Time-Optimal Robot Pick-and-Place Planning

ISEF Category: Robotics and Intelligent Machines

Subcategory: Robot Kinematics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A robot arm can lose seconds on every move if its motion plan is sloppy. That sounds small, but in a factory, those seconds stack up fast. You can test whether a smarter planner beats a smooth-looking spline by a real margin. This project turns robot motion into a measurable race.

What Is It?

This project asks a simple question with a hard answer, how fast can a robot arm move between tasks without breaking its own limits? You start with a low-cost arm and collect data on how much torque and speed it can actually handle. Then you build a planner that chooses a path and speed profile that stay inside those limits.

Think of it like planning a road trip. A cubic spline gives you a nice, smooth map of the route, but it does not always pick the fastest legal drive. Trajectory optimization looks at the whole trip, then searches for the quickest motion that still respects the robot's joints, motors, and safety limits. Drake and CasADi are software tools that help solve that kind of problem.

Your main comparison is cycle time. That means how long the arm takes to pick up, move, and place an object. You measure whether the optimized plan actually beats a standard cubic-spline joint path, and you can also check whether the faster plan stays accurate and repeatable.

Why This Is a Good Topic

This makes a strong science fair topic because you can measure a clear number, cycle time, and compare two real planning methods under the same hardware limits. The project connects to warehouse robots, factory automation, and low-cost assistive arms. You can learn robot modeling, constraint handling, data collection, and performance testing, all from one build.

Research Questions

• How does a trajectory-optimized plan change cycle time compared with cubic-spline joint interpolation?
• What is the effect of tighter torque bounds on the fastest feasible pick-and-place motion?
• Does data-driven estimation of joint velocity limits improve plan reliability compared with using vendor specs alone?
• To what extent does payload mass change the cycle-time gain from trajectory optimization?
• Which joint limit, torque or velocity, most often becomes the bottleneck for time-optimal motion?
• How does path length in joint space affect the benefit of optimization over spline-based motion?

Basic Materials

• Low-cost 4- to 6-degree-of-freedom robot arm with controller documentation.
• Laptop with enough memory to run optimization software.
• USB cable or wireless link for sending commands to the arm.
• Small standardized objects for pick-and-place trials.
• Digital kitchen scale with 0.1 g accuracy for payload checks.
• Smartphone or camera for motion video review.
• Ruler or tape measure for workspace setup.
• Safety glasses for anyone near the arm.

Advanced Materials

• Robot arm with joint-state access and torque or current feedback.
• External force or torque sensor, if available, for validation runs.
• High-speed camera or motion capture system for timing and path verification.
• Encoder data logs from the arm controller.
• Calibrated payload set for load testing.
• Computer capable of running Drake or CasADi.
• Optional motion capture markers or fiducials for tracking end-effector motion.
• Emergency stop hardware and guarded test area.

Software & Tools

• Drake: Models robot motion and solves trajectory optimization problems for constrained planning.
• CasADi: Solves nonlinear optimization problems and helps you search for time-optimal trajectories.
• Python: Organizes data, runs analysis, and compares planning methods.
• ROS 2: Sends motion commands and records robot telemetry if your arm supports it.
• Excel: Helps you log cycle times and make quick comparison charts.

Experiment Steps

1. Define the exact pick-and-place task, the robot arm, and the performance metric you will compare.
2. Measure or estimate joint torque and velocity limits from real robot data, not just from the spec sheet.
3. Build a baseline motion plan with cubic-spline joint interpolation and log its cycle time.
4. Formulate the optimization problem with the same start, goal, and physical constraints.
5. Run both planners on the same task set, then compare cycle time, success rate, and motion smoothness.
6. Check whether the faster plan still lands the end effector where you expect and stays inside the identified limits.

Common Pitfalls

• Using vendor limits without checking the real arm data, which can make the optimized plan look faster than it really is.
• Comparing two planners with different start poses or object locations, which makes the cycle-time result unfair.
• Ignoring motor saturation during acceleration, which can cause the arm to miss waypoints or stall.
• Measuring timing from video taken at different angles or frame rates, which adds noise to the cycle-time data.
• Letting the optimization solver return a mathematically valid path that the physical robot cannot follow smoothly.

What Makes This Competitive

A stronger version of this project does more than prove optimization can be faster. You can add better motor-limit identification, test multiple payloads, or compare several objective functions, not just one spline baseline. You can also report success rate, tracking error, and energy use alongside cycle time. That turns a simple speed test into a real systems study.

Project Variations

• Test the same planner on a different low-cost arm and compare how hardware quality changes the gain from optimization.
• Swap pick-and-place cubes for uneven objects or fragile items and see how constraints affect the best motion plan.
• Compare time-optimal planning with energy-minimizing planning to see whether the fastest motion is also the most efficient.

Learn More

• Drake documentation: Search the official Drake docs for examples on robot trajectory optimization and constrained motion planning.
• CasADi documentation: Read the official CasADi docs for nonlinear optimization and optimal control examples.
• MIT OpenCourseWare, Underactuated Robotics: Find lecture notes and assignments on robot dynamics, control, and optimization.
• Modern Robotics by Northwestern University: Use the free online textbook and video lectures to review kinematics and motion planning.
• IEEE Xplore and PubMed search, when relevant: Look for papers on robot trajectory optimization, time-optimal control, and low-cost manipulator benchmarking.
• NASA Technical Reports Server: Search for robotics motion-planning reports that discuss constrained trajectory design.