LIDAR Obstacle Detection for Heavy Machinery
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Solution Overview
Problem
Current obstacle detection systems for heavy machinery lack precision in determining stopping distances and efficiently scanning known paths, especially in varying terrain and conditions, which can lead to collisions or operational inefficiencies.
Innovation Solution
A method utilizing a LIDAR system that determines the position and orientation of the machine relative to a map, calculates stopping distances, and configures LIDAR regions of interest to perform concentrated and broad scans, enabling precise obstacle detection by segmenting the ground plane and adjusting scan patterns based on machine speed, weight, and path characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a LIDAR system performs broad scans to cover large areas, then the coverage area increases, but the detection precision and scan concentration decrease
Solution Approach 1:
The LIDAR system divides the scan area into multiple regions of interest (ROIs) based on path segments and stopping distances. Instead of scanning the entire area uniformly, the system segments the scan into concentrated regions along the known path where obstacles are most likely to affect machine operation, thereby maintaining high detection precision in critical areas while reducing unnecessary scans in less critical areas.
Solution Approach 2:
The system applies different scan concentrations to different spatial regions. High-density scans are concentrated along the known path at distances corresponding to the machine's stopping distance, while peripheral areas receive less scanning attention. This local differentiation of scan quality optimizes detection precision where it matters most while reducing overall system resource consumption.
2Measurement precision
If the LIDAR system performs concentrated scans along the known path, then the obstacle detection precision improves, but the scan coverage area decreases
Solution Approach 1:
The system pre-determines regions of interest based on the known path geometry and machine stopping distance before performing scans. By calculating in advance where obstacles could potentially impact the machine (within stopping distance along the path), the system concentrates scanning resources on these pre-identified critical zones, achieving high detection precision without wasting resources on areas that cannot affect machine safety.
Solution Approach 2:
The LIDAR system dynamically adjusts scan concentration and orientation based on real-time machine position, speed, and the geometry of the known path. As the machine moves along the path, the ROIs are recalculated and scan patterns are reoriented to maintain concentrated coverage over the relevant stopping distance, adapting the scan coverage area to match the dynamic operational context.
3Productivity
If the machine travels at higher speeds, then the productivity increases, but the required stopping distance increases, expanding the LIDAR region of interest
Solution Approach 1:
The system dynamically calculates the LIDAR region of interest length as a function of machine speed, weight, and path characteristics. When the machine travels faster, the ROI length automatically increases to cover the longer stopping distance required at higher speeds. Conversely, when speed decreases, the ROI length contracts. This dynamic adjustment ensures the LIDAR system maintains appropriate detection coverage for safety while minimizing unnecessary scanning at lower speeds.
Solution Approach 2:
The system changes the spatial parameters of the LIDAR scan (specifically the length of the region of interest) based on operational parameters such as machine speed and weight. By linking the ROI length to the calculated stopping distance, which varies with speed and weight, the system adapts its detection zone to match the actual safety requirements of the current operational state, optimizing both safety and scanning efficiency.
4Reliability
If the LIDAR system performs frequent scans to detect obstacles in real-time, then the detection reliability improves, but the energy consumption increases
Solution Approach 1:
The LIDAR system performs scans selectively only in segmented regions of interest along the known path, rather than continuously scanning all surrounding areas. By dividing the environment into relevant ROIs based on path geometry and stopping distance, the system maintains high detection reliability in critical zones while significantly reducing the total number of scans performed, thereby lowering energy consumption.
Solution Approach 2:
The system performs scans at a frequency and coverage level that is sufficient for safety-critical detection along the known path, rather than performing exhaustive continuous scanning of all possible directions. The scan frequency and ROI coverage are calibrated to provide adequate detection reliability for obstacle avoidance while avoiding excessive scanning that would waste energy, implementing a balanced partial action approach.
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 enhances the accuracy and efficiency of obstacle detection, allowing machines to navigate safely and efficiently along known paths, even in complex terrains, by optimizing LIDAR scans and adjusting to real-time operational variables.
Implementation Method 1
performing a concentrated LIDAR scan on the determined LIDAR region of interest to generate LIDAR scan data
Data Source
AI summary
A method for detecting an obstacle along a known path of a machine can include relating the location of the machine with a map. The map can include a worksite with a known path or known paths that the machine can travel on. The stopping distance of the machine can be determined and based on these identified known path and its characteristics along with a traveling speed and a weight of the machine. A LIDAR region of interest can be determined based on the stopping distance, the position and orientation of the machine, and characteristics of the known path. The machine can include a LIDAR system that can be configured to be oriented with respect to the LIDAR region of interest. A concentrated LIDAR scan can be performed to detect if an obstacle is present within the LIDAR region of interest.