Autonomous Fruit Picking
1.0.0

Overview

  • Digital Twin Safety for HRI

Test Environments

  • Comparative Analysis
  • Isaac Simulation
  • Gazebo Classic
  • Gazebo Fortress

Safety Protocols

  • Robotic Protocols
  • Pertinent Standard: ISO 13482
  • Pertinent Standard: IEEE P7009

Data Protection Policies

  • Retail Robot Operations
  • Privacy Protection

Person Detection

  • ZED 2i Overview
  • ZED 2i Relevancy
  • ZED 2i Compatability

ROS2 Navigation

  • Nav2 Local & Global Planners
  • Nav2 Key Parameters
  • Nav2 Experimental Parameters

Experimentation

  • NavFn Planner + DWB Controller
  • NavFn Planner + MPPI Controller
  • NavFn Planner + RPP Controller
  • Theta* Planner + RPP Controller
  • SMAC Planner + MPPI Controller

Results

  • Nav2 Performance for Safety Scenarios
  • Nav2 Suitability for Different Robots
  • Nav2 Controller Suitability for Different Robots
  • Conclusion
Autonomous Fruit Picking
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  • Nav2 Performance for Safety Scenarios

Nav2 Performance for Safety Scenarios¶

Performance Summary¶

Global Planner + Local Controller

Straight-Line Movement

Static Obstacles

Dynamic Obstacles

Obstacle Clearance

Dynamic Obstacle Handling

NavFn + DWB

✅

✅

❌

Good

Moderate responsiveness

NavFn + MPPI

✅

✅

✅

Excellent

Highly responsive

NavFn + RPP

✅

❌

❌

Average (struggles in gaps)

Slow

Theta_* + RPP

✅

✅

❌

Excellent

Slow

Smac Planner Hybrid + MPPI

✅

✅

❌

Bad

Moderate responsiveness

Note

  • ✅: Suitable

  • ❌: Not Suitable

  • Obstacle Clearance: Describes the ability to navigate close proximities without collisions (e.g., Excellent, Good, Bad).

  • Dynamic Obstacle Handling: Describes the responsiveness to moving obstacles (e.g., Highly responsive, Moderate, Slow).

Important

Controllers perform differently based on drive types (e.g., differential, Ackermann), impacting navigation results. Proper pairing and tuning are crucial for optimal performance.

Note

Different drives may give varying results.

Nav2 Suitability for Different Robots¶

Planner Suitability for Robot Types¶

Planner Name

Circular Differential

Circular Omnidirectional

Non-Circular Ackermann

Non-Circular Legged

Non-Circular Differential/Omnidirectional

Arbitrary

NavFn Planner

✅

✅

❌

❌

❌

❌

Smac Planner 2D

✅

✅

❌

❌

❌

❌

Theta* Planner

✅

✅

❌

❌

❌

❌

Smac Hybrid-A* Planner

✅

✅

✅

✅

✅

❌

Smac Lattice Planner

❌

❌

✅

✅

✅

✅

Note

  • ✅: Suitable for this type of robot.

  • ❌: Not suitable for this type of robot.

This table provides a clear summary of which planner is best suited for each type of robot, helping users make informed decisions based on their robot’s design and operational needs.

Note

Not all controllers are suitable for all robot tasks; it depends on the type of robot as well as the task being performed.

Nav2 Controller Suitability for Different Robots¶

Controller Suitability for Robot Types and Tasks¶

Controller Name

Differential

Omnidirectional

Ackermann

Legged

Primary Task

DWB Controller

✅

✅

❌

❌

Dynamic obstacle avoidance

MPPI Controller

✅

✅

✅

✅

Dynamic obstacle avoidance

RPP Controller

✅

❌

✅

✅

Exact path following

Rotation Shim

✅

✅

❌

❌

Rotate to rough heading

VP Controller

✅

❌

✅

✅

High-speed path tracking

Note

  • ✅: Suitable for this type of robot.

  • ❌: Not suitable for this type of robot.

This table offers a clear summary of controller suitability based on robot type and primary tasks, helping users make informed decisions about their controller configuration.

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