Simulation · Builder
Unified Search.
A compact whitepaper plus a runnable simulator that pits five autonomous-search strategies (random walk, Levy flight, gradient ascent, surge-cast, swarm stigmergy) against each other in a noisy, sparse plume environment, with an interactive in-browser Digital Lab.
- Role
- Builder
- When
- 2026
- Stack
- Python, NumPy, JavaScript, Matplotlib
- Scale
- 5 strategies head-to-head
5 strategies · turbulent plume5 strategieshead-to-head
Turbulent plumeenvironment
2 runtimesPython + browser
Leaderboardrepeatable trials
The problem
How an agent should search for a sparse target in a noisy field, a foraging animal, a robot sniffing for a gas leak, a swarm sweeping an area, depends heavily on the strategy and the environment, and the trade-offs are easy to argue about and hard to see. The goal was a small, controlled sandbox where several search strategies run on the same environment so their behavior can actually be compared rather than asserted.
What it does
- A simulator that implements five search strategies, random walk, Levy flight, gradient ascent, surge-cast (chemotaxis-style), and swarm stigmergy (pheromone trails), as interchangeable behaviors over one agent model.
- A plume environment with static or turbulent modes: in turbulent mode the signal comes from drifting Gaussian puffs, with configurable sensing noise, so strategies are tested under realistic sparsity and uncertainty.
- A leaderboard runner that scores strategies across repeated trials and plume conditions and writes out plots, plus a short whitepaper that frames the strategies and cites the literature.
- Two runtimes from the same idea: a Python CLI simulator (NumPy + plots) for repeatable experiments, and a JavaScript reimplementation that runs the Digital Lab live in the browser.
Impact
- Makes a fuzzy argument concrete: the same five strategies run on identical plume conditions, so their differences are observed in a leaderboard rather than claimed.
- The in-browser Digital Lab lets anyone change the conditions and watch the strategies search, no setup, the simulation runs client-side.
- Deliberately compact and deterministic enough to repeat, so a result can be reproduced rather than admired once.