Eclares (Energy-Aware Clarity-Driven Ergodic Search) is a framework for planning informative trajectories for persistent coverage in stochastic spatiotemporal environments under battery constraints.

Planning informative trajectories that account for both the spatial distribution of environmental information and constraints such as limited battery capacity makes the long-horizon persistent coverage problem complex. This package implements two main contributions: (1) a method to construct target information spatial distributions for ergodic trajectory optimization using clarity, an information measure bounded in [0,1], which captures information decay from lack of measurements and quantifies maximum attainable information; (2) the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure the robot can return to the charging station when needed. The eware filter is applicable to nonlinear robot models and is computationally lightweight.

For implementation details, see the paper on arXiv (published in IEEE ICRA 2024—see Citation below).

• Julia ≥ 1.9
• See Project.toml for package dependencies

1. Clone the repository:

   git clone https://github.com/yourusername/Eclares.git
   cd Eclares

2. Start Julia and activate the project:

   import Pkg
   Pkg.activate(".")
   Pkg.instantiate()

3. Place the oceanographic data file expt_32_2019.nc4 in the data/ folder (or project root).

Eclares/
├── src/
│   ├── Eclares.jl           # Main package entry
│   ├── QuaternionUtils.jl   # Quaternion math
│   ├── QuadSys.jl           # Quadrotor dynamics, linearization
│   ├── RefTrajLib.jl        # Reference trajectories (Lissajous)
│   ├── OceanographicData.jl # NetCDF salinity/clarity processing
│   ├── MathUtils.jl         # Math utilities
│   ├── DomainMod.jl         # Spatial domain representation
│   ├── ErgodicManager.jl    # Ergodic coverage optimization
│   ├── TrajectoryManagerLib.jl # Trajectory optimization
│   ├── TrajectoryGeneration.jl # Ergodic trajectory generation
│   ├── SimulateCoverge.jl   # Clarity dynamics, DI ergodic sim
│   ├── Controller.jl        # MPC + LQR controller
│   ├── EclaresSim.jl        # Main simulation loop
│   └── Visualization.jl     # MeshCat animation
├── notebooks/
│   └── Eclares_Full_Code_Working.ipynb
├── data/
│   └── expt_32_2019.nc4     # Oceanographic data (NetCDF)
├── utils/
│   └── quadrotor_scaled.obj # Quadrotor mesh for visualization
├── Project.toml
└── README.md

# Activate project
import Pkg
Pkg.activate(joinpath(@__DIR__, ".."))
Pkg.instantiate()

using Eclares

# Initial state: [position, quaternion, velocity, angular_velocity, state_of_charge]
r0 = [0.0; 0.0; 0.0]
q0 = Eclares.QuaternionUtils.L([1.0; 0.0; 0; 0]) * Eclares.QuaternionUtils.rptoq([0.; 0; 0])
x0 = [r0; q0; zeros(3); zeros(3); 40.0]

# Run simulation
sol = Eclares.EclaresSim.simulate(x0, 0.0, 60.0)

using Pkg
Pkg.activate("/path/to/Eclares")
Pkg.instantiate()

using Eclares

x0 = [0; 0; 0; 1; 0; 0; 0; zeros(6); 40.0]
sol = Eclares.EclaresSim.simulate(x0, 0.0, 60.0)

The expt_32_2019.nc4 file (HYCOM Gulf of Mexico salinity data) is required for oceanographic clarity distributions. Place it in:

• data/expt_32_2019.nc4, or
• The project root directory

See LICENSE for details.

The paper has been published in IEEE ICRA 2024. If you use this code, please cite:

• Eclares: Energy-Aware Clarity-Driven Ergodic Search
  Kaleb Ben Naveed, Devansh Agrawal, Christopher Vermillion, Dimitra Panagou, 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 14326-14332, 2024.

@INPROCEEDINGS{10611286,
  author={Naveed, Kaleb Ben and Agrawal, Devansh and Vermillion, Christopher and Panagou, Dimitra},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  title={Eclares: Energy-Aware Clarity-Driven Ergodic Search},
  year={2024},
  pages={14326-14332},
  doi={10.1109/ICRA57147.2024.10611286}
}