maptor 0.2.1
Multiphase Adaptive Trajectory Optimizer
Requires Python: >=3.10
Keywords: control, engineering, optimization, robotics, aerospace, autonomous, trajectory, planning, numerical, simulation

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  <img src="docs/source/_static/MAPTOR_banner.png" alt="MAPTOR" width="600">
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# MAPTOR: Multiphase Adaptive Trajectory Optimizer

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**Author:** [David Timothy](https://github.com/ChenDavidTimothy)

**Contact:** chendavidtimothy@gmail.com

A Python framework for **trajectory and design optimization** using optimal control. MAPTOR simultaneously optimizes system parameters and trajectories for vehicles, robots, spacecraft, and other dynamic systems.

## When to Use MAPTOR

**If you only need basic PATH planning** (geometry-focused problems), use path planning algorithms:
- A*, Dijkstra, basic RRT, PRM
- Fastest for obstacle avoidance without complex dynamics

**If you need TRAJECTORY optimization** with simple constraints, faster methods exist:
- iLQR (iterative Linear Quadratic Regulator)
- ALTRO (Augmented Lagrangian Trajectory Optimizer)
- Faster convergence for dynamics-heavy, constraint-light problems

**Use MAPTOR for complex DESIGN + TRAJECTORY problems:**
- Multiple design parameters + trajectory optimization
- Complex nonlinear path constraints (obstacle avoidance, state bounds)
- Complex multibody dynamics with built-in SymPy Lagrangian mechanics integration  
- Multiphase missions with automatic phase linking
- When you need the full flexibility of direct transcription

## Quick Start: Pure Trajectory Optimization

```python
import maptor as mtor

# Minimum time trajectory: reach target with bounded control
problem = mtor.Problem("Minimum Time to Target")
phase = problem.set_phase(1)

# Variables
t = phase.time(initial=0.0)  # Free final time
position = phase.state("position", initial=0.0, final=1.0)
velocity = phase.state("velocity", initial=0.0, final=0.0)
force = phase.control("force", boundary=(-2.0, 2.0))

# Dynamics and objective
phase.dynamics({position: velocity, velocity: force})
problem.minimize(t.final)

# Solve
phase.mesh([8], [-1.0, 1.0])
solution = mtor.solve_adaptive(problem)

if solution.status["success"]:
    print(f"Optimal time: {solution.status['objective']:.3f} seconds")
    solution.plot()
```

## Example: Simultaneous Design and Trajectory Optimization

While the above shows basic trajectory optimization, MAPTOR also handles simultaneous design and trajectory optimization:

```python
import maptor as mtor


# Engine sizing optimization with mass penalty
problem = mtor.Problem("Engine Sizing Optimization")
phase = problem.set_phase(1)

# Design parameter: maximum engine thrust capability
max_thrust = problem.parameter("max_thrust", boundary=(1000, 5000))

# Physical parameters
base_mass = 100.0  # kg (vehicle dry mass)
engine_mass_factor = 0.05  # kg per Newton (engine specific mass)
gravity = 9.81  # m/s²

# Mission variables
t = phase.time(initial=0.0)
altitude = phase.state("altitude", initial=0.0, final=1000.0)
velocity = phase.state("velocity", initial=0.0, final=0.0)
thrust = phase.control("thrust", boundary=(0, None))

# Engine cannot exceed design capability
phase.path_constraints(thrust <= max_thrust)

# Total vehicle mass increases with engine size
total_mass = base_mass + max_thrust * engine_mass_factor

# Vertical flight dynamics with gravity
phase.dynamics({altitude: velocity, velocity: thrust / total_mass - gravity})

# Objective: minimize mission time + engine mass penalty
engine_mass_cost = max_thrust * engine_mass_factor * 0.1  # Cost per kg of engine
problem.minimize(t.final + engine_mass_cost)

# Mesh configuration
phase.mesh([6], [-1.0, 1.0])


phase.guess(terminal_time=50.0)

# Solve with adaptive mesh refinement
solution = mtor.solve_adaptive(problem)

# Results
if solution.status["success"]:
    optimal_thrust = solution.parameters["values"][0]
    engine_mass = optimal_thrust * engine_mass_factor
    total_vehicle_mass = base_mass + engine_mass
    mission_time = solution.status["objective"] - engine_mass * 0.1

    print("Optimal Engine Design:")
    print(f"  Max thrust capability: {optimal_thrust:.0f} N")
    print(f"  Engine mass: {engine_mass:.1f} kg")
    print(f"  Total vehicle mass: {total_vehicle_mass:.1f} kg")
    print(f"  Mission time: {mission_time:.1f} seconds")
    print(f"  Thrust-to-weight ratio: {optimal_thrust / (total_vehicle_mass * gravity):.2f}")

    solution.plot()
else:
    print(f"Optimization failed: {solution.status['message']}")

#Output
#Optimal Engine Design:
#  Max thrust capability: 3535 N
#  Engine mass: 176.7 kg
#  Total vehicle mass: 276.7 kg
#  Mission time: 29.6 seconds
#  Thrust-to-weight ratio: 1.30
```

**Example Applications**:
- **Aerospace**: Optimize fuel capacity + ascent trajectory
- **Robotics**: Optimize actuator sizing + motion planning
- **Autonomous Vehicles**: Optimize battery capacity + route planning

**Beyond Spatial Trajectories**: MAPTOR also handles abstract optimal control problems where "trajectory" refers to the evolution of any system state over time (chemical processes, financial optimization, resource allocation).

## Core Methodology

MAPTOR implements the **Legendre-Gauss-Radau pseudospectral method** with:

- **Spectral accuracy**: Exponential convergence for smooth solutions
- **Adaptive mesh refinement**: Automatic error control through phs-adaptive mesh refinement method
- **Multiphase capability**: Complex missions with automatic phase linking
- **Symbolic computation**: Built on CasADi for exact differentiation and optimization

## Installation

```bash
pip install maptor
```

**Requirements**: Python 3.10+, NumPy, SciPy, CasADi, Matplotlib

**Development Installation**:
```bash
git clone https://github.com/maptor/maptor.git
cd maptor
pip install -e .
```

## Documentation

| Resource | Description |
|----------|-------------|
| **[Installation Guide](https://maptor.github.io/maptor/installation.html)** | Setup and dependencies |
| **[Quick Start](https://maptor.github.io/maptor/quickstart.html)** | Basic workflow and first example |
| **[Problem Definition Tutorial](https://maptor.github.io/maptor/tutorials/problem_definition.html)** | Comprehensive problem construction guide |
| **[Solution Analysis Tutorial](https://maptor.github.io/maptor/tutorials/solution_access.html)** | Working with optimization results |
| **[Examples Gallery](https://maptor.github.io/maptor/examples/index.html)** | Complete problems with mathematical formulations |
| **[API Reference](https://maptor.github.io/maptor/api/index.html)** | Detailed function documentation |

## Example Trajectories

The examples gallery demonstrates trajectory optimization across multiple domains:

### Design + Trajectory Optimization
- **[3DOF Manipulator Design](https://maptor.github.io/maptor/examples/manipulator_3dof.html)**: Simultaneous motor sizing and trajectory optimization with 5kg payload transport
- **[2DOF Manipulator Design](https://maptor.github.io/maptor/examples/manipulator_2dof.html)**: Actuator investment vs. performance trade-offs with SymPy-generated dynamics

### Advanced Trajectory Optimization
- **[Quadcopter Flight](https://maptor.github.io/maptor/examples/quadcopter.html)**: Quadcopter dynamics with obstacle avoidance
- **[Overtaking Maneuver](https://maptor.github.io/maptor/examples/overtaking_maneuver.html)**: Complex street scenario with dual moving obstacles
- **[Multiphase Vehicle Launch](https://maptor.github.io/maptor/examples/multiphase_vehicle_launch.html)**: Realistic rocket trajectory with stage separations and orbital insertion

### Classical Benchmarks
- **[Hypersensitive Problem](https://maptor.github.io/maptor/examples/hypersensitive.html)**: Challenging optimal control benchmark with sensitive dynamics

## Architecture

MAPTOR provides a layered architecture separating trajectory design from numerical implementation:

```
User API (Problem, solve_adaptive, solve_fixed_mesh)
         ↓
Trajectory Definition (States, controls, dynamics, constraints)
         ↓
Mathematical Framework (Radau pseudospectral method)
         ↓
Symbolic Computation (CasADi expressions and differentiation)
         ↓
Optimization (IPOPT nonlinear programming solver)
```

**Key Design Principles**:
- **Intuitive API**: Define trajectories naturally without numerical details
- **Automatic differentiation**: CasADi handles complex derivative computations
- **Adaptive precision**: Mesh refinement ensures solution accuracy
- **Multiphase support**: Complex missions with automatic phase transitions

## Contributing

We currently do not accept code submissions, but we welcome **issues and feedback reports** from the trajectory optimization and optimal control community. Please use [GitHub Issues](https://github.com/maptor/maptor/issues) to:

- Report bugs or unexpected behavior
- Request new features or enhancements
- Ask questions about usage or implementation
- Suggest improvements to documentation or examples
- Share feedback on your experience with MAPTOR

Your input helps improve MAPTOR for the entire community.

## License

MAPTOR is licensed under the [GNU Lesser General Public License v3.0](LICENSE). This allows use in both open source and proprietary applications while ensuring improvements to the core library remain open.

## Citation

If you use MAPTOR in academic research, please cite:

```bibtex
@software{maptor2025,
  title={MAPTOR: Multiphase Adaptive Trajectory Optimizer},
  author={Timothy, David},
  year={2025},
  url={https://github.com/maptor/maptor},
  version={0.2.0}
}
```

## References

MAPTOR builds upon established methods in computational optimal control:

**Optimal Control Theory and Methods**:
- Betts, J. T. (2020). *Practical Methods for Optimal Control Using Nonlinear Programming, Third Edition*. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611976199

**Pseudospectral Methods**:
- Agamawi, Y. M., & Rao, A. V. (2020). CGPOPS: A C++ Software for Solving Multiple-Phase Optimal Control Problems Using Adaptive Gaussian Quadrature Collocation and Sparse Nonlinear Programming. *ACM Transactions on Mathematical Software*, 46(3), Article 25. https://doi.org/10.1145/3390463

**Adaptive Mesh Refinement**:
- Haman III, G. V., & Rao, A. V. (2024). Adaptive Mesh Refinement and Error Estimation Method for Optimal Control Using Direct Collocation. *arXiv preprint arXiv:2410.07488*. https://arxiv.org/abs/2410.07488

**Symbolic Computation Framework**:
- Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., & Diehl, M. (2019). CasADi -- A software framework for nonlinear optimization and optimal control. *Mathematical Programming Computation*, 11(1), 1-36. https://doi.org/10.1007/s12532-018-0139-4

## Support

- **Documentation**: [https://maptor.github.io/maptor](https://maptor.github.io/maptor)
- **Issues**: [GitHub Issues](https://github.com/maptor/maptor/issues)

## Acknowledgments

MAPTOR implements methods from the computational optimal control literature, particularly pseudospectral collocation techniques and adaptive mesh refinement strategies. The framework leverages CasADi for symbolic computation and automatic differentiation.

---

**Next Steps**: Begin with the [Quick Start Guide](https://maptor.github.io/maptor/quickstart.html) or explore the [Examples Gallery](https://maptor.github.io/maptor/examples/index.html) to see MAPTOR applied to trajectory optimization problems in your domain.