Manipulator Motion Planning With Parallel Global and Local Planners
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Solution Overview
Problem
Existing collision-free motion planning methods for robots struggle with adapting to changing environments, particularly in dynamic settings, as they often require frequent stops and speed reductions due to the inability to continuously account for robot dynamics and moving obstacles, leading to inefficient and unnatural movements.
Innovation Solution
A method combining continuous global and local planners, where the global planner repeatedly determines a target path independently and the local planner adjusts movements in real-time to avoid collisions, allowing for seamless adaptation to changing conditions without stopping or reducing speed, using deterministic and probabilistic approaches to optimize path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global path planning methods are used for collision-free motion planning, then a complete path from starting point to destination point can be determined, but the robot must frequently stop and reduce speed when adapting to moving obstacles
Solution Approach 1:
The motion planning system is segmented into two independent planners: a global planner that determines the overall target path from start to destination, and a local planner that continuously adjusts the path in real-time to avoid moving obstacles. This segmentation allows each planner to specialize - the global planner focuses on completeness while the local planner focuses on real-time adaptability, resolving the contradiction between reliable collision-free movement and movement efficiency
Solution Approach 2:
The global planner performs preliminary action by determining the complete target path in advance before the robot begins movement. This preliminary path planning allows the local planner to then focus solely on real-time adjustments without needing to recalculate the entire path, reducing computational overhead and allowing the robot to maintain higher speeds while still adapting to moving obstacles
2Adaptability or versatility
If kinodynamic motion planning with probabilistic methods is used, then robot dynamics can be considered in motion planning, but the computing time becomes unpredictable and trajectories are not deterministic
Solution Approach 1:
The system segments the planning task so that the global planner uses deterministic methods for overall path determination while the local planner handles real-time dynamics adaptation. This segmentation allows deterministic computation for the majority of the planning process, with only localized probabilistic adjustments when needed, thereby reducing overall computing time while maintaining adaptability to dynamics
Solution Approach 2:
Probabilistic kinodynamic considerations are applied locally rather than globally - the local planner applies probabilistic methods only when and where dynamics adaptation is needed, rather than applying probabilistic computation to the entire trajectory. This local application of probabilistic methods reduces overall computing time while still providing the necessary adaptability to robot dynamics in changing environments
3Speed
If local reactive methods are used for motion planning, then quick reaction to moving obstacles is possible, but the robot can get stuck in local minima and cannot find optimal global paths
Solution Approach 1:
The system merges global path planning with local reactive planning into a unified motion planning framework. The global planner provides the overall target path that guides the robot toward the destination, while the local planner provides quick reactive adjustments to avoid moving obstacles. The merging of these two approaches allows the robot to benefit from both the global perspective for optimal path finding and the local reactivity for quick obstacle avoidance, eliminating the limitation of getting stuck in local minima
Solution Approach 2:
The local planner continuously monitors the robot's position and environmental changes, providing feedback to adjust the path in real-time. This feedback mechanism allows the system to react quickly to moving obstacles while the global planner periodically updates the target path to ensure the robot remains on an optimal global trajectory, combining the advantages of both global and local planning approaches
Data Source
AI summary
A method for collision-free motion planning of a first manipulator of a robotic control apparatus from a starting point to a destination point. The method includes repeatedly determining a target path of the first manipulator to the destination point with a global planner, continuously determining a movement of the first manipulator with a local planner based on a current target path of the global planner, and performing the movement by the first manipulator in parallel with the determination by the global planner and the local planner.