UAV Velocity Control via Dynamic Environmental Complexity Assessment
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Solution Overview
Problem
Existing control schemes for unmanned aerial vehicles (UAVs) are not optimal in environments with obstacles, relying on user judgment and being challenging for inexperienced users or in situations where the user cannot see the UAV, leading to potential collisions.
Innovation Solution
A system and method for automatically determining operating rules for UAVs using sensors to assess environmental complexity, such as obstacle density, to prevent collisions, which includes determining environmental complexity factors and adjusting velocity rules and attitude rules dynamically based on sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing control schemes rely on user judgment to determine safe operating parameters, then the system maintains simplicity in control architecture, but the safety and collision avoidance performance deteriorates in environments with obstacles
Solution Approach 1:
The UAV system performs self-assessment of environmental complexity using its own sensors (cameras, GPS, barometers) without requiring external input. The processor automatically determines operating rules based on sensor data, allowing the system to serve itself in evaluating safety conditions and adjusting control parameters dynamically.
Solution Approach 2:
The system continuously monitors environmental conditions through sensors and uses this feedback to dynamically adjust operating rules. The processor receives sensor data, evaluates environmental complexity, and modifies velocity and attitude rules in real-time based on the assessed risk level, creating a closed-loop control system that improves safety through continuous adaptation.
2Reliability
If the system automatically determines operating rules based on sensor data, then the safety and collision avoidance improves, but the device complexity and processing requirements increase
Solution Approach 1:
The control system is divided into distinct functional modules: sensor data acquisition, environmental complexity assessment, operating rule determination, and execution. The processor separates the evaluation of different sensor types (visual obstacles, GPS position, barometric pressure) and processes them independently to determine specific operating rules for velocity and attitude control.
Solution Approach 2:
The operating rules are made dynamic rather than static. The system continuously updates velocity limits and attitude constraints based on real-time environmental assessment. The control parameters adapt dynamically to changing conditions, allowing the UAV to operate safely in varying environmental complexities without requiring a completely new control architecture.
3Reliability
If velocity rules are restricted to reduce collision risk, then safety improves, but the productivity and mission completion time deteriorates
Solution Approach 1:
Velocity rules are made dynamic rather than uniformly restrictive. The system adjusts maximum velocity limits based on real-time environmental complexity assessment. In safe environments with low obstacle density, the UAV can operate at higher velocities to maintain productivity. In complex environments with high obstacle density, velocity limits are automatically reduced to minimize collision risk, optimizing the trade-off between safety and efficiency.
Solution Approach 2:
Different velocity constraints are applied to different spatial regions and flight phases. The system identifies specific areas of high risk and applies localized velocity restrictions only where necessary, rather than imposing uniform speed limits throughout the entire mission. This allows the UAV to maintain high speed in safe zones while slowing down only in problematic areas.
Data Source
AI summary
Systems and methods for controlling an unmanned aerial vehicle within an environment are provided. In one aspect, a system comprises one or more sensors carried on the unmanned aerial vehicle and configured to receive sensor data of the environment and one or more processors. The one or more processors may be individually or collectively configured to: determine, based on the sensor data, an environmental complexity factor representative of an obstacle density for the environment; determine, based on the environmental complexity factor, one or more operating rules for the unmanned aerial vehicle; receive a signal indicating a desired movement of the unmanned aerial vehicle; and cause the unmanned aerial vehicle to move in accordance with the signal while complying with the one or more operating rules.