",\"x-frame\":\"planar_map_xy\",\"description\":\"length_m; units=m; frame=planar_map_xy\"}}},\"preconditions\":[\"Inputs use the declared units and frame; no implicit transforms.\",\"This exact numerical/simulation model is not a physical robot configuration.\"],\"postconditions\":[\"All declared checks must pass and all outputs must be finite.\",\"Success applies only to this operation and model, not higher-level task completion.\"],\"failure_codes\":[\"invalid_input_or_environment\",\"execution_failed\",\"task_failed\"],\"composition\":{\"depends_on\":[],\"automatic_frame_conversion\":false,\"automatic_hardware_execution\":false},\"output_selector\":\"outputs\"},\"example\":{\"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},\"class\":\"DijkstraPlanner\"}')\n\nif __name__ == \"__main__\":\n raise SystemExit(adapter_main(SPEC, pythonrobotics_backend))\n", "requirements.txt": "contourpy==1.3.2\ncycler==0.12.1\nfonttools==4.65.0\nkiwisolver==1.5.1\nmatplotlib==3.9.4\nnumpy==1.26.4\npackaging==26.3\npillow==12.3.0\npyparsing==3.3.3\npython-dateutil==2.9.0.post0\nscipy==1.13.1\nsix==1.17.0\n" } }, "evidence": { "level": "task_complete", "verified_by": "Hub maintainer runtime harness", "date": "2026-10-19", "environment": { "scope": "computation", "platforms": [ "linux", "windows" ], "physical_robot": false }, "commands": [ "python tools/verify_executable_recipes.py --source-cache SOURCE_CACHE --output OUTPUT --backend pythonrobotics" ], "result": "Three frozen positive cases passed against this exact adapter; see per-case receipts. This does not estimate general accuracy.", "artifacts": [ "https://raw.githubusercontent.com/Superfast-Robotics/robot-skill-hub/39b06d7a6016edcea27e348e49ffd86f29ec1811/evidence/runtime/executable-conversions-v1/pythonrobotics-dijkstra-planner.json" ], "runtime_verification": "task_complete", "contributor_reported": null, "hub_verification": { "runtime": "task_complete", "scope": "computation", "case_count": 3, "source_unchanged": true } }, "maintenance": { "record_version": "1.0.0", "last_checked": "2026-10-19", "upstream_status": "immutable_source_and_explicit_dependency_versions" }, "contribution": { "allowed_paths": [ "catalog/records/pythonrobotics-dijkstra-planner.json", "docs/recipes/pythonrobotics-dijkstra-planner.md", "recipes/pythonrobotics-dijkstra-planner/run.py", "recipes/pythonrobotics-dijkstra-planner/requirements.txt" ], "instructions": "Preserve upstream origin/license and interface semantics. Edit the finite manifest/templates and regenerate; record new exact-byte evidence for behavior changes." } }