ARO Robot Controller
1. First-Order Error Dynamics (PD control)1.1. Demo Case Study: Velocity Control of a Mass-Damper System
Super Domain: Control Systems, Dynamics
Type of Method: Error Dynamics Analysis
1.2. Problem Definition with Variables Notations
Objective: Analyze the error dynamics of a system to predict how the system responds to a change from a desired state, focusing on velocity control.
Variables:
$\theta_e$: Error in position or angle.
$\dot{\theta}_e$: Error in velocity.
$m$: Mass of the object.
$b$ ...
ARO Robot Dynamics
1. Spring-Damper System Analysis1.1. Demo Case Study: Vehicle Suspension System Design
Super Domain: Mechanical Engineering and Dynamics
Type of Method: Analytical Modeling
1.2. Problem Definition and Variables
Objective: To design a vehicle suspension system that maximizes comfort by minimizing the impact of road irregularities on the vehicle’s passengers.
Variables:
$x(t)$: Displacement of the vehicle body or suspension component at time $t$.
$m$: Mass of the vehicle or the portion of the ve ...
ARO Robot Motion Planning
1. Motion Planning in Robotics1.1. Demo Case Study: Navigating an Autonomous Rover on Mars
Super Domain: Motion Planning
Type of Method: Pathfinding in Complex Environments
1.2. Problem Definition and Variables
Objective: Plan collision-free motions for a rover from a start to a goal position among a set of obstacles on a Martian landscape.
Variables:
Environmental model, including terrain map and obstacle positions.
Rover’s capabilities, including its movement constraints and geometry.
1.3. ...
ARO Robot Kinematics
1. Kinematic Tree, Kinematic Chain, and Forward Geometry and Backward Geometry in Robotics1.1. Demo Case Study: Robotic Arm for Assembly Line Tasks
Super Domain: Kinematics in Robotics
Type of Method: Kinematic Analysis and Control
1.2. Kinematic Tree and Kinematic Chain
Objective: To model the structure and movement of a robotic arm.
Kinematic Tree:
Represents the hierarchical structure of a robot.
Includes all possible movements and connections between different components.
Kinematic Chain: ...
Coding Note
apt install libopenblas-dev
bash setup.sh cue server
Checklist and Plan
Notice
Use CUE as baseline.
Ideas
Use information theory to explain the process.
Plan
Use uncertainty estimation method in CUE.
Evaluate CUE on semantic kitti (outdoor), or scannet v2 (indoor).
Extract uncertainty estimation method of CUE.
Achieve socket communication for model inference for CUE model.
Socket communication.
Local small model training, calculate uncertainty information.
Cloud large model training, with global feature map and compressed point cloud input.
Deploy to e ...
Paper Reading List
Helpful Materials: GitHub repo
https://github.com/ramdrop/cue
https://github.com/NVIDIA/MinkowskiEngine
【思考】创新点子
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【思考】其他思考
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【思考】核心思考
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SP Module 10 Connected Speech & HMM Training
From subword units to n-grams: hierarchy of modelsDefining a hierarchy of models: we can compile different HMMs to create models of utterances
We can do some pruning, remove some tokens while proceeding, reduce computation cost (Maybe Heuristic is also can be helpful in such case.)
Conditional independence and the forward algorithmWe use the Markov property of HMMs (i.e. conditional independence assumptions) to make computing probabilities of observation sequences easier
HMM tra ...
SP Module 9 the Hidden Markov Model
Hidden Markov Models for ASRIntro to Hidden Markov Models, comparison to DTW.
dynamic time warping (DTW)
Hidden state sequences and alignmentHidden state sequence, Trellis (i.e. lattice) representation of an HMM, aligning observations to states
grid and lattice
The Viterbi algorithm and token passingThe Viterbi algorithm can be computed on different data structures. The token passing version turns out to be very convenient for ASR.Tthese two different implementations are simply two different w ...
SP Module 8 Feature Engineering
Gaussian distributions in modelsClassification model
If features are highly correlate with each other, we can solve this correlation by rotating the axis, by PCA.
Gaussian distribution of classification result of feature vector
Decision boundary
Cepstral Analysis, Mel-FilterbanksWe now start thinking about what a good representation of the acoustic signal should be, motivating the use Mel-Frequency Cepstral Coefficients (MFCCs).
Since the feature in a feature vector is correlated, if we want to ...
MOB LEC12 Motion Planning
Mission, scenarios and behaviorsTask: Navigate the robot itself from the current position to final destination on the map, without human intervention.
Goal: find most efficient path (trajectory) in terms of time or distance travelled.
Example Common Behavior Sets
Speed tracking
Deceleration to stop
Stay stopped
Yield
Emergency stop
Motion planning constraints
Read more about vehicle dynamic.
Objective functions in planning
Supplementary Readings
Motion Planning For Au ...