🎓 Lesson 15
D5
Latency-Aware Replanning for Vision-Directed Pick-and-Place
Latency-aware replanning means quickly adjusting a robot’s pick-and-place motion in real time when its camera sees something unexpected—like a shifted rock pile—so it still grabs the target safely and accurately.
🎯 Learning Objectives
- ✓ Analyze end-to-end system latency components in a vision-guided pick-and-place pipeline
- ✓ Design a latency-aware replanning trigger condition based on pose uncertainty and motion time budget
- ✓ Apply time-elastic trajectory scaling to ensure collision-free execution within remaining latency-bound time windows
- ✓ Explain how camera shutter mode (global vs. rolling) affects pose estimation delay and replanning frequency
📖 Why This Matters
In open-pit mining operations, robotic excavators and drill-assist manipulators increasingly rely on onboard cameras to locate fragmented ore piles or drill hole markers—but lighting changes, dust, vibration, and motion blur cause intermittent visual lag. If a robot plans a reach based on an image captured 120 ms ago, and the pile shifts due to blast-induced ground settlement, executing the original plan risks collision, missed grasp, or structural damage. Latency-aware replanning isn’t just about speed—it’s about *timing integrity*: ensuring every action is grounded in temporally valid perception. This capability directly impacts equipment uptime, operator safety, and autonomous cycle time reliability.
📘 Core Principles
Latency-aware replanning rests on three interlocking principles: (1) *Latency decomposition*—breaking total system delay into identifiable stages (exposure → transmission → inference → path search → servo update); (2) *Temporal feasibility*—verifying that any new trajectory can be executed before the next perceptual update arrives or before dynamic obstacles (e.g., falling debris) invalidate assumptions; and (3) *Uncertainty-aware triggering*—replanning only when pose estimation variance exceeds a threshold tied to motion tolerance, avoiding unnecessary computation. Advanced implementations use predictive state estimation (e.g., Kalman-filtered object pose) and time-parameterized RRT* variants that embed latency budgets as hard constraints—not soft penalties—into the optimization objective.
📐 Maximum Allowable Replanning Interval
This formula determines the longest permissible time between successive replanning decisions, given sensor latency, controller update rate, and motion time-to-goal. Exceeding it risks executing outdated plans in dynamic environments.
Maximum Allowable Replanning Interval (T_max)
T_max = T_motion − (T_sensor + T_infer + T_plan + T_control)Longest time after motion initiation within which a new plan must be triggered and completed to remain temporally valid.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| T_max | Maximum allowable replanning interval | ms | Latest time (relative to motion start) to initiate replanning |
| T_motion | Total motion duration | ms | Time required to execute current trajectory to goal |
| T_sensor | Sensor acquisition latency | ms | Time from scene change to digital image availability (includes exposure, readout, transmission) |
| T_infer | Perception inference time | ms | Time for object detection, pose estimation, and uncertainty quantification |
| T_plan | Planning computation time | ms | Time to generate new collision-free, dynamically feasible trajectory |
| T_control | First control loop latency | ms | Time from plan output to first actuator command update |
Typical Ranges:
Industrial robotic manipulator (GPU-accelerated): 35 – 65 ms
Embedded mining robot (CPU-only inference): 80 – 150 ms
💡 Worked Example
Problem: A mining manipulator uses a global-shutter camera (exposure = 5 ms), processes images via embedded YOLOv5n (inference = 22 ms), computes a time-optimal cubic spline trajectory (planning = 18 ms), and updates servo commands at 200 Hz (control period = 5 ms). The arm’s current motion to target takes 420 ms. What is T_max?
1.
Step 1: Sum fixed latency components: 5 ms (exposure) + 22 ms (inference) + 18 ms (planning) + 5 ms (first control update) = 50 ms
2.
Step 2: Subtract from motion duration: 420 ms − 50 ms = 370 ms — this is the latest moment a new plan can begin computing and still finish before motion ends
3.
Step 3: Since planning takes 18 ms, the latest *trigger time* for replanning is 370 ms − 18 ms = 352 ms after initial plan start
Answer:
T_max = 352 ms. This means the system must re-evaluate vision input and decide whether to replan no later than 352 ms into the current motion cycle—or risk missing the window for safe intervention.
🏗️ Real-World Application
At Rio Tinto’s Gudai-Darri mine (Western Australia), autonomous excavator 'Project K' uses latency-aware replanning to handle post-blast muck pile displacement. After each blast, LiDAR+RGB fusion detects >5 cm pile shift within 80 ms of exposure. When pose uncertainty exceeds ±30 mm (calibrated to bucket tip tolerance), the system triggers a constrained CHOMP replan with 60 ms hard deadline—scaling velocity profiles to complete adjusted motion in ≤390 ms. Field data shows 99.2% grasp success vs. 83.7% with static planning, reducing manual intervention by 74% over 12-month deployment.
🔧 Interactive Calculator
🔧 Open Robot Motion Planning & Trajectory Generation Calculator📋 Case Connection
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