Improved forest harvester

Abstract

A forest harvester (100) for cutting trees includes a harvest sequencing system (10) configured to determine a harvesting sequence (34) of trees to be cut. The system comprises a perception system (20) configured to collect data about the trees and a perception computer (30) for generating Tree level information (110) for the trees. The perception computer (30) is configured to determine the harvesting sequence (34) in dependence on the Tree level information (110) and a harvesting policy (90). The forest harvester (100) also includes a control system (80) configured to receive the harvesting sequence (34) from the perception computer (30) and coordinate the cutting of trees according to the harvesting sequence (34). This allows for optimal tree selection and efficient forest thinning operations.

Description

[0001] IMPROVED FOREST HARVESTER

[0002] Field

[0003] The technology relates to the field of forestry management and harvesting equipment, specifically focusing on forest thinning and the optimization of tree selection and cutting sequences. This field involves the development and implementation of advanced systems and methods to improve the efficiency, accuracy, and sustainability of forest harvesting operations.

[0004] Forests used for timber production exhibit significant variations in tree species distribution, tree density, ground vegetation, and terrain. Forest management strategies also differ between forest owners with varying goals, such as carbon capture and biodiversity conservation or maximizing short or long-term profitability. In forest thinning or other forms of partial forest harvesting, the choice of which trees to cut and which to leave has significant short and long-term effects on the continued growth rate, carbon capture rate, health, and resilience of the remaining trees, as well as the overall forest ecosystem.

[0005] Current forest harvesting prescriptions or specifications are usually given at a high level, specifying target volume and species distribution over a large area. It is the responsibility of the machine operator to continuously translate these high-level specifications into individual tree selections and adapt to local conditions. This task is performed manually based on the experience of a particular machine operator while simultaneously operating a complex machine. Factors such as tree age, shape, health, species diversity, and spatial distribution need to be taken into account in order to realize a particular forest management goal in a given forest. However, due to the complexity of the task and the reliance on manual selection, these factors may not be adequately considered, leading to suboptimal tree selection and negative impacts on the forest ecosystem. Furthermore, tree selection and cutting sequence have direct economic impacts. Individual trees have very different refined values, and selecting an appropriate sequence of trees to cut has a large impact on the overall productivity of the operation, and by extension, the profitability of both the subcontractor and forest owner. Due to the manual nature of the process and the complexity of the task, machine operators may not be able to optimize the tree selection sequence, leading to reduced productivity and profitability for both parties involved.

[0006] Prior art operator support systems based on perceptive sensors, such as cameras or lidar, have been described in academic publications and patents and have been demonstrated by original equipment manufacturers (OEMs) and other entities. These systems provide feedback on an aggregated level, such as tree density or volume, toward a traditional forest management prescription. However, these systems do not provide an optimal cutting sequence based on a forest management goal and only estimate tree position and diameter. They lack access to the richer tree description (position, species, geometric shape, and health / damage status) and functionality for factoring in machine dynamics that are required to determine the optimal cutting sequence.

[0007] Summary

[0008] According to a first aspect of the disclosure, a forest harvester is provided for cutting trees. The harvester comprises a harvest sequencing system configured to determine a harvesting sequence of trees to be cut. The system includes a perception system configured to collect data about the trees and a perception computer for generating tree level information for the trees. The perception computer is configured to determine the harvesting sequence in dependence on the tree level information and a harvesting policy. The harvester also includes a control system configured to receive the harvesting sequence from the perception computer and coordinate the cutting of trees according to the harvesting sequence. This aspect of the disclosure allows for efficient and effective tree cutting, as the harvesting sequence is determined based on detailed information about the trees and a specific harvesting policy. The harvesting sequence may include the movements of the forest harvester to cut the identified trees so that determining the harvesting sequence comprises determining the trees to be cut as well as the movements of the forest harvester to achieve the cutting of the trees.

[0009] Optionally in some examples, the perception system of the forest harvester comprises sensors configured to collect data from the surrounding environment. This allows for accurate and comprehensive data collection, which can enhance the quality of the tree level information and the effectiveness of the harvesting sequence.

[0010] Optionally in some examples, the sensors of the perception system comprise at least one of a lidar sensor and a camera. This provides a variety of data collection methods, which can capture different types of information about the trees and the surrounding environment.

[0011] Optionally in some examples, the forest harvester further comprises a processor configured to process data from the sensors to generate the tree level information in dependence on a tree characterization algorithm. This allows for efficient and accurate processing of the collected data, which can enhance the quality of the tree level information and the effectiveness of the harvesting sequence.

[0012] Optionally in some examples, the tree characterization algorithm is selected from the group consisting of a rule-based algorithm, a machine learning algorithm, a genetic algorithm, a dynamic programming algorithm, and a multi-objective optimization algorithm. This provides a variety of algorithm options, which can be selected based on the specific needs and conditions of the forest and the trees.

[0013] Optionally in some examples, the harvesting sequence is further determined in dependence on machine parameters of the forest harvester. This allows for a more customized and optimized harvesting sequence, which can enhance the efficiency and effectiveness of the tree cutting process.

[0014] Optionally in some examples, the harvesting policy comprises at least one of a variety of targets, including overall or per species diameter range or distribution, overall or per species height range or distribution, overall or per species tree age range or distribution, overall or per species spatial distribution, overall or per species volume ratio before / after, overall or per species basal area ratio before / after, overall or per species tree count ratio before / after, to be extracted or remaining wood volume, to be extracted or remaining carbon storage, carbon capture rate after harvesting, cutting lane width, cutting lane distance, cutting lane area, special considerations for certain species, species diversity, individual tree health or damage, individual tree growth rate, ecosystem services, fire risk mitigation, wind damage risk mitigation, ground damage risk mitigation, environmental regulations, preservation of areas of cultural value, and profitability of operation. This provides a comprehensive and flexible harvesting policy, which can be tailored to the specific goals and constraints of the forest management.

[0015] Optionally in some examples, the perception computer comprises a sequencing algorithm configured to determine the harvesting sequence in dependence on the tree level information and the harvesting policy. This allows for a more accurate and optimized harvesting sequence, which can enhance the efficiency and effectiveness of the tree cutting process.

[0016] Optionally in some examples, the sequencing algorithm is further configured to determine the harvesting sequence in dependence on machine parameters of the forest harvester. This allows for a more customized and optimized harvesting sequence, which can enhance the efficiency and effectiveness of the tree cutting process.

[0017] Optionally in some examples, the sequencing algorithm is selected from the group consisting of a dynamic programming algorithm, a linear programming algorithm, a nonlinear optimization algorithm, a convex optimization algorithm, a gradient method, a simulated annealing algorithm, a discrete optimization algorithm, a constraint programming algorithm, a random search algorithm, and a machine learning algorithm. This provides a variety of algorithm options, which can be selected based on the specific needs and conditions of the forest and the trees.

[0018] Optionally in some examples, the control system of the forest harvester further comprises a visual operator interface configured to communicate the harvesting sequence to an operator. This allows for clear and effective communication of the harvesting sequence, which can enhance the accuracy and efficiency of the tree cutting process.

[0019] Optionally in some examples, the visual operator interface comprises a display selected from the group consisting of a head-up display, augmented reality glasses, and a screen. This provides a variety of display options, which can be selected based on the specific needs and preferences of the operator.

[0020] Optionally in some examples, the control system comprises a machine control system configured to receive the harvesting sequence from the perception computer and control the forest harvester to perform cutting of the trees according to the harvesting sequence autonomously. This allows for autonomous tree cutting, which can enhance the efficiency and effectiveness of the tree cutting process.

[0021] Optionally in some examples, the tree level information comprises at least one of tree age, tree diameter, tree height, tree volume, tree growth rate, tree species, tree health / damage status, overall tree shape, tree trunk shape, tree canopy shape, tree canopy volume, branch structure, tree biomass, and tree carbon content, tree carbon capture rate. This provides a comprehensive set of tree level information, which can enhance the accuracy and effectiveness of the harvesting sequence.

[0022] According to a second aspect of the disclosure, a method is provided for optimal tree selection for forest thinning using the forest harvester. The method comprises the steps of collecting and detecting data from the surrounding environment using sensors, processing the collected data using a tree characterization algorithm to generate tree level information, calculating the harvesting sequence based on the tree level information and the harvesting policy, continuously recomputing the harvesting sequence as the forest harvester moves forward and the state of the trees around the machine changes, and coordinating the cutting of the selected trees using a control system following the determined harvesting sequence. This method allows for efficient and effective tree selection and cutting, as the harvesting sequence is continuously updated based on the changing state of the trees and the forest. Optionally in some examples, the method further comprises the step of allowing the control system to control the forest harvester directly if the forest harvester is autonomous. This allows for autonomous tree cutting, which can enhance the efficiency and effectiveness of the tree cutting process.

[0023] Optionally in some examples, the method further comprises the step of communicating the harvesting sequence to a machine operator through a visual operator interface if the forest harvester is manually operated. This allows for clear and effective communication of the harvesting sequence, which can enhance the accuracy and efficiency of the tree cutting process.

[0024] According to a third aspect of the disclosure, a harvest sequencing system is provided for generating tree level information for a set of trees. The system comprises sensors configured to collect data from the surrounding environment and a processor configured to process data from the sensors to generate the tree level information in dependence on a tree characterization algorithm, and determine a harvesting sequence of trees to be cut in dependence on the tree level information and a harvesting policy. The sensors comprise at least one of a lidar sensor and a camera. This system allows for accurate and comprehensive data collection and processing, which can enhance the quality of the tree level information and the effectiveness of the tree selection and cutting process.

[0025] Optionally in some examples, the tree characterization algorithm is selected from the group consisting of a rule-based algorithm, a machine learning algorithm, a genetic algorithm, a dynamic programming algorithm, and a multi-objective optimization algorithm. This provides a variety of algorithm options, which can be selected based on the specific needs and conditions of the forest and the trees. This aspect of the disclosure allows for efficient and effective tree characterization, as the algorithm can be tailored to the specific characteristics and conditions of the trees.

[0026] Brief Description of the Drawings

[0027] Examples are described in more detail below with reference to the appended drawings. Figure 1 is a schematic representation of the forest harvester with the harvest sequencing system, perception system, perception computer, and control system.

[0028] Figure 2 is a block diagram illustrating the components and relationships of the harvest sequencing system, perception system, perception computer, and control system.

[0029] Figure 3 is a flowchart depicting the method of optimal tree selection for forest thinning using the forest harvester.

[0030] Figure 4 is a perspective view of the forest harvester in operation and the cutting sequence of the trees.

[0031] Figure 5 is an example view of the visual operator interface, including the display options such as a head-up display, augmented reality glasses, and a screen.

[0032] Detailed Description

[0033] The detailed description set forth below provides information and examples of the disclosed technology with sufficient detail to enable those skilled in the art to practice the disclosure.

[0034] Figure 1 shows a schematic representation of the forest harvester 100, which includes the harvest sequencing system 10, perception system 20, perception computer 30, and control system 80. The forest harvester 100 is a vehicle designed for cutting trees, and its various components work together to determine the optimal sequence of trees to be cut based on forest management goals and constraints.

[0035] Figure 2 is a block diagram illustrating the components and relationships of the harvest sequencing system 10, perception system 20, perception computer 30, and control system 80. The perception system 20, which includes sensors 22, collects and detects data from the surrounding environment. The processor 26 processes the data from the sensors 22 to generate Tree level information 110 using a tree characterization algorithm 28. The perception computer 30 determines the harvesting sequence 34 using sequencing algorithm 32 based on the Tree level information 110 and a harvesting policy 90 and machine parameters 92. The control system 80 receives the harvesting sequence 34 from the perception computer 30 and coordinates the cutting of trees according to the harvesting sequence 34.

[0036] Figure 3 is a flowchart depicting the method of optimal tree selection for forest thinning using the forest harvester 100. The method includes collecting and detecting data from the surrounding environment using sensors 22, processing the collected data using a tree characterization algorithm 28 to generate Tree level information 110, calculating the harvesting sequence 34 using the sequencing algorithm 32 based on the tree level information 110 and the harvesting policy 90 and machine parameters 92, continuously recomputing the harvesting sequence 34 as the forest harvester 100 moves forward and the state of the trees around the machine changes, and coordinating the cutting of the selected trees using a control system 80 following the determined harvesting sequence 34.

[0037] Figure 4 is a perspective view of the forest harvester 100 in operation and the cutting sequence of the trees. The figure illustrates a harvesting sequence 34 showing numbered trees (1-4) and machine placement points (A and B) with a forest harvester 100 shown on the right side. The figure demonstrates a sample harvesting sequence 34 where:

[0038] The machine first positions itself at placement point A, from which it can:

[0039] Harvest tree #1

[0040] Harvest tree #2

[0041] The machine then repositions to placement point B, from which it can:

[0042] Harvest tree #3

[0043] Harvest tree #4 The positions A and B for forest harvester 100 are indicated by dashed lines. The harvesting sequence 34 may include the movements of the forest harvester 100, including instructions to move to the corresponding positions, to cut the identified trees.

[0044] Figure 5 is an example view of the display 42. The display 42 presents the harvesting sequence 34 to the machine operator in a visual format, reducing cognitive load and improving accuracy and productivity during the tree cutting process.

[0045] 1 . Forest Harvester Details

[0046] In one configuration, the forest harvester 100 is a specialized vehicle designed for the purpose of felling, delimbing, and bucking trees in a forest environment. The forest harvester 100 is equipped with a variety of components and systems that work together to optimize the tree cutting process, taking into account various factors such as the characteristics of the surrounding trees, forest management goals and constraints, and machine parameters 92. The forest harvester 100 is designed to operate in a variety of forest conditions and can be configured for either manual or autonomous operation, depending on the specific requirements of the harvesting operation.

[0047] 1.1. Harvest Sequencing System

[0048] In some implementations, the forest harvester 100 includes a harvest sequencing system 10. The harvest sequencing system 10 is a complex system that processes data from the environment and determines the optimal sequence for cutting trees based on forest management goals and constraints. This determination may include not only the order in which trees should be cut but also plans the necessary movements of the forest harvester 100 to efficiently execute the cutting of those trees. The harvest sequencing system 10 includes several components, including a perception system 20 and a perception computer 30, which work together to generate tree level information 110 and determine the harvesting sequence 34. The planning of movements may consider the capabilities of the forest harvester 100, such as its reach, maneuverability, and the time required for different actions, as well as the surrounding environment, including terrain and obstacles. 1.1.1. Perception System

[0049] In some examples, the perception system 20 is a component of the harvest sequencing system 10. The perception system 20 is responsible for collecting and detecting data from the surrounding environment, including the trees to be harvested, which is then processed to generate tree level information 110. The perception system 20 includes a variety of sensors 22, which can include one or more types of sensors 22 depending on the specific requirements of the harvesting operation.

[0050] 1 .1 .1 .1 . Sensors

[0051] In some configurations, the sensors 22 of the perception system 20 can include a variety of sensor types, such as lidar sensors 22, cameras, and other types of sensors 22 suitable for collecting data from the surrounding environment. The sensors 22 are configured to collect and detect data from the surrounding environment, including data related to the characteristics of the surrounding trees, the spatial distribution of the trees, and other relevant environmental factors. The number of sensors 22 can vary, with at least one sensor being included, but multiple sensors 22 can be used to collect a wider range of data and provide a more comprehensive view of the environment. The specific types of sensors 22 used, as well as their specifications, can vary depending on the specific requirements of the harvesting operation. For example, a lidar sensor may be used for its ability to accurately measure distances and generate detailed three-dimensional representations of the environment, while a camera may be used for its ability to capture high-resolution images of the trees and their surroundings. The sensors 22 are designed to be compact and lightweight for easy integration on the forest harvester 100, and they are also designed to have low power consumption for extended operation time.

[0052] 1.1.2. Perception Computer

[0053] In some examples, the harvest sequencing system 10 includes a perception computer 30. The perception computer 30 is a component of the system, as it processes the tree level information 110 and determines the harvesting sequence 34. The perception computer 30 operates in dependence on a sequencing algorithm 32 and the harvesting policy 90. The sequencing algorithm 32 is used to determine the optimal sequence for cutting trees, and to optionally plan the movements of the forest harvester 100 to perform the cutting, based on the tree level information 110 and the harvesting policy 90. The harvesting policy 90 sets the rules and guidelines for selecting the harvesting sequence 34 based on the forest management goals and constraints. The perception computer 30 is designed to handle complex computations and make intelligent decisions based on the tree level information 110 and the harvesting policy 90. This may include planning efficient routes and actions for the harvester to minimize travel time and optimize the cutting process.

[0054] 1 .1 .2.1 . Processor

[0055] In some implementations, the perception computer 30 includes a processor 26. The processor 26 is responsible for processing the data collected by the sensors 22. The processor 26 processes the sensor data to generate tree level information 110, which is used to inform the decision-making process for tree selection and sequencing. The processor 26 operates in dependence on a tree characterization algorithm 28, which is used to analyze the sensor data and extract relevant tree characteristics and attributes. The processor 26 is designed to handle large volumes of data and perform complex computations in real-time, enabling the system to quickly and accurately generate tree level information 110 based on the sensor data.

[0056] 1 .1 .2.1 .1 . Tree Characterization Algorithm

[0057] In some examples, the processor 26 utilizes a tree characterization algorithm 28 to process the sensor data and generate Tree level information 110. The tree characterization algorithm 28 is a complex algorithm that analyzes the sensor data and extracts relevant tree characteristics and attributes. The tree characterization algorithm 28 can include a variety of sub-algorithms or techniques, including machine learning algorithms, image processing algorithms, point cloud processing algorithms, classification algorithms, statistical algorithms, data fusion algorithms, and artificial intelligence algorithms. These algorithms and techniques are used to train models on large datasets of tree characteristics and their corresponding attributes, such as age, species, health, and spatial distribution. These models can then be used to accurately classify and analyze tree level information from the sensor data, enabling the system to make informed decisions on tree selection for thinning.

[0058] 1 .1 .2.1 .2. Perception Algorithm

[0059] In some configurations, the tree characterization algorithm 28 includes a perception algorithm. The perception algorithm is a sophisticated algorithm that utilizes a variety of techniques to analyze the sensor data and generate T ree level information 110. The perception algorithm can utilize machine learning techniques to train models on large datasets of tree characteristics and their corresponding attributes. These models can then be used to accurately classify and analyze tree level information from the sensor data, enabling the system to make informed decisions on tree selection for thinning. The perception algorithm can also employ image processing techniques to extract relevant features and information from the camera data. This can include edge detection, color analysis, texture analysis, and shape recognition algorithms to identify tree species, canopy cover, health / damage status, and geometric characteristics. If the system incorporates lidar sensors 22, the perception algorithm can utilize point cloud processing techniques to analyze the three-dimensional structure of the environment. This can include segmentation algorithms to separate individual trees from the point cloud, clustering algorithms to group trees based on their spatial distribution, and feature extraction algorithms to derive tree characteristics such as diameter and height.

[0060] 1.1.2.1.3. Tree Level Information

[0061] In some implementations, the perception computer 30 generates Tree level information 110 based on the data collected by the sensors 22 and processed by the processor 26 using the tree characterization algorithm 28. The Tree level information 110 includes a variety of data related to the characteristics and attributes of the individual trees in the forest. This can include information such as tree age, tree volume, tree growth rate, tree diameter, tree height, tree species, tree health / damage status, overall tree shape (e.g., conical, cylindrical), tree trunk shape, tree canopy shape, tree canopy volume, tree biomass, branch structure, tree carbon content, and tree carbon capture rate. The Tree level information 110 is used by the perception computer 30 to determine the harvesting sequence 34 based on the forest management goals and constraints. The Tree level information 110 is continuously updated as the forest harvester 100 moves through the forest and the state of the surrounding trees changes.

[0062] 1.1.2.2. Harvesting Policy

[0063] In some configurations, the perception computer 30 utilizes a harvesting policy 90 to guide the decision-making process for tree selection and sequencing. The harvesting policy 90 sets the rules and guidelines for selecting the harvesting sequence 34 based on the forest management goals and constraints. The harvesting policy 90 can include a variety of targets, such as maximizing the growth rate of the trees, promoting biodiversity, enhancing wildlife habitat, minimizing soil erosion, improving water quality, optimizing timber quality, optimizing carbon storage, supporting sustainable harvesting, and improving aesthetics. The harvesting policy 90 can also take into account various factors such as the overall or per species diameter range or distribution, overall or per species height range or distribution, overall or per species tree age range or distribution, overall or per species spatial distribution, overall or per species volume ratio before / after, overall or per species basal area ratio before / after, overall or per species tree count ratio before / after, to be extracted or remaining wood volume, to be extracted or remaining carbon storage, carbon capture rate after harvesting, cutting lane width, cutting lane distance, cutting lane area, special considerations for certain species, species diversity, individual tree health or damage, individual tree growth rate, ecosystem services, fire risk mitigation, wind damage risk mitigation, ground damage risk mitigation, environmental regulations, preservation of areas of cultural value, and profitability of operation.

[0064] 1 .1 .2.2.1 . Tree Specific Goals

[0065] In some implementations, the harvesting policy 90 includes Tree specific goals. These goals are specific targets or objectives related to individual trees that the system aims to achieve through the harvesting operation. The Tree specific goals can include a variety of objectives, such as maximizing the growth rate of the trees, promoting biodiversity, enhancing wildlife habitat, minimizing soil erosion, improving water quality, optimizing timber quality, optimizing carbon storage, supporting sustainable harvesting, and improving aesthetics. The Tree specific goals can also take into account various factors such as the overall or per species diameter range or distribution, overall or per species height range or distribution, overall or per species tree age range or distribution, overall or per species spatial distribution, overall or per species volume ratio before / after, overall or per species basal area ratio before / after, overall or per species tree count ratio before / after, to be extracted or remaining wood volume, to be extracted or remaining carbon storage, carbon capture rate after harvesting, cutting lane width, cutting lane distance, cutting lane area, special considerations for certain species, species diversity, individual tree health or damage, individual tree growth rate, ecosystem services, fire risk mitigation, wind damage risk mitigation, ground damage risk mitigation, environmental regulations, preservation of areas of cultural value, and profitability of operation. These Tree specific goals guide the decision-making process for tree selection and sequencing, helping to ensure that the harvesting operation aligns with the forest management goals and constraints.

[0066] 1.1.2.3. Sequencing Algorithm

[0067] In some examples, the perception computer 30 utilizes a sequencing algorithm 32 to determine the harvesting sequence 34. The sequencing algorithm 32 is a complex algorithm that determines the optimal sequence for cutting trees based on the tree level information 110 and the harvesting policy 90. The sequencing algorithm 32 may also plan the necessary movements of the forest harvester 100 to execute the cutting of the selected trees in the determined order. The sequencing algorithm 32 takes into account a variety of factors, including the characteristics of the surrounding trees, the forest management goals and constraints, and the machine parameters 92. The sequencing algorithm 32 includes several sub-algorithms or techniques, including sequence tree set algorithmss and sequence order algorithmss, which are used to determine the set of trees to be included in the harvesting sequence 34 and the order in which the selected trees should be cut. Furthermore, it may also incorporate a motion planning component to determine the optimal path and actions for the harvester to move between trees and perform the cutting operations efficiently.

[0068] 1 .1 .2.3.1 . Sequence Tree Set Algorithm In some configurations, the sequencing algorithm 32 includes a sequence tree set algorithm. The sequence tree set algorithm is a specialized algorithm that determines the set of trees to be included in the harvesting sequence 34 based on the surrounding trees' characteristics and the forest management goals and constraints. The sequence tree set algorithm takes into account a variety of factors, including the characteristics of the surrounding trees, the forest management goals and constraints, and the machine parameters 92. The sequence tree set algorithm utilizes a variety of techniques, including optimization algorithms, to determine the optimal set of trees to be included in the harvesting sequence 34.

[0069] 1 .1 .2.3.1 .1 . Optimisation Algorithm

[0070] In some implementations, the sequence tree set algorithm includes an Optimisation algorithm. The Optimisation algorithm is a sophisticated algorithm that optimizes the set of trees to be included in the harvesting sequence 34 based on the surrounding trees' characteristics and the forest management goals and constraints. The Optimisation algorithm can utilize a variety of techniques, including gradient descent, convex optimisation, dynamic programming, linear programming, nonlinear optimization, convex optimization, simulated annealing, discrete optimization, constraint programming, random search, and machine learning. These techniques are used to optimize the set of trees to be included in the harvesting sequence 34, taking into account the characteristics of the surrounding trees, the forest management goals and constraints, and the machine parameters 92.

[0071] 1 .1 .2.3.2. Sequence Order Algorithm

[0072] In some examples, the sequencing algorithm 32 includes a sequence order algorithm. The sequence order algorithm is a specialized algorithm that determines the order in which the selected trees should be cut based on the optimal tree sequence. The sequence order algorithm takes into account a variety of factors, including the characteristics of the selected trees, the forest management goals and constraints, and the machine parameters 92. The sequence order algorithm utilizes a variety of techniques, including Time optimisation algorithms, to determine the optimal order for cutting the selected trees. 1 .1 .2.3.2.1 . Time Optimisation Algorithm

[0073] In some configurations, the sequence order algorithm includes a Time optimisation algorithm. The Time optimisation algorithm is a sophisticated algorithm that optimizes the order in which the selected trees should be cut based on the optimal tree sequence and the time required to cut each tree. The Time optimisation algorithm can utilize a variety of techniques, including gradient descent, convex optimisation, dynamic programming, linear programming, nonlinear optimization, convex optimization, simulated annealing, discrete optimization, constraint programming, random search, and machine learning. These techniques are used to optimize the order for cutting the selected trees, taking into account the time required to cut each tree, the characteristics of the selected trees, the forest management goals and constraints, and the machine parameters 92.

[0074] 1.1.2.4. Harvesting Sequence

[0075] In some implementations, the perception computer 30 determines the harvesting sequence 34 based on the tree level information 110, the harvesting policy 90, and the sequencing algorithm 32. The harvesting sequence 34 is a sequence of trees to be cut, determined in an optimal order that takes into account various factors such as the characteristics of the surrounding trees, the forest management goals and constraints, and the machine parameters 92. The harvesting sequence 34 may also include the automatically planned movements of the forest harvester 100, such as driving to a specific location, extending the crane, and manipulating the harvester head, necessary to cut the trees in the determined order, and to place log assortments in suitable locations. For example, these may include selecting an optimal forest harvester 100 position considering crane reach and stability, driving instructions for forest harvester 100 to reach the desired position, positioning the forest harvester 100 to minimize repositioning between trees, calculating optimal crane extension path to target tree, positioning harvester head at optimal cutting height, align harvester head with tree stem for efficient grabbing, planning delimbing path based on branch structure, determining optimal bucking positions for log assortments, determining log placement locations, etc. The harvesting sequence 34 is continuously updated as the forest harvester 100 moves through the forest and the state of the surrounding trees changes. The harvesting sequence 34 is communicated to the control system 80, which coordinates the cutting of the selected trees according to the determined sequence.

[0076] 1 .1 .2.4.1 . A Set Of Trees And Corresponding Order In Which They Are To Be Cut

[0077] In some examples, the harvesting sequence 34 includes a set of trees and the corresponding order in which they are to be cut. The set of trees is determined by the sequence tree set algorithm, which is part of the sequencing algorithm 32. The order in which the trees are to be cut is determined by the sequence order algorithm, which is also part of the sequencing algorithm 32. The set of trees and the corresponding order are determined based on the tree level information 110, the harvesting policy 90, and the machine parameters 92. In addition to the tree order, the harvesting sequence 34 may also specify the planned movements of the forest harvester 100 to reach each tree and perform the cutting operation. This includes movements of the base machine, the crane, and the harvester head. The set of trees and the corresponding order, and optional planned movements, are continuously updated as the forest harvester 100 moves through the forest and the state of the surrounding trees changes.

[0078] 1 .2. Control System

[0079] In some configurations, the forest harvester 100 includes a control system 80. The control system 80 is a component of the forest harvester 100, as it is responsible for coordinating the cutting of trees according to the harvesting sequence 34. The control system 80 receives the harvesting sequence 34 from the perception computer 30 and coordinates the cutting of the selected trees. The control system 80 can be configured to control the forest harvester 100 directly if the forest harvester 100 is autonomous, or it can communicate the harvesting sequence 34 to a machine operator through a visual operator interface 40 if the forest harvester 100 is manually operated. The control system 80 is designed to handle complex operations and make intelligent decisions based on the harvesting sequence 34, the characteristics of the selected trees, the forest management goals and constraints, and the machine parameters 92. 1 .2.1 . Visual Operator Interface

[0080] In some examples, the control system 80 includes a visual operator interface 40. The visual operator interface 40 is a user-friendly interface that communicates the harvesting sequence 34 to a machine operator. The visual operator interface 40 includes a display 42, which presents the harvesting sequence 34 to the machine operator in a visual format. The visual operator interface 40 is designed to reduce the cognitive load on the operator, allowing them to easily understand and follow the recommended tree cutting sequence. The visual operator interface 40 also helps to improve the accuracy of tree selection and reduce the likelihood of error, as the operator can clearly see which trees to cut next. Furthermore, the visual operator interface 40 can serve as a training tool, providing real-time feedback and guidance to novice machine operators, leading to improved skill development and operational proficiency.

[0081] 1.2.1.1. Display

[0082] In some configurations, the visual operator interface 40 includes a display 42. The display 42 is a component of the visual operator interface 40, as it presents the harvesting sequence 34 to the machine operator in a visual format. The display 42 can be a variety of types, including a head-up display, augmented reality glasses, or a screen, depending on the specific requirements of the harvesting operation. The display 42 is designed to present the harvesting sequence 34 in a clear and easy-to- understand format, allowing the machine operator to quickly and easily see which trees to cut next. The display 42 is also designed to be easy to read and operate, with a user-friendly interface and intuitive controls.

[0083] 1.3. Machine Parameters

[0084] In some implementations, the forest harvester 100 operates in dependence on a variety of machine parameters 92. The machine parameters 92 include a variety of factors that can affect the operation of the forest harvester 100, including the machine speed, cutting tool position, cutting tool angle, cutting tool speed, cutting tool pressure, cutting tool rotation, machine position, machine stability, machine power, machine fuel consumption, and machine maintenance status. The machine parameters 92 are continuously monitored and adjusted as necessary to optimize the operation of the forest harvester 100. The machine parameters 92 can also influence the determination of the Harvesting Sequence 34, as they can affect the efficiency and effectiveness of the tree cutting process.

[0085] Examples of how the Machine parameters 92 can influence the time optimal sequence include:

[0086] - Different machines have different crane configurations (joint configuration, telescoping features, crane reach, crane mounting point on machine) which influences time to perform certain crane motions, which in turn influences the time optimal sequence.

[0087] - Machine size and weight capacity: a small machine may have to drive closer to a large tree in order to fell it safely, in a situation where a large machine may extend the crane from a stationary position.

[0088] - Machines of different shapes and sizes may need to cut down a different set of trees to traverse the forest.

[0089] 2. Optimal Tree Selection For Forest Thinning Method Details

[0090] In one example, the Forest Harvester 100 is used to implement a method for optimal tree selection for forest thinning. The method involves a series of steps that are carried out by the various components and systems of the Forest Harvester 100, including the Harvest Sequencing System 10, Perception System 20, Perception Computer 30, and Control system 80.

[0091] 2.1. Data Collection and Detection

[0092] In some implementations, the first step of the method involves collecting and detecting data from the surrounding environment using Sensors 22. The Sensors 22 are part of the Perception System 20 and are configured to collect a wide range of data from the surrounding environment, including data related to the characteristics of the surrounding trees, the spatial distribution of the trees, and other relevant environmental factors. The Sensors 22 can include a variety of sensor types, such as lidar Sensors 22, cameras, and other types of Sensors 22 suitable for collecting data from the surrounding environment. The data collected by the Sensors 22 is then processed by the Processor 26 to generate Tree level information 110.

[0093] 2.1.1. Role of Sensors in Data Collection

[0094] In some configurations, the Sensors 22 play a role in the data collection and detection step of the method. The Sensors 22 are responsible for collecting and detecting data from the surrounding environment, which is then processed to generate Tree level information 110. The Sensors 22 can include a variety of sensor types, such as lidar Sensors 22, cameras, and other types of Sensors 22 suitable for collecting data from the surrounding environment. The specific types of Sensors 22 used, as well as their specifications, can vary depending on the specific requirements of the harvesting operation.

[0095] 2.2. Data Processing and Tree Level Information Generation

[0096] In some examples, the next step of the method involves processing the collected data and generating Tree level information 110. This step is carried out by the Processor 26, which is part of the Perception Computer 30. The Processor 26 processes the data collected by the Sensors 22 using a Tree characterization algorithm 28, which is used to analyze the sensor data and extract relevant tree characteristics and attributes. The Tree level information 110 generated by the Processor 26 includes a variety of data related to the characteristics and attributes of the individual trees in the forest, such as tree age, tree volume, tree growth rate, tree diameter, tree height, tree species, tree health / damage status, overall tree shape, tree trunk shape, tree canopy shape, tree canopy volume, tree biomass, branch structure, tree carbon content, and tree carbon capture rate. The Tree level information 110 is used by the perception computer 30 to determine the harvesting sequence 34 based on the forest management goals and constraints. 2.2.1. Use of Tree Characterization Algorithm

[0097] In some configurations, the processor 26 utilizes a tree characterization algorithm 28 to process the sensor data and generate Tree level information 110. The tree characterization algorithm 28 is a complex algorithm that analyzes the sensor data and extracts relevant tree characteristics and attributes. The tree characterization algorithm 28 can include a variety of sub-algorithms or techniques, including machine learning algorithms, image processing algorithms, point cloud processing algorithms, classification algorithms, statistical algorithms, data fusion algorithms, and artificial intelligence algorithms. These algorithms and techniques are used to train models on large datasets of tree characteristics and their corresponding attributes, such as age, species, health, and spatial distribution. These models can then be used to accurately classify and analyze tree level information from the sensor data, enabling the system to make informed decisions on tree selection for thinning.

[0098] 2.3. Calculation and Recomputation of Harvesting Sequence

[0099] In some implementations, the next step of the method involves calculating the harvesting sequence 34 based on the Tree level information 110 and the harvesting policy 90. This step is carried out by the perception computer 30, which uses a sequencing algorithm 32 to determine the optimal sequence for cutting trees. The sequencing algorithm 32 takes into account a variety of factors, including the characteristics of the surrounding trees, the forest management goals and constraints, and the Machine parameters 92. The harvesting sequence 34 is continuously recomputed as the forest harvester 100 moves forward and the state of the trees around the machine changes.

[0100] 2.3.1. Role of Sequencing Algorithm and Harvesting Policy

[0101] In some examples, the sequencing algorithm 32 and the harvesting policy 90 play a role in the calculation and recomputation of the harvesting sequence 34. The sequencing algorithm 32 is a complex algorithm that determines the optimal sequence for cutting trees based on the tree level information 110 and the harvesting policy 90. The sequencing algorithm 32 includes several sub-algorithms or techniques, including the sequence tree set algorithm and sequence order algorithm, which are used to determine the set of trees to be included in the harvesting sequence 34 and the order in which the selected trees should be cut. The harvesting policy 90 sets the rules and guidelines for selecting the harvesting sequence 34 based on the forest management goals and constraints. The harvesting policy 90 can include a variety of targets, such as maximizing the growth rate of the trees, promoting biodiversity, enhancing wildlife habitat, minimizing soil erosion, improving water quality, optimizing timber quality, optimizing carbon storage, supporting sustainable harvesting, and improving aesthetics.

[0102] 2.4. Coordination of Tree Cutting

[0103] In some configurations, the next step of the method involves coordinating the cutting of the selected trees according to the determined harvesting sequence 34. This step is carried out by the control system 80, which receives the harvesting sequence 34 from the perception computer 30 and coordinates the cutting of the selected trees. The control system 80 can be configured to control the forest harvester 100 directly if the forest harvester 100 is autonomous, or it can communicate the harvesting sequence 34 to a machine operator through a visual operator interface 40 if the forest harvester 100 is manually operated.

[0104] 2.4.1. Role of Control System in Tree Cutting

[0105] In some examples, the control system 80 plays a role in the coordination of tree cutting. The control system 80 is responsible for coordinating the cutting of trees according to the harvesting sequence 34. The control system 80 receives the harvesting sequence 34 from the perception computer 30 and coordinates the cutting of the selected trees. The control system 80 can be configured to control the forest harvester 100 directly if the forest harvester 100 is autonomous, or it can communicate the harvesting sequence 34 to a machine operator through a visual operator interface 40 if the forest harvester 100 is manually operated.

[0106] 2.5. Autonomous and Manual Operation Modes In some implementations, the forest harvester 100 can be configured for either autonomous or manual operation. In the autonomous operation mode, the control system 80 controls the forest harvester 100 directly, coordinating the cutting of the selected trees according to the determined harvesting sequence 34 without the need for human intervention. In the manual operation mode, the control system 80 communicates the harvesting sequence 34 to a machine operator through a visual operator interface 40. The machine operator then manually operates the forest harvester 100 to cut the selected trees according to the communicated harvesting sequence 34.

[0107] 3. Description of Examples of the Disclosure

[0108] In one example, the forest harvester 100 is equipped with a lidar sensor as part of the sensors 22. The lidar sensor is used to collect data from the surrounding environment, including data related to the characteristics of the surrounding trees and the spatial distribution of the trees. The lidar sensor is capable of accurately measuring distances and generating detailed three-dimensional representations of the environment, making it an effective tool for collecting data in a forest environment.

[0109] In another example, the forest harvester 100 is equipped with a camera as part of the sensors 22. The camera is used to capture high-resolution images of the trees and their surroundings. The images captured by the camera are processed by the processor 26 using image processing techniques to extract relevant features and information, such as tree species, canopy cover, health / damage status, and geometric characteristics.

[0110] In yet another example, the forest harvester 100 is configured for autonomous operation. In this mode, the control system 80 controls the forest harvester 100 directly, coordinating the cutting of the selected trees according to the determined harvesting sequence 34 without the need for human intervention. This eliminates the need for a machine operator and optimizes the overall efficiency of the operation. In a further example, the forest harvester 100 is configured for manual operation. In this mode, the control system 80 communicates the harvesting sequence 34 to a machine operator through a visual operator interface 40. The machine operator then manually operates the forest harvester 100 to cut the selected trees according to the communicated harvesting sequence 34. This mode allows for human oversight and control of the harvesting operation, providing flexibility and adaptability in response to changing conditions or unexpected situations.

[0111] 3.1 . Example of Forest Harvester with Lidar Sensor

[0112] In one implementation, the forest harvester 100 is equipped with a lidar sensor as part of the sensors 22. The lidar sensor is a type of remote sensing technology that uses light in the form of a pulsed laser to measure distances. The lidar sensor emits laser pulses and measures the time it takes for the light to return after hitting an object, allowing it to calculate the distance to the object. This data is then used to create a detailed three-dimensional representation of the surrounding environment.

[0113] 3.1 .1 . Specifics of Lidar Sensor Use

[0114] In this configuration, the lidar sensor is used to collect data from the surrounding environment, including data related to the characteristics of the surrounding trees. The lidar sensor is capable of accurately measuring distances and generating detailed three-dimensional representations of the environment, making it an effective tool for collecting data in a forest environment. The lidar sensor can measure the height, diameter, and spatial distribution of the trees, as well as other characteristics such as the shape and density of the tree canopy. The data collected by the lidar sensor is processed by the processor 26 to generate tree level information 110, which is used to inform the decision-making process for tree selection and sequencing.

[0115] 3.2. Example of Forest Harvester with Camera Sensor

[0116] In another implementation, the forest harvester 100 is equipped with a camera as part of the sensors 22. The camera is a type of imaging sensor that captures high-resolution images of the trees and their surroundings. 3.2.1 . Specifics of Camera Sensor Use

[0117] In this configuration, the camera is used to capture visual data from the surrounding environment. The images captured by the camera are processed by the processor 26 using image processing techniques to extract relevant features and information. This can include color analysis to identify tree species, texture analysis to assess tree health or damage, and shape recognition algorithms to determine geometric characteristics such as tree height and diameter. The processed image data is used to generate tree level information 110, which is used to inform the decision-making process for tree selection and sequencing.

[0118] 3.3. Example of Autonomous Forest Harvester

[0119] In yet another implementation, the forest harvester 100 is configured for autonomous operation. In this mode, the control system 80 controls the forest harvester 100 directly, coordinating the cutting of the selected trees according to the determined harvesting sequence 34 without the need for human intervention.

[0120] 3.3.1. Specifics of Autonomous Operation

[0121] In this configuration, the control system 80 receives the harvesting sequence 34 from the perception computer 30 and coordinates the cutting of the selected trees. The control system 80 is capable of controlling the forest harvester 100 directly, allowing the machine to operate autonomously without the need for a human operator. This can improve the efficiency of the harvesting operation, as the machine can operate continuously without breaks and can make decisions based on the tree level information 110 and the harvesting policy 90 without the potential for human error. The control system 80 can also adjust the machine parameters 92 in real-time to optimize the operation of the forest harvester 100 based on the current conditions.

[0122] 3.4. Example of Manually Operated Forest Harvester In a further implementation, the forest harvester 100 is configured for manual operation. In this mode, the control system 80 communicates the harvesting sequence 34 to a machine operator through a visual operator interface 40.

[0123] 3.4.1 . Specifics of Manual Operation

[0124] In this configuration, the control system 80 communicates the harvesting sequence 34 to a machine operator through the visual operator interface 40. The visual operator interface 40 includes a display 42, which presents the harvesting sequence 34 to the machine operator in a visual format. The machine operator then manually operates the forest harvester 100 to cut the selected trees according to the communicated harvesting sequence 34. This mode allows for human oversight and control of the harvesting operation, providing flexibility and adaptability in response to changing conditions or unexpected situations. The visual operator interface 40 can also serve as a training tool, providing real-time feedback and guidance to novice machine operators, leading to improved skill development and operational proficiency.

[0125] Example 1 : A forest harvester 100 for cutting trees, comprising: a harvest sequencing system 10 configured to determine a harvesting sequence 34 of trees to be cut, comprising; a perception system 20 configured to collect data about the trees; a perception computer 30 for generating tree level information 110 for the trees and configured to determine the harvesting sequence 34 in dependence on the tree level information 110 and a harvesting policy 90; and a control system 80 configured to receive the harvesting sequence 34 from the perception computer 30 and coordinate the cutting of trees according to the harvesting sequence 34.

[0126] Example 2: The forest harvester 100 of example 1 , wherein the perception system 20 comprises sensors 22 configured to collect data from the surrounding environment. Example 3: The forest harvester 100 of example 2, wherein the sensors 22 comprise at least one of a lidar sensor and a camera.

[0127] Example 4: The forest harvester 100 of any one of examples 2 to 3, further comprising a processor 26 configured to process data from the sensors 22 to generate the tree level information 110 in dependence on a tree characterization algorithm 28.

[0128] Example 5: The forest harvester 100 of example 4, wherein the tree characterization algorithm 28 is selected from the group consisting of a rule-based algorithm, a machine learning algorithm, a genetic algorithm, a dynamic programming algorithm, and a multiobjective optimization algorithm.

[0129] Example 6: The forest harvester 100 of any preceding example, wherein the harvesting sequence 34 is further determined in dependence on machine parameters 92 of the forest harvester 100.

[0130] Example 7: The forest harvester 100 of any one of examples 1 to 6, wherein the harvesting policy 90 comprises at least one of the following targets: overall or per species diameter range or distribution, overall or per species height range or distribution, overall or per species tree age range or distribution, overall or per species spatial distribution, overall or per species volume ratio before / after, overall or per species basal area ratio before / after, overall or per species tree count ratio before / after, to be extracted or remaining wood volume, to be extracted or remaining carbon storage, carbon capture rate after harvesting, cutting lane width, cutting lane distance, cutting lane area, special considerations for certain species, species diversity, individual tree health or damage, individual tree growth rate, ecosystem services, fire risk mitigation, wind damage risk mitigation, ground damage risk mitigation, environmental regulations, preservation of areas of cultural value, and profitability of operation. Example 8: The forest harvester 100 of any preceding example, wherein the Perception Computer 30 comprises a Sequencing Algorithm 32 configured to determine the Harvesting Sequence 34 in dependence on the Tree level information 110 and the Harvesting Policy 90.

[0131] Example 9: The Forest Harvester 100 of example 8, wherein the Sequencing Algorithm 32 is further configured to determine the Harvesting Sequence 34 in dependence on Machine parameters 92 of the Forest Harvester 100.

[0132] Example 10: The Forest Harvester 100 of example 8 or 9, wherein the Sequencing Algorithm 32 is selected from the group consisting of a dynamic programming algorithm, a linear programming algorithm, a nonlinear optimization algorithm, a convex optimization algorithm, a gradient method, a simulated annealing algorithm, a discrete optimization algorithm, a constraint programming algorithm, a random search algorithm, and a machine learning algorithm.

[0133] Example 11 : The Forest Harvester 100 of any preceding example, the Control system 80 further comprising a Visual Operator Interface 40 configured to communicate the Harvesting Sequence 34 to an operator.

[0134] Example 12: The Forest Harvester 100 of example 11 , wherein the Visual Operator Interface 40 comprises a Display 42 selected from the group consisting of a head-up display, augmented reality glasses, and a screen.

[0135] Example 13: The Forest Harvester 100 of any one of examples 1 to 9, wherein the Control system 80 comprises a machine control system configured to receive the Harvesting Sequence 34 from the Perception Computer 30 and control the Forest Harvester 100 to perform cutting of the trees according to the Harvesting Sequence 34 autonomously.

[0136] Example 14: The Forest Harvester 100 of any one of examples 1 to 13, wherein the Tree level information 110 comprises at least one of tree age, tree diameter, tree height, tree volume, tree growth rate, tree species, tree health / damage status, overall tree shape, tree trunk shape, tree canopy shape, tree canopy volume, branch structure, tree biomass, and tree carbon content, tree carbon capture rate.

[0137] Example 15: A method for optimal tree selection for forest thinning using the Forest Harvester 100 according to any one of examples 1 to 14, comprising the steps of: collecting and detecting data from the surrounding environment using Sensors 22; processing the collected data using a Tree characterization algorithm 28 to generate Tree level information 110; calculating the Harvesting Sequence 34 based on the tree level information 110 and the harvesting policy 90; continuously recomputing the harvesting sequence 34 as the forest harvester 100 moves forward and the state of the trees around the machine changes; and coordinating the cutting of the selected trees using a control system 80 following the determined harvesting sequence 34.

[0138] Example 16: The method according to example 15, further comprising the step of allowing the control system 80 to control the forest harvester 100 directly if the forest harvester 100 is autonomous.

[0139] Example 17: The method according to examples 15 or 16, further comprising the step of communicating the harvesting sequence 34 to a machine operator through a visual operator interface 40 if the forest harvester 100 is manually operated.

[0140] Example 18: A harvest sequencing system 10 for generating tree level information 110 for a set of trees, comprising: sensors 22 configured to collect data from the surrounding environment; and a processor 26 configured to: process data from the sensors 22 to generate the tree level information 110 in dependence on a tree characterization algorithm 28, and determine a harvesting sequence 34 of trees to be cut in dependence on the tree level information 110 and a harvesting policy 90, wherein the sensors 22 comprise at least one of a lidar sensor and a camera.

[0141] Example 19: The harvest sequencing system 10 according to example 18, wherein the tree characterization algorithm 28 is selected from the group consisting of a rule-based algorithm, a machine learning algorithm, a genetic algorithm, a dynamic programming algorithm, and a multi-objective optimization algorithm.

[0142] The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. It will be further understood that the terms "comprises," "comprising," "includes," and / or "including" when used herein specify the presence of stated features, integers, actions, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, actions, steps, operations, elements, components, and / or groups thereof.

[0143] It will be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the present disclosure.

[0144] Relative terms such as "below" or "above" or "upper" or "lower" or "horizontal" or "vertical" may be used herein to describe a relationship of one element to another element as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0145] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0146] It is to be understood that the present disclosure is not limited to the aspects described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the present disclosure and appended claims. In the drawings and specification, there have been disclosed aspects for purposes of illustration only and not for purposes of limitation, the scope of the disclosure being set forth in the following claims.

Claims

Claims1 . A forest harvester (100) for cutting trees, comprising: a harvest sequencing system (10) configured to determine a harvesting sequence (34) of trees to be cut, comprising; a perception system (20) configured to collect data about the trees, wherein the data comprises at least one of tree age, tree diameter, tree height, tree volume, tree growth rate, tree species, tree health / damage status, overall tree shape, tree trunk shape, tree canopy shape, tree canopy volume, branch structure, tree biomass, tree carbon content, tree carbon capture rate; a perception computer (30) for generating tree level information (110) for the trees and configured to determine and continuously update the harvesting sequence (34) in dependence on the tree level information (110) and a harvesting policy (90); and a control system (80) configured to receive the harvesting sequence (34) from the perception computer (30) and coordinate the cutting of trees according to the harvesting sequence (34).

2. The forest harvester (100) according to claim 1 , wherein the perception system (20) comprises sensors (22) configured to collect data from the surrounding environment.

3. The forest harvester (100) according to claim 2, wherein the sensors (22) comprise at least one of a lidar sensor and a camera.

4. The forest harvester (100) according to any one of claims 2 to 3, further comprising a processor (26) configured to process data from the sensors (22) to generate the tree level information (110) in dependence on a tree characterization algorithm (28).

5. The forest harvester (100) according to claim 4, wherein the tree characterization algorithm (28) is selected from the group consisting of a rule-based algorithm, a machine learning algorithm, a genetic algorithm, a dynamic programming algorithm, and a multi-objective optimization algorithm.

6. The forest harvester (100) according to any preceding claim, wherein the harvesting sequence (34) is further determined in dependence on machine parameters (92) of the forest harvester (100).

7. The forest harvester (100) according to any preceding claim, wherein the perception computer (30) comprises a sequencing algorithm (32) configured to determine the harvesting sequence (34) in dependence on the tree level information (110) and the harvesting policy (90).

8. The forest harvester (100) according to claim 7, wherein the sequencing algorithm (32) is further configured to determine the harvesting sequence (34) in dependence on machine parameters (92) of the forest harvester (100).

9. The forest harvester (100) according to any preceding claim, the control system (80) further comprising a visual operator interface (40) configured to communicate the harvesting sequence (34) to an operator.

10. The forest harvester (100) according to claim 9, wherein the visual operator interface (40) comprises a display (42) selected from the group consisting of a head- up display, augmented reality glasses, and a screen.

Images