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GP-ME-1O87YCPMechanicalOpen for request

Robot-arm path-planning simulator

A robotics simulator for comparing collision-free manipulator path-planning methods under joint, workspace, obstacle, and motion constraints.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • NumPy
  • PyBullet
  • Docker

Project definition

Problem statement

A robot end-effector can reach a target through many joint configurations, but obstacles, self-collision, singularities, joint limits, velocity, acceleration, and clearance determine whether a path is usable.

The engineering problem is to implement and compare path planners fairly, convert a geometric path into a feasible trajectory, and verify every motion in simulation.

Project objectives

  • Model selected serial manipulators, joints, links, tools, obstacles, and task poses.
  • Implement forward kinematics, inverse kinematics, collision checking, and workspace validation.
  • Compare selected sampling-based and graph or optimisation-based planning methods.
  • Smooth and time-parameterise paths under joint velocity and acceleration limits.
  • Measure success, path length, clearance, computation time, smoothness, and repeatability.

System design

System modules

01

Robot and scene modeller

Loads a prepared robot, joint limits, collision geometry, tool frame, obstacles, and start and goal states.

02

Kinematics engine

Calculates forward and inverse kinematics, Jacobian indicators, reachability, and joint-limit checks.

03

Collision checker

Tests robot-obstacle and self-collision with configurable safety clearance.

04

Planner workbench

Runs selected RRT, RRT*, PRM, or trajectory methods using saved parameters and seeds.

05

Trajectory analyser

Smooths paths, applies time limits, rechecks collision, and compares motion and planning metrics.

Methodology

System workflow

  1. 01
    Load scenario

    The student selects a prepared robot, obstacles, tool, start state, and target pose.

  2. 02
    Check feasibility

    Reachability, inverse-kinematic solutions, collisions, and joint limits are validated.

  3. 03
    Run planners

    Selected methods run repeatedly with recorded seeds, budgets, and parameters.

  4. 04
    Build trajectory

    Successful paths are smoothed and time-parameterised under motion limits.

  5. 05
    Compare and replay

    The 3D simulator rechecks and displays paths, clearances, joint traces, and metrics.

Demonstration scenario

A six-axis arm must move a tool between two fixtures while avoiding a central obstacle. RRT and PRM run with repeated seeds, their paths are smoothed and timed, and the simulator compares success rate, clearance, path length, joint motion, and computation time.

Engineering

Technical architecture

Web application
Nuxt and Vue for task setup, 3D playback, joint charts, planner controls, and comparison.
Planning API
FastAPI for robot and scene data, planning jobs, paths, trajectories, metrics, and exports.
Simulation engine
PyBullet for robot models, kinematics support, collision queries, and verified trajectory playback.
Planning engine
Python and NumPy implementations or adapters for selected planners, smoothing, and time parameterisation.
Reproducibility
Versioned robot and scene models, fixed planner budgets, recorded seeds, and regression scenarios.

Testing

Evaluation

Evaluation measures

  • Planning success rate across prepared scenes and repeated seeds
  • Collision-free and joint-limit-valid trajectory rate
  • Path length, minimum clearance, smoothness, and execution time
  • Planning time and explored states under fixed budgets
  • Final pose position and orientation error
  • Robustness to obstacle, start-state, and target changes

System boundaries

  • The project operates in simulation and does not command a physical robot.
  • Collision geometry, actuator dynamics, flexibility, backlash, sensing, and controller behaviour are simplified.
  • A simulated collision-free path is not automatically safe on real equipment.
  • Physical use requires calibration, safety systems, risk assessment, and qualified supervision.

Included

  1. 01Robot, obstacle, and task configuration interface
  2. 02Kinematics, collision, planning, and trajectory modules
  3. 033D path, joint, clearance, time, and comparison visualisations
  4. 04Prepared robot models, planning scenarios, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, robot and planning diagrams, simulation results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-ME-1O87YCP
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.