Not every path is chosen for speed.
Not every movement is about arrival.
Some paths are taken because they reveal—
because every step gathers what the system didn’t yet know.
This is the quiet power of Informative Motion Planning:
a way of moving not just to get somewhere,
but to get smarter.
It is motion with a second purpose:
to observe,
to measure,
to reduce uncertainty
about the world, the mission, or the system itself.
Because in true autonomy, the path is not just a line—
it’s a strategy for insight.
What Is Informative Motion Planning?
Informative motion planning is the process of planning trajectories that maximize information gain,
rather than (or in addition to) minimizing time, distance, or energy.
The system moves not just to reach a goal,
but to learn the most valuable things along the way—
about the environment, a target, a map, or itself.
It’s used when:
– The system is uncertain about the world
– Data is sparse or noisy
– Observations are limited by perspective or range
– Planning needs to be active, not passive
At every step, the vehicle asks:
What can I learn from here?
And what could I learn if I moved there instead?
Core Concepts
- Belief Representation
– The system holds a probabilistic model of the unknown (e.g., a map, target location, or wind field) - Information Gain Metrics
– Uses entropy, mutual information, or variance reduction to quantify how much a new observation would improve knowledge - Sensor Modeling
– Considers the system’s field of view, resolution, occlusion, and noise characteristics - Coupled Planning and Sensing
– Plans movement and sensing together, ensuring each motion choice improves future decisions - Tradeoffs
– Balances informative value with cost: time, energy, risk, or mission constraints
Where It’s Used
– Autonomous exploration: UAVs mapping unknown environments with limited visibility
– Search and rescue: Prioritizing areas that maximize the chance of finding a target
– Environmental monitoring: Planning air or water sampling paths that best reduce uncertainty about pollution or temperature
– Active SLAM (Simultaneous Localization and Mapping): Moving to refine the map and improve the robot’s pose estimate
– Wildlife and asset tracking: Planning flight paths based on probabilistic models of target locations
In all of these, motion becomes a question,
and the answer is data.
Planning Methods
– Greedy or Myopic Planners
– Maximize information at the next step
– Fast and local, but not always globally optimal
– Sampling-Based Planners (e.g., RRT, PRM)*
– Explore motion spaces while considering information gain along each path
– Receding Horizon Planners
– Plan a few steps ahead, always re-optimizing with new data
– Information-Theoretic MPC
– Optimize over a prediction horizon to select actions that reduce long-term uncertainty
– Bayesian Optimization and Learning-Based Methods
– Adapt plans based on previous experience or model predictions
Why It Matters
Informative planning turns autonomy from reaction into inquiry.
It gives a machine the ability to move with curiosity—
to sense the value of its own perspective,
and to change that perspective for better understanding.
It makes autonomy more than motion.
It makes it awareness in motion.
Because sometimes, reaching the goal is not enough.
Sometimes, the mission is to reveal what the goal should be.
And to do that,
the system must learn—intelligently, intentionally, and in motion.