- Design and Development of

- Omegabot: Inchworm inspired

- Large deformable morphing

- Wearable robotic hand
- Hands on surgical robot:
   Shared control system
- Situation Understanding for
   Smart Devices

- Wireless Camera Sensor
   Networks Technology

- Mobile Sensor Networks:
   Algorithms and Applications
- Whole-Body Control Framework
    for Humanoid Robot

- Walking Pattern Generation for
   Humanoid Robot

- Robot Hand Control
- Quadruped Robot Control with
   Whole-Body Control Framework

- Human Gait Analysis using
   3D Motion Capture
- Coordination of multiple robots
- Flocking and consensus
- Vision-based guidance and

- Online collision avoidance for
   mobile robots

- Wireless sensor network
- Aerial Manipulation
- Haptics/VR
- Autonomous Mobility
- Telerobotics
- Mechanics/Control
- Industrial Control
- Mobile Manipulation
- Simultaneous Visual and
   Inertia Calibration

- Mechanics of Closed Chains
- Motion Optimization via
   Nonlinear Dimension

- Probabilistic Optimal Planning
   Algorithm for Minimum
   upstream Motions

- Automated Plug-In Recharging
   System for Hybrid Electric
Probabilistic Optimal Planning Algorithm for Minimum Upstream Motions
The rapidly-exploring random tree and its many variants have become an essential component of randomized motion planning algorithm. This efficiency is achieved in part by settling for any feasible path that is obtained, regardless of the final quality. In this research, a new sampling-based optimal motion planning algorithm, which operates in the vector field, is proposed.
First, a new criterion for path quality is defined. We called it the upstream criterion, that measures the extent to which the path goes upstream against the optimal vector field. The criterion, expressed as a path integral, is invariant with respect to the parametrization of the path (e.g., arclength, time), and also defined continuously with respect to the path as long as the vector field is continuous.
A new motion planning algorithm retains the efficiency characteristic of RRT algorithms, and at the same time randomly samples nodes such that, with only minimal additional overhead, attempts to minimize the upstream criterion. The main distinguish feature of it is that trees tend to extend toward vector field direction. By using inversion method, it can find the optimal path in a short time.

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