Robotic Motion Planning With Iterative Waypoints and Collision Limits
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Solution Overview
Problem
Current solutions for vehicle yard operations are labor-intensive, dangerous, and limited by human operational capabilities, especially in dynamic environments, and lack integrated system-wide solutions for tasks like vehicle maintenance, which requires mobility, perception, and manipulation in outdoor settings, posing challenges in collision avoidance and efficient path planning for robotic systems.
Innovation Solution
A robotic system with a propulsion system, onboard sensors for environmental data, and a local controller that determines waypoints and movement limitations to avoid collisions, allowing autonomous navigation and switching to tele-operation mode as needed, enabling safe and efficient movement and task performance in vehicle yards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If robotic systems are deployed for autonomous vehicle yard operations, then labor intensity and danger are reduced, but collision avoidance and path planning complexity increase
Solution Approach 1:
The autonomous navigation system is segmented into multiple independent modules: obstacle detection module using sensors, path planning module that generates waypoints, motion control module that executes movement, and safety monitoring module. This modular architecture reduces overall system complexity by allowing each module to be developed and optimized independently while working together to solve the collision avoidance problem.
Solution Approach 2:
A central controller acts as an intermediary between the various subsystems (sensors, actuators, navigation algorithms). The controller receives raw sensor data, processes it through navigation algorithms to determine safe paths and waypoints, and then sends control signals to the propulsion system. This intermediary layer simplifies the integration complexity by providing a unified interface between perception and action systems.
2Productivity
If robotic systems perform maintenance tasks in dynamic environments, then operational efficiency improves, but reliability decreases due to unpredictable situations
Solution Approach 1:
The robotic system incorporates continuous feedback loops where onboard sensors constantly monitor the environment and system state, this information is fed back to the controller which adjusts navigation and manipulation actions in real-time. The feedback mechanism allows the system to adapt to dynamic changes in the vehicle yard environment, maintaining reliability while performing maintenance tasks efficiently.
Solution Approach 2:
The navigation and control system is designed to be dynamic rather than static, allowing real-time adjustments to path planning and motion execution based on current environmental conditions. The system can dynamically recalculate waypoints, adjust speed, and modify manipulation forces in response to moving obstacles or changing task requirements, ensuring both efficiency and reliability in unpredictable situations.
3Reliability
If autonomous navigation with iterative waypoint determination is implemented, then collision avoidance improves, but navigation time increases
Solution Approach 1:
The navigation system uses partial action by determining waypoints at appropriate intervals rather than continuously calculating every possible position. The system calculates the next waypoint based on current position and goal, executes movement toward it, then recalculates when needed. This approach provides sufficient collision avoidance without the excessive computational overhead of continuous recalculation, optimizing the balance between safety and navigation time.
Data Source
AI summary
A system includes a robotic vehicle having a propulsion and a manipulator configured to perform designated tasks. The system also including a local controller disposed onboard the robotic vehicle and configured to receive input signals from an off-board controller. Responsive to receiving an input signal for moving in an autonomous mode, the local controller is configured to move the robotic vehicle toward one of the different final destinations by autonomously and iteratively determining a series of waypoints until the robotic vehicle has reached the one final destination. For each iteration, the local controller is configured to determine a next waypoint between a current location of the robotic vehicle and the final destination, determine movement limitations of the robotic vehicle, and generate control signals in accordance with the movement limitations.