Robot Motion Planning With Equipotential Obstacle Avoidance
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Solution Overview
Problem
Existing motion planning methods for robots are computationally expensive and may not generate optimal paths that avoid obstacles effectively, leading to potential collisions.
Innovation Solution
The method involves creating a bounded virtual space with scaled obstacles and applying electrostatic principles to determine equipotential curves, which are used to plan a motion path that avoids obstacles by selecting a path along a curve with a different potential value, ensuring the robot navigates safely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sampling-based methods such as RRT are used to determine motion path, then the robot can navigate through complex environments with obstacles, but the computational cost becomes excessively high
Solution Approach 1:
The patent replaces traditional mechanical search-based motion planning methods (like RRT and A*) with an electrostatic field-based approach. By modeling obstacles as charged objects and computing equipotential curves, the system achieves collision-free path planning without the computational expense of iterative sampling and local search, directly resolving the contradiction between path safety and computational time.
2Adaptability or versatility
If heuristic and probabilistic methods are used for motion planning, then the robot can find paths in complex environments, but the generated paths may not be optimal
Solution Approach 1:
The patent substitutes heuristic and probabilistic algorithms with deterministic electrostatic field theory. By solving for equipotential curves in an electrostatic field model where obstacles are charged objects, the system generates optimal collision-free paths that are both adaptable to complex environments and mathematically guaranteed to be optimal, eliminating the trade-off between adaptability and precision.
3Ease of operation
If local search methods are used to determine motion path, then the robot can avoid immediate obstacles, but the methods fail to consider global information and may陷入 local traps
Solution Approach 1:
The patent replaces local search methods with a global electrostatic field approach. By modeling the entire environment as an electrostatic field with obstacles as charged objects and computing equipotential curves that naturally avoid all obstacles, the system achieves both easy obstacle avoidance and global path optimality simultaneously, as the equipotential curves inherently consider all obstacles in the environment rather than just local ones.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the determination of a feasible and safe motion path that avoids obstacles, leveraging global information and eliminating local traps, while also enabling optimization and smoothing of the path based on various criteria.
Implementation Method 1
Some embodiments are based on the recognition that electrostatics can used to describe the obstacles, and the boundaries. For example, the obstacles, and the boundaries can be treated as metallic surfaces in the electrostatics problem. Each metallic surface has a constant potential value.
Implementation Method 2
a bounded virtual space corresponding to the bounded space is formed by scaling the floorplan of the bounded space with the obstacles and applying opposite charges to at least two opposite bounds (e.g., the boundaries) of the bounded virtual space while treating the scaled obstacles as the metallic surfaces with the constant potential value. Further, an electric potential in the bounded virtual space is solved for to produce multiple equipotential curves within the bounded virtual space.
Data Source
AI summary
The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.