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Recruiter brief
Robotics & autonomyDecember 2025

Robotics & Autonomous Driving

Multi-robot navigation

A potential-field controller evaluated over every goal assignment for five robots in a fixed obstacle-filled circular workspace.

PythonControlSimulationPotential fieldsNumPy
Study / experimentRank 03 of 5 in this field ↗
Why it sits here. Best finite quantitative outcome evidence in the course set, though a smaller and more constrained project than the driving systems.
01 / An open road. A closed loop.STUDY IN SPACE

THE QUESTION

How do attraction, repulsion, and circulation cooperate without trapping the robots?

Navigate five robots around static obstacles, one another, and a bounded environment across every goal-assignment permutation in a fixed study.

SAVED SIMULATION RESULTS
120/120assignments completed
Fixed test suitenot a convergence proof
Source: saved summary.csv

01 / IMPLEMENTATION & CONTRIBUTION

What the work involves

Donald developed and tuned the potential-field controller, parameter sweeps, and trajectory visualizations for an ECE470 project using a supplied simulator.

Technical depth

Too much goal attraction can overpower repulsion. Local minima and orbiting can prevent completion.

Data and assumptions

The saved results table contains 120 assignments; all 120 report successful completion. Mean completion was independently recomputed as 8,434.06 simulation steps.

Robots and obstacles follow the course simulator’s geometry and dynamics. This is a fixed benchmark, not a general proof.

The project family

uiuc/ECE_470_FA25_Project-Code

02 / THE EXPERIMENTAL RECORD

What the experiment taught

The report documents orbiting, local minima, and collisions during parameter exploration.

What changed

The controller combines nonlinear attraction, obstacle circulation, robot repulsion, and conditional boundary repulsion.

03 / RESULTS

What came out of it

All 120 goal assignments completed in the saved five-robot simulation suite, averaging 8,434 simulation steps. The result applies to that fixed simulator and obstacle configuration.

The lesson in the work

Evaluate the assignments where robots interfere, not only visually clean trajectories.

04 / SUPPORTING EVIDENCE

Follow the source

Implementation notes, project records, and supporting artifacts.

Source context & project scope

Fixed course simulation suite, not a general convergence guarantee or physical-robot result.

Prose report min/max differ from the saved CSV; use only checked 120/120 and mean.

Excessive attraction caused collisions; nonlinear attraction addressed orbiting/local minima.

The controller remains reactive and does not anticipate future conflicts.

Donald-attributed ECE470 course project, using supplied simulator infrastructure.

Goal attraction, obstacle repulsion and circulation, robot repulsion, and conditional boundary repulsion form the navigation controller.

SOURCE · 2025-12-01

120 rows. 120 success=True. Mean completion: 8,434.06 simulation steps.

DATA · 2026-09-17

The report records orbiting, local minima, and collisions when goal gains overpower repulsion.

REPORT · 2025-12

Donald-named report, method, parameter sensitivity, failures and future improvements.

SOURCE · 2026-09-17

Implemented controller; Donald commits 5f01b8a and 21eab11, December 2025.

SOURCE · 2026-09-17

120 recorded successes and independently recomputed mean.

SOURCE · 2026-09-17

Failure-case visualization tooling.

SOURCE · 2026-09-17
CONTINUE IN ROBOTICS & AUTONOMOUS DRIVING

F1Tenth lane following