Vehicle Control System for Working Equipment Path Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems with working equipment, such as loader cranes or hook-lifts, rely heavily on driver experience and knowledge for optimal and safe operation, and struggle to adapt to varying environmental conditions like slippery surfaces, making it difficult to generate effective driving instructions.
Innovation Solution
A vehicle system equipped with a sensor system to capture environmental data, a vehicle data unit to determine vehicle characteristics, and a control unit to generate driving instructions based on image data, ambient conditions, and vehicle data, allowing for path calculation and adaptation to ensure safe and accurate movement towards a target object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driving instructions are generated based on general rules, then the system is simple to implement, but the instructions are not accurate enough for varying environmental conditions
Solution Approach 1:
The control system dynamically adapts driving instructions based on real-time sensor data about environmental conditions (slippery surfaces, slopes, obstacles) and vehicle state (load, speed). The system transitions from static general rules to dynamic condition-based control, adjusting parameters like acceleration, steering angle, and braking force according to current conditions.
Solution Approach 2:
The system implements feedback loops where sensor data from the environment and vehicle state are continuously monitored and fed back to the control unit. This feedback mechanism allows the system to refine driving instructions in real-time, improving accuracy by comparing actual vehicle response with expected behavior and making corrective adjustments.
2Reliability
If the system considers numerous environmental and vehicle parameters, then the safety and accuracy improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The control system segments the complex control task into distinct modules: environmental perception module (processing sensor data about surfaces and obstacles), vehicle state monitoring module (tracking load, speed, position), path planning module (calculating optimal trajectory), and execution module (controlling steering, acceleration, braking). Each module handles specific parameters independently, reducing overall computational complexity while maintaining comprehensive analysis.
Solution Approach 2:
The system performs preliminary analysis of environmental conditions and vehicle state before generating driving instructions. By pre-processing sensor data to identify surface types, slopes, and potential hazards in advance, the system reduces the computational burden during real-time control decisions, enabling safe operation without excessive processing complexity.
3Extent of automation
If autonomous control is implemented, then reliance on driver experience is reduced, but the system requires extensive sensor integration and complex algorithms
Solution Approach 1:
The control system is designed with multi-functional sensors and processing units that serve multiple purposes. For example, sensors detect both environmental conditions (surface type, obstacles) and vehicle state (position, orientation), while the control unit handles both path planning and real-time adjustment of driving parameters. This universal approach reduces the total number of components needed compared to dedicated specialized systems.
Solution Approach 2:
The system implements self-calibration and adaptive learning capabilities where the control unit automatically adjusts parameters based on observed vehicle behavior and environmental feedback. This self-service mechanism reduces the need for complex manual configuration and extensive pre-programming, enabling autonomous operation with more streamlined sensor and algorithm requirements.
Data Source
Figure 1~3
Figure 4~5
AI summary
A vehicle (2) comprising a working equipment (4), and further comprising: - a sensor system (6) configured to capture environmental data reflecting the environment around the vehicle and to determine, based on said data, image data (8) representing an area at least partly surrounding the vehicle (2), and optionally ambient condition data (10), - a vehicle data unit (12) configured to determine vehicle data (14) representing characteristics of the vehicle (2), - a control unit (16) configured to receive said image data (8), and said vehicle data (14), and optionally said ambient condition data (10), and to determine and generate control signals (18) for controlling said vehicle (2), wherein said control signals (18) comprise driving instructions. The control unit (16) is configured to receive a working task to be performed by the vehicle, wherein said working task includes information of an object (20) for the vehicle (2) to reach when performing said working task. The control unit (16) is configured to: determine a target position (22), being a position based on the location of the object (20) to reach when performing said working task in said image data representation, in relation to said vehicle (2), calculate at least a first path (24) from the vehicle (2) to the target position (22) by applying a set of path calculation rules, and determine driving instructions such that said vehicle (2) is controlled to follow said at least first path (24), wherein said driving instructions are determined in dependence of said image data (8), and vehicle data (14), and optionally said ambient condition data (10), by applying a set of path following rules.