Robot Path Planning With CNN-DWA Crowd Avoidance
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Solution Overview
Problem
Existing path determination devices for autonomous mobile robots often fail to prevent interference with traffic participants, especially in crowded environments, leading to frequent robot stoppages and reduced merchantability.
Innovation Solution
A path determination device that uses a prediction algorithm to determine a predicted path for the robot, avoiding interference with traffic participants, and a control algorithm to maximize the objective function including distance to the closest traffic participant and robot velocity, ensuring a balanced and optimal path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a possible interference time and virtual obstacle region are used to determine the robot's path, then the robot can avoid interference with traffic participants in theory, but the robot frequently stops when actual traffic participants move in unpredictable loci, reducing merchantability
Solution Approach 1:
The patent applies dynamics by making the path determination system adaptive and responsive to real-time changes. The robot continuously updates its path based on current traffic participant positions and predicted trajectories, rather than relying on static virtual obstacle regions. This dynamic approach allows the robot to smoothly navigate around moving pedestrians without frequent stoppages, resolving the contradiction between path accuracy and movement continuity.
Solution Approach 2:
The patent changes key parameters from fixed virtual obstacle boundaries to dynamic distance and velocity variables. By using an objective function that incorporates real-time distance to traffic participants and robot velocity, the system continuously optimizes the path. This parameter transformation enables smooth navigation through crowded environments while maintaining safe distances, eliminating frequent stoppages caused by rigid virtual obstacle constraints.
2Reliability
If the robot maintains a large distance from traffic participants to avoid interference, then safety is improved, but the robot's movement freedom and speed are reduced
Solution Approach 1:
The system dynamically adjusts the distance from traffic participants based on their motion state. When pedestrians are stationary or moving predictably, the robot can maintain larger safety distances. When pedestrians are moving away or their trajectories are clear, the robot increases its speed and reduces distance. This dynamic distance adjustment resolves the contradiction between safety and speed.
Solution Approach 2:
The patent transforms the fixed safety distance constraint into a dynamic parameter optimized by the objective function. The distance to traffic participants becomes a variable that changes based on real-time conditions, allowing the robot to maximize velocity while maintaining appropriate safety margins. This resolves the contradiction by making distance adaptive rather than fixed.
3Measurement precision
If complex prediction algorithms are used to improve path accuracy in crowded environments, then path determination precision is improved, but the computation load increases
Solution Approach 1:
The patent focuses computational resources on optimizing key parameters (distance and velocity) in the objective function rather than using complex full-state prediction. By concentrating computation on these critical variables that directly affect path quality, the system achieves high predicted path accuracy with reduced computation load compared to comprehensive prediction models.
Data Source
AI summary
A path determination device and so forth are provided which can determine a path of a robot such that an autonomous mobile robot smoothly moves to a destination while avoiding an interference with a traffic participant even under a traffic environment such as a crowd. A path determination device (1) determines a temporary moving velocity command v_cnn, by using a CNN, such that an interference between a robot (2) and a traffic participant is avoided and determines a moving velocity command v of the robot (2), by using a DWA, such that in a case where the robot (2) is assumed to move from a present position by the temporary moving velocity command v_cnn, an objective function G(v) including a distance dist to the traffic participant closest to the robot (2) and the temporary moving velocity command v_cnn of the robot (2) as independent variables becomes a maximum value.