Cooperative guidance method for complex terrain multi-vehicle topology collision avoidance corridor
Patent Information
- Application Number
- CN202611011212.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-08
- Publication Date
- 2026-10-09
AI Technical Summary
[0004]为了解决现有优先级轨迹规划方法中高优先级探测器轨迹可能过度占用低优先级探测器可行区域、导致后续规划不可行，以及规划轨迹难以直接转化为安全可执行制导指令的问题，本发明的目的是提供一种复杂地形多器拓扑避碰走廊协同制导方法，该方法通过拓扑避碰走廊完成多探测器协同轨迹规划，以所选轨迹为参考轨迹生成跟踪制导指令，使各探测器按照规划轨迹安全到达目标区域
[0072]1、本发明公开的复杂地形多器拓扑避碰走廊协同制导方法，在当前探测器的时变可行区域内生成多个拓扑不同的候选拓扑避碰走廊，用于表示复杂危险地形中的不同可行分支。相比仅沿单一路径或单一初始猜测进行轨迹规划，本发明能够为探测器提供多个候选规划空间，降低轨迹规划陷入局部不可行或局部较差解的风险，提高复杂障碍环境下多探测器轨迹规划的可行性和鲁棒性。
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Figure CN122883976A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a multi-detector cooperative guidance method, and more particularly to a multi-detector topological collision avoidance corridor cooperative guidance method for complex terrain, belonging to the field of deep space exploration technology. Background Technology
[0002] Extraterrestrial objects hold immense scientific value and are crucial for studying key scientific questions such as the formation and evolution of the solar system, the origin of celestial bodies, and the origin of life. Compared to single probes, multi-probe systems can improve the reliability of planetary exploration missions through information sharing, cooperative navigation, and mission redundancy, and can acquire richer topographic and scientific information. Therefore, multi-probe collaborative exploration is an important development direction for future deep space exploration missions.
[0003] When performing multi-detector detection missions in complex and hazardous terrain, collaborative guidance is required. This involves planning and tracking the trajector trajector to ensure that each detector reaches the target area while satisfying dynamic constraints, thrust constraints, terrain obstacle avoidance constraints, and inter-detector collision avoidance constraints. Existing multi-detector trajectory planning methods mainly include centralized planning, distributed planning, and priority planning. Among these, centralized planning has high computational costs, distributed planning is prone to introducing conservative constraints in complex obstacle environments, and while priority planning is computationally efficient, high-priority detectors may over-occupy the feasible areas of low-priority detectors by selecting trajectories based solely on their own fuel consumption, rendering subsequent detector trajectory planning infeasible. Therefore, a collaborative guidance method for multi-detector missions in complex and hazardous terrain is needed. This method should ensure that high-priority detectors consider not only their own fuel consumption but also their impact on the feasible areas of low-priority detectors when selecting trajectories. Furthermore, the collaboratively planned trajectories should be transformed into executable tracking and guidance commands, thereby improving the feasibility, safety, and reliability of multi-detector collaborative missions. Summary of the Invention
[0004] To address the problems in existing priority trajectory planning methods, such as high-priority detector trajectories potentially over-occupying the feasible area of low-priority detectors, leading to infeasibility of subsequent planning, and the difficulty in directly converting planned trajectories into safe and executable guidance commands, the present invention aims to provide a multi-detector topology collision avoidance corridor cooperative guidance method for complex terrain. This method completes multi-detector cooperative trajectory planning through a topology collision avoidance corridor, generates tracking guidance commands using the selected trajectory as a reference trajectory, and enables each detector to safely reach the target area according to the planned trajectory.
[0005] The objective of this invention is achieved through the following technical solution.
[0006] This invention discloses a multi-probe topology collision avoidance corridor cooperative guidance method for complex terrain. Based on a given planetary topographic map, the initial and final states of each probe, flight time, thrust upper limit, safety radius, uncertainty margin, and probe planning priority, a multi-probe trajectory planning model is constructed. The trajector ... By minimizing the coordination index, the current probe can adaptively select a final corridor and trajectory that balances its own fuel consumption and the preservation of feasible areas for subsequent probes. Using this collaboratively planned trajectory as a reference trajectory for each probe, the tracking error between the actual state and the reference state is calculated, and thrust or acceleration guidance commands are generated to enable each probe to complete a collaborative tracking landing according to the reference trajectory.
[0007] The cooperative guidance method for multi-device topology collision avoidance corridors in complex terrain disclosed in this invention includes the following steps:
[0008] Step 1: In the fixed coordinate system of the extraterrestrial body, construct the dynamic equations of the multi-detector system. Based on the given obstacles, establish state, control, and dynamic and static collision avoidance constraints to construct the multi-detector trajectory planning problem.
[0009] The multi-detector trajectory planning problem described in step one is as follows:
[0010]
[0011] in, Indicates the number of detectors performing the mission. , , and These represent its position vector, velocity vector, mass vector, and thrust vector, respectively. , The vector of gravitational acceleration of extraterrestrial objects. The vector of the rotational angular velocity of an extraterrestrial body. and The first The initial position and initial velocity of each detector. and The first The target position and target velocity of each detector For the first A given flight time for a detector, This is the upper limit of the probe thrust amplitude. For barrier-free spaces that do not include dangerous terrain, The safety radius is defined as follows. Since the planning problem includes both static terrain obstacle avoidance constraints and inter-detector collision avoidance constraints, and there are coupling relationships between different detector trajector trajectories, this problem is characterized by high dimensionality and non-convexity. To improve solution efficiency, a priority planning framework is adopted in subsequent steps, and adaptive topological corridor coordination is used to achieve collaborative generation of multi-detector trajector trajectories.
[0012] Step 2: Use a priority planning framework to determine the planning order of detectors, and combine the reachable area, unobstructed space and the area occupied by the planned detectors to construct the time-varying feasible area of the current detector;
[0013] Let the planning priority order for multiple detectors be as follows: That is, detectors with smaller numbers have higher planning priority; the flight time is discretized into several moments. , ;make Indicates the first Each detector at discrete time The reachable region is defined by dynamic constraints, thrust constraints, and boundary conditions. Let Indicates the first A high-priority detector at discrete time The selected occupied corridor is defined as the first... The high-priority detector is relative to the first The area occupied by each detector is
[0014]
[0015] At this time, the The time-varying feasible region of each detector is
[0016]
[0017] Step 3: Generate topologically different guiding paths within the time-varying feasible region of the current detector, and construct candidate topological corridors by combining the safety radius and uncertainty margin.
[0018] Based on the time-varying feasible region of the current detector obtained in step two This generates multiple guidance paths with different topologies. These guidance paths describe different feasible branches for the current detector to move from the initial region to the target region, ensuring that subsequent trajectory optimization is not limited to a single local path.
[0019] For the Each detector has a set of guidance paths represented as...
[0020]
[0021] in, Indicates the first The first detector A guiding path, , This represents the number of topological branches generated in the current feasible region of the detector.
[0022] Along the guiding path Sampling was performed to obtain
[0023]
[0024] in, Indicates the first The first on the guiding path One sampling point, , This represents the number of sampling points along the guidance path. Each sampling point corresponds to a reference time node. .
[0025] For each sampling point Calculate its time at the reference time node Euclidean distance to the nearest obstacle The local corridor radius is determined based on the nearest obstacle distance, the detector's safe radius, and the uncertainty margin.
[0026]
[0027] in, To preset the maximum corridor radius, For the first Uncertainty margin of each detector;
[0028] Based on sampling points and corresponding local corridor radius Constructing local spherical corridor units
[0029]
[0030] Multiple local spherical corridor units generated along the same guiding path are arranged in path order to obtain the first... Candidate topological corridors
[0031]
[0032] Step 4: Transform the non-convex obstacle avoidance constraint into a convex corridor constraint within the candidate topology corridor, and solve for the candidate trajectory of the current detector.
[0033] Based on the candidate topological corridor set obtained in step three, for the current... Each detector performs trajectory optimization within each candidate topological corridor; for the first detector... For each candidate topological corridor, the original static terrain obstacle avoidance constraints and inter-detector collision avoidance constraints are replaced by corridor constraints, i.e.
[0034]
[0035] Since the candidate topological corridor consists of a series of local spherical corridor units, the trajectory is discretized according to the local spherical corridor units in the candidate topological corridor; let the th... The time nodes corresponding to the candidate topological corridors are
[0036]
[0037] Then in the At each time point, the detector position satisfies the following convex corridor constraint.
[0038]
[0039] Equation (11) is a 2-norm convex constraint;
[0040] For the The first detector Given ...
[0041]
[0042] After solving equation (12), we obtain the first... Candidate trajectories and their corresponding fuel consumptions within candidate topological corridors; candidate trajectories are denoted as...
[0043]
[0044] Let the set of candidate topological corridor numbers that can generate feasible candidate trajectories be denoted as . Then the first The set of feasible candidate trajectories for each detector is
[0045]
[0046] Step 5: Construct a coordinated index that balances fuel consumption and the preservation of feasible areas for low-priority detectors, and select the final corridor and final trajectory for the current detector.
[0047] For each candidate topological corridor, a coordination index is constructed to ensure that the current detector considers both its own fuel consumption and the impact on the feasible region of subsequent detectors when selecting a trajectory; for any low-priority detector... ,in , order the Before the detector selects a candidate corridor, the first Each detector at time The feasible area is
[0048]
[0049] Therefore, the first The first detector The candidate topological corridor pairs with the first The occupancy index of the feasible region of a detector is defined as follows:
[0050]
[0051] in, Indicates volume measurement, To prevent positive numbers with a denominator of zero;
[0052] Furthermore, the total occupancy index of the candidate topology corridor for all low-priority detectors is defined as follows:
[0053]
[0054] in, These are weighting coefficients, used to indicate the retention of the first... The importance of the feasible region for each low-priority detector;
[0055] To unify the evaluation of candidate trajectory fuel consumption and feasible area occupancy indicators, the first... The fuel consumption of each feasible candidate trajectory for each detector is normalized; for Normalized fuel consumption is defined as
[0056]
[0057] in, To prevent positive numbers with a denominator of zero
[0058]
[0059] Therefore, the first The coordination index for each candidate topological corridor is constructed as follows:
[0060]
[0061] in, The tradeoff coefficient between fuel consumption and the preservation of feasible areas; when When the candidate corridor selection degenerates to only consider the current detector's own fuel consumption; when When the size increases, the current detector tends to choose a corridor that occupies less of the feasible area for low-priority detectors;
[0062] Select the current number based on the coordination indicators. The final corridor number of each detector
[0063]
[0064] The corresponding final trajectory and final corridor are respectively
[0065]
[0066] After the final corridor of the current probe is determined, in the... At each reference time point, the currently occupied corridor of the detector is recorded as...
[0067]
[0068] Repeat steps two through five in order of priority until all detectors have completed trajectory planning, resulting in a multi-detector cooperative trajectory set.
[0069]
[0070] Step 6: Each detector generates corresponding guidance and control commands based on the final trajectory obtained from the plan, and tracks the final trajectory to achieve safe and collaborative detection.
[0071] Beneficial effects:
[0072] 1. The multi-detector topological collision avoidance corridor cooperative guidance method for complex terrain disclosed in this invention generates multiple candidate topological collision avoidance corridors with different topologies within the time-varying feasible region of the current detector, which are used to represent different feasible branches in complex and dangerous terrain. Compared with trajectory planning along only a single path or a single initial guess, this invention can provide the detector with multiple candidate planning spaces, reduce the risk of trajectory planning getting stuck in local infeasibility or local poor solutions, and improve the feasibility and robustness of multi-detector trajectory planning in complex obstacle environments.
[0073] 2. The multi-device topology collision avoidance corridor cooperative guidance method for complex terrain disclosed in this invention transforms the original non-convex obstacle avoidance constraints into convex corridor constraints within each candidate topology collision avoidance corridor, and solves for candidate trajectories under the convex corridor constraints. By transforming the complex terrain obstacle avoidance constraints and inter-detector collision avoidance constraints into easily tractable convex constraints, the difficulty of solving the trajectory optimization problem can be reduced, and the efficiency of candidate trajectory generation can be improved. This method is suitable for the rapid planning requirements of multi-detector cooperative detection tasks in complex and dangerous terrain.
[0074] 3. The multi-device topology collision avoidance corridor cooperative guidance method for complex terrain disclosed in this invention constructs a coordination index that simultaneously considers the fuel consumption of candidate trajectories and the degree to which candidate corridors occupy the feasible area of low-priority detectors. This ensures that high-priority detectors, when selecting trajectories, not only consider their own fuel consumption but also the impact on the planning space of subsequent detectors. This reduces the excessive occupation of the feasible area of low-priority detectors by high-priority detectors, lowering the risk of infeasible trajectory planning for subsequent detectors. Simultaneously, each detector can generate guidance and control commands based on the final planned trajectory and perform tracking, thereby improving the safety and executability of multi-detector cooperative guidance missions. Attached Figure Description
[0075] Figure 1 Flowchart of a collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain;
[0076] Figure 2 The corridor and trajectory ultimately selected for the three detectors;
[0077] Figure 3 The curves showing the position and velocity changes during the coordinated detection process of the three detectors;
[0078] Figure 4 The curves represent the minimum distance between the detector and the static obstacle, as well as the minimum distance between the detectors. Detailed Implementation
[0079] To better illustrate the purpose and advantages of the present invention, the following description, in conjunction with an example and corresponding drawings, further explains the invention.
[0080] like Figure 1As shown in the figure, the adaptive topology corridor coordination multi-detector trajectory planning method disclosed in this embodiment has the following specific implementation steps:
[0081] Step 1: In the fixed coordinate system of the extraterrestrial body, construct the dynamic equations of the multi-detector system. Based on the given obstacles, establish state, control, and dynamic and static collision avoidance constraints to construct the multi-detector trajectory planning problem.
[0082] In a fixed coordinate system for extraterrestrial objects, consider a total of three detectors performing a collaborative detection mission. For the first... One detector, making , , and Let their position vector, velocity vector, mass vector, and thrust vector be respectively represented, where, . No. The dynamic equations of the detectors are as follows
[0083]
[0084] in, The vector of gravitational acceleration of extraterrestrial objects. This is the angular velocity vector of the rotation of an extraterrestrial body. Each detector needs to transition from a given initial state to a given target state, and its initial and final states are as follows:
[0085]
[0086] in, and The first The initial position and initial velocity of each detector. and The first The target position and target velocity of each detector For the first The given flight times for each detector are as follows: In this embodiment, detector 1 has an initial position of [-1260, -210, 520] m, an initial velocity of [19, 4, -1] m / s, a target position of [1260, 210, 0] m, and a flight time of 290 s; detector 2 has an initial position of [920, 1120, 470] m, an initial velocity of [-14, -15, -1] m / s, a target position of [-1040, -940, 0] m, and a flight time of 300 s; detector 3 has an initial position of [1120, -980, 430] m, an initial velocity of [-14, 14, -1] m / s, a target position of [-900, 1010, 0] m, and a flight time of 295 s. The terminal velocities of all three detectors are set to zero.
[0087] The probe thrust amplitude satisfies the constraints
[0088]
[0089] in, This represents the upper limit of the probe's thrust amplitude.
[0090] Based on the given planetary topographic map, hazardous areas are delineated, and spaces that do not contain hazardous terrain are defined as barrier-free spaces. The probe needs to meet static terrain obstacle avoidance constraints during flight.
[0091]
[0092] To avoid collisions between different detectors, each detector is equivalent to having a safe radius. For a sphere, collision avoidance constraints must be satisfied between any two different detectors.
[0093]
[0094] Using fuel consumption as a performance indicator for the trajectory, the first The fuel consumption of each detector is defined as
[0095]
[0096] Therefore, the multi-detector trajectory planning problem can be described as follows: under the conditions of satisfying detector dynamics constraints, initial and final state constraints, thrust amplitude constraints, static terrain obstacle avoidance constraints, and inter-detector collision avoidance constraints, optimize the position, velocity, and thrust of each detector to ensure that all detectors safely reach their respective target areas and reduce total fuel consumption. This problem can be expressed as...
[0097]
[0098] in, Because the planning problem described above includes both static terrain obstacle avoidance constraints and inter-detector collision avoidance constraints, and there are coupling relationships between different detector trajector trajectories, the problem is characterized by high dimensionality and non-convexity. To improve solution efficiency, a priority planning framework is adopted in subsequent steps, and adaptive topological corridor coordination is used to achieve the collaborative generation of multi-detector trajector trajectories.
[0099] Step 2: Use a priority planning framework to determine the planning order of detectors, and combine the reachable area, unobstructed space and the area occupied by the planned detectors to construct the time-varying feasible area of the current detector;
[0100] Let the planning priority order for multiple detectors be as follows: That is, detectors with smaller numbers have higher planning priority; the flight time is discretized into several moments. , ;make Indicates the first Each detector at discrete time The reachable region is defined by dynamic constraints, thrust constraints, and boundary conditions. Let Indicates the first A high-priority detector at discrete time The selected corridor is defined as the first one. The high-priority detector is relative to the first The area occupied by each detector is
[0101]
[0102] At this time, the The time-varying feasible region of each detector is
[0103]
[0104] Step 3: Generate topologically different guiding paths within the time-varying feasible region of the current detector, and construct candidate topological corridors by combining the safety radius and uncertainty margin.
[0105] Based on the time-varying feasible region of the current detector obtained in step two This generates multiple guidance paths with different topologies. These guidance paths describe different feasible branches for the current detector to move from the initial region to the target region, ensuring that subsequent trajectory optimization is not limited to a single local path.
[0106] For the Each detector has a set of guiding paths represented as...
[0107]
[0108] in, Indicates the first The first detector Guided path, , This represents the number of topological branches generated in the current feasible region of the detector.
[0109] Along the guiding path Sampling was performed to obtain
[0110]
[0111] in, Indicates the first The first on the guiding path One sampling point, , This represents the number of sampling points along the guidance path. Each sampling point corresponds to a reference time node. .
[0112] For each sampling point Calculate its time at the reference time node Euclidean distance to the nearest obstacle The local corridor radius is determined based on the nearest obstacle distance, the detector's safe radius, and the uncertainty margin.
[0113]
[0114] in, To preset the maximum corridor radius, For the first Uncertainty margin of each detector;
[0115] Based on sampling points and corresponding local corridor radius Constructing local spherical corridor units
[0116]
[0117] Multiple local spherical corridor units generated along the same guiding path are arranged in path order to obtain the first... Candidate topological corridors
[0118]
[0119] Step 4: Transform the non-convex obstacle avoidance constraint into a convex corridor constraint within the candidate topology corridor, and solve for the candidate trajectory of the current detector.
[0120] Based on the candidate topological corridor set obtained in step three, for the current... Each detector performs trajectory optimization within each candidate topological corridor; for the first detector... For each candidate topological corridor, the original static terrain obstacle avoidance constraints and inter-detector collision avoidance constraints are replaced by corridor constraints, i.e.
[0121]
[0122] Since the candidate topological corridor consists of a series of local spherical corridor units, the trajectory is discretized according to the local spherical corridor units in the candidate topological corridor; let the th... The time nodes corresponding to the candidate topological corridors are
[0123]
[0124] Then in the At each time point, the detector position satisfies the following convex corridor constraint.
[0125]
[0126] Equation (41) is a convex constraint of the second norm;
[0127] For the The first detector Given ...
[0128]
[0129] After solving equation (42), we obtain the first... Candidate trajectories and their corresponding fuel consumptions within candidate topological corridors; candidate trajectories are denoted as...
[0130]
[0131] Let the set of candidate topological corridor numbers that can generate feasible candidate trajectories be denoted as . Then the first The set of feasible candidate trajectories for each detector is
[0132]
[0133] Step 5: Construct a coordinated index that balances fuel consumption and the preservation of feasible areas for low-priority detectors, and select the final corridor and final trajectory for the current detector.
[0134] For each candidate topological corridor, a coordination index is constructed to ensure that the current detector considers both its own fuel consumption and the impact on the feasible region of subsequent detectors when selecting a trajectory; for any low-priority detector... ,in , order the Before the detector selects a candidate corridor, the first Each detector at time The feasible area is
[0135]
[0136] Therefore, the first The first detector The candidate topological corridor for the first The occupancy index of the feasible region of a detector is defined as follows:
[0137]
[0138] in, Indicates volume measurement, To prevent positive numbers with a denominator of zero;
[0139] Furthermore, the total occupancy index of the candidate topology corridor for all low-priority detectors is defined as follows:
[0140]
[0141] in, These are weighting coefficients, used to indicate the retention of the first... The importance of the feasible region for each low-priority detector;
[0142] To unify the evaluation of candidate trajectory fuel consumption and feasible area occupancy indicators, the first... The fuel consumption of each feasible candidate trajectory for each detector is normalized; for Normalized fuel consumption is defined as
[0143]
[0144] in, To prevent positive numbers with a denominator of zero
[0145]
[0146] Therefore, the first The coordination index for each candidate topological corridor is constructed as follows:
[0147]
[0148] in, The tradeoff coefficient between fuel consumption and the preservation of feasible areas; when When the candidate corridor selection degenerates to only consider the current detector's own fuel consumption; when When the size increases, the current detector tends to choose a corridor that occupies less of the feasible area for low-priority detectors;
[0149] Select the current number based on the coordination indicators. The final corridor number of each detector
[0150]
[0151] The corresponding final trajectory and final corridor are respectively
[0152]
[0153] After the final corridor of the current probe is determined, in the... At each reference time point, the currently occupied corridor of the detector is recorded as...
[0154]
[0155] Repeat steps two through five in order of priority until all detectors have completed trajectory planning, resulting in a multi-detector cooperative trajectory set.
[0156]
[0157] Step 6: Each detector generates corresponding guidance and control commands based on the final trajectory obtained from the plan, and tracks the final trajectory to achieve safe and collaborative detection.
[0158] Figure 2 The proposed method demonstrates the corridor and trajectory ultimately selected by the three detectors. Figure 3 The curves showing the position and velocity changes of the three detectors during the collaborative detection process are displayed; Figure 4 The minimum distance between the detector and a static obstacle, as well as the minimum distance between detectors, are shown in the curves. The results demonstrate that this invention improves the feasibility of multi-device cooperative guidance and ensures obstacle avoidance performance.
[0159] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A cooperative guidance method for multi-device topology collision avoidance corridors in complex terrain, characterized by: Includes the following steps, Step 1: In the fixed coordinate system of the extraterrestrial body, construct the dynamic equations of the multi-detector system. Based on the given obstacles, establish state, control, and dynamic and static collision avoidance constraints to construct the multi-detector trajectory planning problem. Step 2: Use a priority planning framework to determine the planning order of detectors, and combine the reachable area, unobstructed space and the area occupied by the planned detectors to construct the time-varying feasible area of the current detector; Step 3: Generate topologically different guidance paths within the time-varying feasible region of the current detector, and construct candidate topological corridors by combining the safety radius and uncertainty margin; Step 4: Within the candidate topology corridor, transform the multi-detector trajectory planning problem from Step 1 into a convex optimization problem, and solve for the candidate trajectory of the current detector; Step 5: Construct a coordinated index that balances fuel consumption and preservation of feasible regions for low-priority detectors, and select the final corridor and final trajectory of the current detector from the candidate trajectories in Step 4. Step Six: Take the final corridor obtained in Step Five and bring it into Step Two. Repeat Steps Two to Five. Each detector generates corresponding guidance and control commands based on the final trajectory obtained from the plan, and tracks the final trajectory to achieve safe and collaborative detection.
2. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 1, characterized in that: The multi-detector trajectory planning problem described in step one is as follows: in, Indicates the number of detectors performing the mission. , , and These represent its position vector, velocity vector, mass vector, and thrust vector, respectively. , The vector of gravitational acceleration of extraterrestrial objects. The vector of the rotational angular velocity of an extraterrestrial body. and The first The initial position and initial velocity of each detector. and The first The target position and target velocity of each detector For the first A given flight time for a detector, This is the upper limit of the probe thrust amplitude. For barrier-free spaces that do not include dangerous terrain, For the safety radius.
3. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 2, characterized in that: The specific implementation method for step two is as follows: The planning priority order for multiple detectors is as follows: That is, detectors with smaller numbers have higher planning priority; Discretize the flight time into several moments. , ;make Indicates the first Each detector at discrete time The reachable region is defined by dynamic constraints, thrust constraints, and boundary conditions. Let Indicates the first A high-priority detector at discrete time The selected occupied corridor is defined as the first... The high-priority detector is relative to the first The area occupied by each detector is At this time, the The time-varying feasible region of each detector is 。 4. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 3, characterized in that: The method for generating different topological boot paths described in step three is as follows: Based on the time-varying feasible region of the current detector obtained in step two This generates multiple boot paths with different topologies; For the Each detector has a set of guidance paths represented as... in, Indicates the first The first detector A guiding path, , This represents the number of topological branches generated in the current feasible region of the detector.
5. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 4, characterized in that: Step three describes the method for constructing candidate topological corridors by combining the safety radius and uncertainty margin. Along the guiding path Sampling was performed to obtain in, Indicates the first The first on the guiding path One sampling point, , This represents the number of sampling points along the guidance path; each sampling point corresponds to a reference time node. ; For each sampling point Calculate its time at the reference time node Euclidean distance to the nearest obstacle The local corridor radius is determined based on the nearest obstacle distance, the detector's safe radius, and the uncertainty margin. in, To preset the maximum corridor radius, For the first Uncertainty margin of each detector; Based on sampling points and corresponding local corridor radius Constructing local spherical corridor units Multiple local spherical corridor units generated along the same guiding path are arranged in path order to obtain the first... Candidate topological corridors 。 6. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 5, characterized in that: The method described in step four for transforming the multi-detector trajectory planning problem from step one into a convex optimization problem within the candidate topology corridor is as follows: Based on the candidate topological corridor set obtained in step three, for the current... Each detector performs trajectory optimization within each candidate topological corridor; for the first detector... For each candidate topological corridor, the original static terrain obstacle avoidance constraints and inter-detector collision avoidance constraints are replaced by corridor constraints, i.e. Since the candidate topological corridor consists of a series of local spherical corridor units, the trajectory is discretized according to the local spherical corridor units in the candidate topological corridor; let the th... The time nodes corresponding to the candidate topological corridors are Then in the At each time point, the detector position satisfies the following convex corridor constraint. Equation (19) is a convex constraint of the second norm; For the The first detector Given ... 。 7. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 6, characterized in that: Step four describes the method for solving the candidate trajectory of the current detector. After solving equation (20), we obtain the first... Candidate trajectories within each candidate topological corridor and their corresponding fuel consumption; Candidate trajectories are denoted as Let the set of candidate topological corridor numbers that can generate feasible candidate trajectories be denoted as . Then the first The set of feasible candidate trajectories for each detector is 。 8. The collaborative guidance method for multi-device topology collision avoidance corridors in complex terrain as described in claim 7, characterized in that: The implementation method for step five is as follows: For each candidate topological corridor, a coordination index is constructed to ensure that the current detector considers both its own fuel consumption and the impact on the feasible region of subsequent detectors when selecting a trajectory; for any low-priority detector... ,in , order the Before the detector selects a candidate corridor, the first Each detector at time The feasible area is Therefore, the first The first detector The candidate topological corridor pairs with the first The occupancy index of the feasible region of a detector is defined as follows: in, Indicates volume measurement, To prevent positive numbers with a denominator of zero; The total occupancy index of candidate topology corridors for all low-priority detectors is defined as follows: in, These are weighting coefficients, used to indicate the retention of the first... The importance of the feasible region for each low-priority detector; To unify the evaluation of candidate trajectory fuel consumption and feasible area occupancy indicators, the first... The fuel consumption of each feasible candidate trajectory for each detector is normalized; for Normalized fuel consumption is defined as in, To prevent positive numbers with a denominator of zero Therefore, the first The coordination index for each candidate topological corridor is constructed as follows: in, The tradeoff coefficient between fuel consumption and the preservation of feasible areas; when When the candidate corridor selection degenerates to only consider the current detector's own fuel consumption; when When the size increases, the current detector tends to choose a corridor that occupies less of the feasible area for low-priority detectors; Select the current number based on the coordination indicators. The final corridor number of each detector The corresponding final trajectory and final corridor are respectively After the final corridor of the current probe is determined, in the... At each reference time point, the currently occupied corridor of the detector is recorded as... Repeat steps two through five in order of priority until all detectors have completed trajectory planning, resulting in a multi-detector cooperative trajectory set. 。