Q: What does 2026 National Robotics Competition (NRC) cover?
A: A parent-student primer that links Singapore's longest-running school robotics contest to the very skills IP learners need in Math & Physics - vectors.
TL;DR The National Robotics Competition (NRC) packs design thinking, sensors and real-time maths into a six-month season. It rehearses the same modelling habits demanded by IP Maths (Σ,f=ma) and Paper 4 Physics practicals. Start prototype cycles by March, log PID gains like uncertainty tables, and reserve September for autonomous fine-tuning.
The NRC is Singapore's flagship robotics contest for schools, organised by Science Centre Singapore since 1999 and supported by the Ministry of Education and IMDA. Science Centre Singapore In the Regular Category, robots are LEGO‑based (SPIKE / EV3 / Robot Inventor). Refer to Science Centre's NRC page for the latest categories and hardware rules.
2025 season checkpoints
Milestone
Typical window
Why IP parents should note
Game reveal
Jan\space 1st week
Align project-based learning modules
Registration
Feb-Mar
Early bird slots fill within 2 weeks
Practice scrimmages
May-Jul
Coincide with WA 2 in many IP schools
Nationals (qualifiers & finals)
Late-Sep
Collide with IP End-of-Year/Promo
Flow of this guide: Sections 1-2 give context. Section 3 links each robot action to H2/IP Maths and Physics. Section 4 shows when to cover each topic across the season, followed by pitfalls and quick fixes.
2 Divisions & age-mapping
Division
School level
Robot kit
Game style
Primary
P3-P6
LEGO‑based (SPIKE/EV3/Robot Inventor)
Driver + autonomous skills
Secondary - Novice
Sec 1-2
LEGO‑based
Solo driver challenge
Secondary - Advanced
Sec 3-4 / IP Y3-4
LEGO‑based
Alliances + autonomous
Tertiary
JC1-2 / IP Y5-6 / Poly
LEGO‑based / per category rules
Full eliminations
Source: See NRC official page for current rulebooks and category specifics.
Next: with divisions in mind, let's see how every run is a short Physics/Maths lesson.
3 Math-Physics hooks inside every run
The ideas below are H2-ready and map cleanly to what students meet in class and in Paper 4.
3.1 Vectors & kinematics (core H2/IP)
What is happening: Desired robot motion is a vector. Resolve a target speed u at heading θ into perpendicular components: ux=ucosθ,uy=usinθ. These components then map to wheel speeds.
Differential drive (2 wheels or left/right treads) Command linear speed v and angular speed ω about the vertical axis. With track width b:
vL=v−2bω,vR=v+2bω,ωL,R=rvL,R
Holonomic/X-drive (conceptual) Compute (ux,uy) first. A fixed 2×2 (or 4×2) mapping turns (ux,uy) and a desired ω into individual wheel speeds. Think resolve → map → check limits.
Quick checks
Straight line: set ω=0, check vL=vR.
On-the-spot turn: set v=0, check vL=−vR.
Convert linear to angular via ω=v/r.
3.2 Forces, traction & f=ma (core H2)
What sets acceleration: Net drive force must satisfy F=ma. The maximum usable drive force is friction-limited: Ff≤μN=μmg.
Implications for teams
If the plan needs acceleration a, you need F=ma≤μmg → a≤μg.
When wheels slip, adding gear reduction or more power will not help; improve μ (tread, weight on drive wheels) or reduce a.
Weight shifts (e.g., lifting arms) change N on the drive wheels; expect traction changes mid-run.
Mini example m=5kg, μ=0.6 → amax≈0.6×9.81≈5.9ms−2. Planning a=1.0ms−2 is feasible; a=7 is not.
3.3 Energy, power & battery sag (core H2)
Link electric to mechanical Pe=IV and Pm=τω. As current rises under load, terminal voltage drops due to internal resistance Ri: V=E−IRi.
Two-point test for (R_i) Measure two steady states (I1,V1) and (I2,V2). Plot V vs I; the straight-line gradient is −Ri, intercept is E. Use it to predict late-match behavior: higher I → larger ΔV → lower ω and weaker lifts.
What to expect in matches
High-current events (pushes, lifts) drop V momentarily.
Top speed and lift speed fall slightly near the end of a run.
Design so your robot operates away from stall torque and near the motor's efficient region.
3.4 Rotational motion & gear ratios (core H2/IP)
Speed-torque trade With gear ratio G=ωwheelωmotor: ωwheel=Gωmotor,τwheel≈Gτmotor (ignoring losses). Linear speed from wheel radius r: v=ωwheelr.
Pick a sensible (G) (habit)
Estimate required v (field crossing, time limit).
Choose motor cartridge and wheel radius r.
Compute ω and v.
Check τ margin for starts/turns. If wheels slip or stall, increase G (more torque) or reduce r.
Back-of-envelope If n is wheel RPM, v=(602πn)r. For n=200RPM and r=0.05m, v≈1.05ms−1.
3.5 Measurement, sensors & uncertainty (core H2 Paper 4)
Perform 3-run average; subtract mean bias (Paper 4 ACE skill)
6 Call-to-action
Parents: book a 60 min Robotics-Math-Physics fusion clinic before March holidays - we align vectors, forces and gearing to drivetrain choices. Students: export your run logs to Sheets tonight; use =LINEST() to get slope and ± uncertainty like a Paper 4 pro. Optional: apply it to your PID data to justify a Kd tweak.
