Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse
Abstract
:1. Introduction
- We have provided an optimal load factor solving solution. By converting the three user requirements including working distance, time and load into load-related factors, the optimal result can be obtained among system complexity, efficiency and system energy consumption. The specifications of the main components of the ITS such as drive structure, power and battery components are constrained to a limited range according to the load factor and the work scenario. The specification of constraints helps other researchers determine the design of ITS and the choice of key components.
- We propose a special weighted connected graph structure to support map modeling and navigation applications. Based on this, a specialized visual navigation and motion control system has been proposed. With a fixed control action and boundary-defined Proportion Integral Differential (PID) control algorithm, the repeat path accuracy of the error < 0.02 can be obtained without relying on third-party positioning. This makes prior path planning, accurate mileage and energy consumption prediction possible. It can help researchers and designers further improve the efficiency of the ITS system.
- We present an efficient example of an ITS system and its detailed design based on the proposed method with optimal calculation results. Test results include working paths and mileage, speed and energy consumption are described in detail under different test conditions, including conditions beyond the rated load. The performance of the system, which is verified in the test environment, is described in detail.
2. Related Works
3. Problem Description and Objectives
3.1. An Application Case: ITS in a Greenhouse Environment
3.2. Definition of Requirements and Input Conditions
4. Multiple Conditional Constraints Reasoning
4.1. Load Constraint and Factor
4.2. Work Planning and Mileage Calculating
4.3. Resistance with Load Capacity
4.4. Speed Condition under Efficiency Constraints
4.5. Structural Stability Conditions
4.6. Normalization and Optimal Solution of Multiple Conditional Constraints
5. Efficient Visual Navigation and Motion Control System
5.1. Specialized Undirected Weighted Connected Graph and Fixed Action Control
- All nodes in the graph are connected, but loops are not allowed.
- The relative positions between the nodes are limited to four types of up, down, left, and right. For example, for a node , the node adjacent to it is only allowed to appear in its , , and in four positions. This also limits the direction of ITS movement to four, which can significantly reduce the complexity of graph and control algorithms..
- The association between the nodes is given a weight of according to the distance between the nodes. For example, the weight between nodes and is , and the weight between nodes and is . This weight value helps to control the distance heuristics in the algorithm.
5.2. Vision-Based Navigation System
6. Improved ITS System Selection and Implementation
6.1. Qualification Calculation Based on Instance
- First of all, the quality factor can be determined according to the working area count .
- Then, the total working distance can be calculated according to the factor m that .
- In addition, the total effective working time limit can be calculated: , where the total replenishment time is 430 . The loss time is and the time for the device status to confirm the fixed loss is about 15 per replenishment.
- The average speed of the system can be determined by mileage and time, which is [0.51, 0.71] .
6.2. An ITS Instance for Greenhouse Spraying Application
7. System Efficiency Testing and Result Analysis
7.1. Test Environment Settings
7.2. DAQ System
7.3. System Test and Result Comparison
- When , the main indicators of the system are optimal, and the sub-optimal result is only 3% different. This provides some redundancy for system design.
- The average error between the actual working mileage and the ideal calculated value is only 2%, which proves the accuracy of the navigation and control system designed in this paper. When , the actual working distance is 99.7% of the ideal value. Due to the presence of sensor errors and the response speed of the PID controller, the AGV is unlikely to reach exactly above the target node. It is possible for the AGV to stop during the previous sample-execution cycle or to stop during the next cycle. Thus, the AGV has a error from the target node, and the accumulation of the error will cause a positive and negative deviation between the total mileage of the AGV and the ideal value.
- In the above test of m, although the predicted value is exceeded by about 5%, the total system energy consumption is lower than the battery power supply capacity. Existing power supply systems are feasible, especially if a backup battery is enabled.
- In terms of efficiency, when is equivalent to two workers due to the maximum output speed limit, in other cases in which system efficiency is lower than , but still higher than the efficiency of a single worker . This proves the high efficiency of the ITS system designed in this paper.
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AC | Alternating Current Power |
ADC | Analog-to-Digital Converter |
AGV | Automated Guided Vehicle |
AI | Artificial Intelligence |
AWPS | Agricultural Worker Protection Standard |
CAN | Controller Area Network |
CPU | Central Processing Unit |
CUDA | Compute Unified Device Architecture |
DAQ | Data Acquisition |
DC | Direct Current Power |
ECU | Electronic Control Unit |
IMS | Intelligent Mechatronic Systems |
IMU | Inertial Measurement Unit |
ITS | Intelligent Transportation Systems |
MMC-AGV | Multiple Conditional Constraints for AGV |
MOSFET | Metal-Oxide-Semiconductor Field-Effect Transistor |
NFR | Net Financial Return |
PGR | Planetary Gearboxes Reducer |
PLC | Planetary Gearboxes Reducer |
QR | Quick Response |
RUPs | Restricted Use Pesticides |
PWM | Pulse Width Modulation |
US-EPA | United States Environmental Protection Agency |
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Application Field | Name | Size * (L × W × H,m) | Turning Radius (m) | Speed (m/s) | Load (Kg) | Endurance (h) | Open Source |
---|---|---|---|---|---|---|---|
Warehouse | Richo M2 [33] | 0.5 ×0.6 × 0.7 | 0.6 | 60 | N/A | F | |
Kiva [14] | 1.0 ×0.6 × 0.4 | 0.6 | 1.3 | 450 | N/A | F | |
River [15,34] | 0.9 ×0.6 ×1.25 | 0.7 | N/A | 72 | N/A | F | |
Butler XL [35] | 1.3 ×0.8 ×0.3 | 1.1 | N/A | 1300 | N/A | F | |
CaiNiao G2 [16] | 1.0 ×0.8 ×1.2 | 0.8 | 4 | 50 | 8 | F | |
JingDong [36] | 1.0 ×0.8 ×0.6 | 2.2 | 2 | 40 | 6 | F | |
Delivery | Yelp EAT24 [46] | 1.1 ×0.6 ×1.2 | N/A | 1.9 | 12 | N/A | F |
Segway [45] | 1.2 ×1.0 ×1.2 | 1.0 | 11 | 200 | 8 | T ** | |
Postmates [37] | 0.5 ×0.5 ×0.8 | 0.5 | N/A | 25 | ∼10 | F | |
AGROBOT [38] | 4.8 ×6.2 ×3.2 | >6 | N/A | >100 | >10 * | F | |
Blueriver [39] | 1.2 ×1.0 ×1.0 | ∼0.8 | N/A | N/A | F | ||
Agriculture | LettuceBot [40] | 4 ×6 ×0.8 | ∼4.0 | N/A | N/A | N/A | F |
RMAX II [41] | 3.2 ×3.2 ×0.55 | ∼3.5 | N/A | 16 | 1 | F | |
BoniRob [47,48] | 3.6 ×2.5 ×1.5 | ∼3.5 | N/A | N/A | N/A | T | |
Retail | Bossanova | 0.3 ×0.3 ×0.5 | 0.3 | N/A | N/A | F | |
ICE RS26 [42] | 1.65 ×0.86 ×1.37 | >2 | 1.8 | ∼130 | 4 | T ** | |
Navii [43] | 0.5 ×0.5 ×1.52 | ∼0.5 | ∼0.5 | N/A | 8∼10 | F | |
Tally [44] | 0.47 ×0.47 ×1.62 | ∼0.5 | ∼0.45 | N/A | 2∼4 | F |
Params Name | Symbols | Quantity | Params Name | Symbols | Quantity |
---|---|---|---|---|---|
greenhouse width | W | 90 | pesticide tank | 500 L | |
greenhouse height | H | 50 | Pump flow | Q | L/s |
total operating time | 1.5 | working area count | n | 22 | |
area path length | 50 | operationg area width | 2.5 | ||
space path length | 90 | area path width | 0.6 | ||
space path spacing | d | 4 | spray volume | L/m | |
worker efficiency | required efficiency | ||||
frictional coefficient (asphalt pavement) | [50] | gravitation-acceleration | g | 9.8 | |
vehicle self-weight | 25 | load weight | 22.7 | ||
wheel size | 3∼16 | experience factor |
Components | Specification | Components | Specification |
---|---|---|---|
Moto power | 120 | ECU | Freescale MC9S12X |
Moto output torque | 8.5 | Pump | (rated) |
Total ITS weight | 25 | Main Controller | NVIDIA Tegra K1/X2 |
Rated load | 150 250 MAX | Main battery | 48 12 |
Wheel diameter | 5 | Size (include tank) | 0.52() |
Part | Target | Sensor | Feature | Values | Rate |
---|---|---|---|---|---|
(1) | Moto 1 | Current | 50 mA | 10 Hz | |
(2) | Moto 1 | Voltage | 50 mV | ||
(3) | Moto 2 | Current | 50 mA | ||
(4) | Moto 2 | Voltage | 50 mV | ||
(5) | Moto 1 | Encoder | 1024 PPR | ||
(6) | Moto 2 | Encoder | 1024 PPR | ||
(7) | Pump | Voltage | 50 mV | ||
(8) | Pump | Current | 50 mA | ||
(9) | Control System | Voltage | 50 mV | ||
(10) | Control System | Current | 50 mA | ||
(11)–(13) | Triaxial accelerometer | XYZ | ∼ | 16,384 LSB/g |
Load Factor (m) | Mileage | Consumption | Discharge | Time | Efficiency (%) | ||
---|---|---|---|---|---|---|---|
Ideal | Actual | Mileage Error (%) | Rate (%) | Actual | Actual | ||
1 | 4224 | 4214.7 | 99.78 | 532.8 | 92.5 | 8644 | 62.47 |
2 | 3256 | 3247.6 | 99.74 | 524.2 | 91.0 | 6775 | 79.70 |
3 | 2880 | 3040.1 | 105.56 | 483.8 | 84 | 6353 | 85.00 |
4 | 2856 | 2968.1 | 103.94 | 496.5 | 86.2 | 6191 | 87.22 |
5 | 2616 | 2768.5 | 105.83 | 490.2 | 85.1 | 5831 | 92.61 |
6 | 2664 | 2656.5 | 99.72 | 481.1 | 83.5 | 5633 | 95.86 |
7 | 2544 | 2704.8 | 106.32 | 484.4 | 84.1 | 5701 | 94.72 |
8 * | 2568 | 2712 | 105.61 | 535.7 | 93 | 5749 | 93.93 |
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Zhang, T.; Zhou, W.; Meng, F.; Li, Z. Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse. Electronics 2019, 8, 946. https://doi.org/10.3390/electronics8090946
Zhang T, Zhou W, Meng F, Li Z. Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse. Electronics. 2019; 8(9):946. https://doi.org/10.3390/electronics8090946
Chicago/Turabian StyleZhang, Tianfan, Weiwen Zhou, Fei Meng, and Zhe Li. 2019. "Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse" Electronics 8, no. 9: 946. https://doi.org/10.3390/electronics8090946
APA StyleZhang, T., Zhou, W., Meng, F., & Li, Z. (2019). Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse. Electronics, 8(9), 946. https://doi.org/10.3390/electronics8090946