Multi-source cooperative rocker arm-tracked robot for earthquake search and rescue

Abstract

The invention discloses a multi-source cooperative rocker arm-tracked robot composite mechanical structure for earthquake search and rescue, and belongs to the technical field of intelligent rescue equipment. A rocker arm-crawler belt-wheel three-dimensional motion structure system is innovatively designed, a multi-degree-of-freedom rocker arm structure driven by a steering engine is adopted in the middle of the robot, and a double-crawler belt differential driving module driven by a motor is arranged at the bottom of the robot; a swing arm balance wheel set is arranged on the front portion to assist climbing operation. According to the composite structure, a posture stability control algorithm is established through a mass center dynamic model, and intelligent switching of three motion modes of wheel type high-speed maneuvering, crawler belt obstacle crossing and rocker arm climbing can be achieved. Tests show that the structure can enable the robot to stably cross a 100 mm vertical obstacle, good posture stability is kept in unstructured terrains such as gravel and rubble, and meanwhile the environment is sensed through various sensors. Compared with a traditional single motion structure, the multi-dimensional maneuvering ability in the complex post-disaster environment is achieved through mechanical structure innovation, and the terrain adaptability of rescue equipment is effectively improved.

Description

[0001] [Technical Field] The present invention belongs to the technical field of intelligent rescue equipment, and specifically relates to a multi-source collaborative rocker-crawler robot for earthquake search and rescue, which is particularly suitable for search and rescue operations for trapped people in complex post-disaster environments such as building collapse.

[0002] [Background Technology] Building collapse sites caused by natural disasters such as earthquakes and landslides have significant characteristics such as extremely complex environment, highly unstable structure, and frequent secondary disasters, which puts forward strict requirements on the technical performance of rescue equipment. Traditional rescue equipment has systematic defects in motion structure, environmental perception, and collaborative operation, which seriously restricts the improvement of rescue efficiency within the golden 72 hours after the disaster.

[0003] As a special rescue equipment, earthquake rescue robots are usually composed of four core modules: motion structure, environmental perception, decision control and energy system. Its typical architecture includes: multi-modal motion execution structure, multi-source sensor array, edge computing unit, real-time control module and emergency communication device, mainly responsible for key tasks such as ruins detection, life search and rescue, and environmental monitoring.

[0004] Now, combined with relevant domestic and foreign patents and literature in the past five years, we analyze the existing technical bottlenecks and their causes in detail:

[0005] Defects in motion structure and terrain adaptability: Existing rescue robots generally adopt a single motion mode design, which makes it difficult to cope with the changeable unstructured terrain in the ruins environment. At present, most earthquake rescue crawler robots have a symmetrical double-track structure. Although it improves passability by increasing the ground contact length of the crawler, its maximum crossing height is only about 80mm, and there is no dynamic center of gravity adjustment structure designed. When climbing steps with a height of more than 50mm, the probability of overturning is nearly 30%. What's more serious is that when encountering a gully with a width of more than 250mm, the rigid chassis structure used by most robots will cause the robot to get stuck and require manual intervention to get out of trouble. Similar problems also exist in most bionic robots, such as the six-legged rescue robot. Although its bionic leg structure theoretically has the ability to move in multiple degrees of freedom, actual tests show that the failure rate of mechanical joints in dusty environments is extremely high, and the maximum load is small, and it cannot carry heavy detection equipment.

[0006] The perception system has a single information dimension: The current mainstream rescue robot's environmental perception system has two major technical shortcomings: First, the single sensor modality leads to incomplete environmental modeling. The laser radars carried by most robots cannot identify special obstacles such as glass curtain walls and mesh fences. Second, the life detection and environmental monitoring functions are separated. The infrared thermal imaging modules integrated in most robots do not simultaneously detect the concentration of dangerous gases such as methane and carbon monoxide. If methane accumulation is not warned in time, it may cause a secondary explosion during the search and rescue process.

[0007] Weak system reliability design: Extreme conditions such as high-frequency vibration, electromagnetic interference, high temperature and high humidity in post-disaster environments pose severe challenges to robot reliability. Most tracked robots have difficulty balancing their center of gravity, which may cause posture loss of control when climbing steep slopes. In addition, existing systems generally lack effective decoupling between the hardware layer and the decision-making layer. In the tight coupling design between STM32 and the host computer, when the ROS node crashes, it will directly cause the underlying motor to lose control.

[0008] The existing technology has three core defects: (1) the contradiction between the single mechanical structure mode and the lack of dynamic stability, which makes it impossible to cross complex terrain; (2) the contradiction between the reliability requirements in extreme environments and the weak system redundancy design; (3) the contradiction between the lack of information dimension of the perception system and the inefficient data fusion. These defects have led to a low deployment success rate of existing rescue robots in actual disaster scenarios, which has seriously restricted the improvement of emergency rescue capabilities.

[0009] [Invention content] The purpose of this invention is to provide a reliable and stable solution for tracked robots to cross complex terrains. It adopts a track-rocker-wheel composite mechanical structure and is supplemented by supporting sensors to explore complex seismic environments. The system implementation process covers three stages: mechanical design, hardware integration, and algorithm development.

[0010] The present invention includes the following contents:

[0011] 1. A crawler-rocker-wheel composite mechanical structure (such as Figure 1 ), which is manufactured using CAD modeling and 3D printing technology. The main frame is made of high-strength lightweight composite materials. The middle part adopts a multi-degree-of-freedom rocker arm structure driven by a steering gear, the bottom is equipped with a double-track differential drive module driven by a motor, and the front is equipped with a swing arm balance wheel set. The track structure adopts a modular design. The single-side track is composed of two independent drive wheels, and flexible steering is achieved through differential control. The rocker arm structure is equipped with a steering gear drive, which can achieve 0-180° dynamic angle adjustment according to the terrain characteristics. With the front wheel grip enhancement device, it can complete complex actions such as gully crossing and step climbing.

[0012] (1) The motion control system establishes a mathematical relationship based on the center of mass dynamics model and achieves motion stability control by deriving the center of mass trajectory equation, ensuring the robot's posture balance when crossing a gully with a width of 300 mm and climbing a step with a height of 100 mm.

[0013]

[0014] (2) The motion control algorithm establishes a mathematical model of horizontal plane differential steering and derives the conversion relationship between linear velocity and angular velocity.

[0015]

[0016] 2. The flow chart of up and down stairs movement is as follows Figure 4 , the specific principles and steps are as follows:

[0017] (1) When the robot climbs the stairs, it is necessary to accurately calculate the positional relationship between the gravity line and the outer angle line of the stairs and establish a mathematical model of the elevation angle α and the step height H. The critical state function is derived as follows:

[0018] H(I,h,a)=R+Isina+hcosa-cosah+R

[0019] Where: R is the wheel diameter, I is the horizontal distance from the front wheel to the center of mass, and h is the vertical height of the center of mass.

[0020] In the specific implementation, the step geometry information obtained by the depth camera is combined to estimate the current center of mass position through an algorithm. The calculation is based on the center of mass motion equation:

[0021]

[0022] Where: m1, m2 represent the mass of the front and rear modules respectively, I2 is the length of the rocker arm, and θ is the swing angle of the rocker arm

[0023] (2) The process of climbing stairs is as follows Figure 4 As shown, the rocker arm adjusts the angle to lift the robot body (a), the rocker arm, front wheel, and crawler track work together to complete the climb over the steps (bc), and the robot continues to move forward along the original trajectory (d);

[0024] 3. The sensor perception system integrates a multimodal sensor array: including Intel RealSense D435i depth camera, RPLIDAR laser radar, MQ-4 natural gas detection module and millimeter wave life detector, GPS positioning, hazardous gas sensor, etc.

[0025] The activation steps are as follows:

[0026] (1) The host computer opens the ROS node, turns on the depth camera and lidar to build a three-dimensional point cloud map, and uses the improved Hector SLAM algorithm to achieve real-time mapping and positioning, with a positioning accuracy of ±2cm.

[0027] (2) After the lower computer program enters the while(1) loop, the sensor detects the micro-motion characteristics of the human body through the Doppler effect, with a detection distance of up to 10 meters. It cooperates with the two-way intercom module to realize the positioning of the trapped person and voice interaction.

[0028] (3) After the lower computer program enters the while(1) loop, the hazardous gas detection module collects sensor analog signals in real time through the STM32 built-in 12-bit ADC, and can monitor the concentration of harmful gases such as methane and carbon monoxide in real time; GPS transmits data to STM32 through the antenna, and after parsing, it is written into the custom communication protocol and sent to the upper computer through the Bluetooth transmission module.

[0029] The control system adopts a layered distributed architecture. The upper computer is based on NVIDIA Jetson Nano equipped with ROS to realize advanced functions such as path planning and task allocation. The lower computer uses the STM32F103 main control chip, which is responsible for real-time control tasks such as motor drive and sensor data acquisition, and the system stability is relatively strong.

[0030] [Advantages and positive effects of the present invention] Compared with the prior art, the present invention has the following advantages and positive effects:

[0031] 1. Multi-modal motion capability and adaptability to complex terrain: The crawler-rocker composite structure of the present invention breaks through the limitation of the single motion mode of traditional rescue robots. Through the intelligent switching of triple motion modes (crawler travel, rocker obstacle crossing, wheeled balance), it achieves full adaptation to the earthquake ruins environment. The mechanical design integrates differential steering control and dynamic center of mass adjustment algorithm, so that the robot can stably cross gullies with a width of up to 350mm and climb vertical steps with a height of 100mm. The terrain adaptability is greatly improved compared with traditional crawler robots, and the passability in collapsed buildings is significantly improved;

[0032] 2. Multi-source information fusion and collaborative work efficiency: By integrating millimeter wave life detection, hazardous gas detection, 3D SLAM mapping and distributed task allocation algorithms, an integrated collaborative work system of "detection-positioning-planning" is constructed. The improved HectorSLAM algorithm improves the mapping accuracy to

[0033] ±2cm. With the help of multi-source information integration algorithm, a multi-source information map containing images, harmful gas content, coordinates and other information is constructed to accurately restore the post-disaster building conditions;

Brief Description of the Drawings

[0034] Figure 1 The present invention proposes a robot track-rocker-wheel model;

[0035] Figure 2 It is a schematic diagram of the rocker arm movement and wheel rotation;

[0036] Figure 3 It’s the radar that builds the map;

[0037] Figure 4 It is a schematic diagram of the realization of up and down steps;

[0038] Figure 5 It is the real-time picture of the camera;

[0039] [Specific implementation method] In order to make the implementation method and significance and advantages of the present invention more clearly expressed, the present invention is described in more detail below in conjunction with the accompanying drawings and implementation examples below.

[0040] Figure 1 The overall structure of the present invention is shown. The robot adopts a track-rocker-wheel composite mechanical structure, with a swingable rocker structure driven by a servo motor at the front, a dual-track differential drive system integrated in the middle, and an auxiliary balance wheel set at the rear. The rocker structure is driven by a servo motor and can achieve dynamic angle adjustment of 0-180°. The front wheel is equipped with a rubber toothed wheel surface to enhance grip. The track drive system adopts a modular design. The single-side track is composed of two independent drive wheels, and flexible steering is achieved through differential control. The auxiliary balance wheel set is used to provide additional support when the robot crosses gullies or climbs steps to ensure the stability of the robot's posture in complex terrain. The design of the mechanical structure is modeled by CAD, and finite element analysis is performed on key load-bearing components to ensure the strength and stability of the structure. The main frame is made of high-strength and lightweight composite materials and manufactured by 3D printing technology. After obtaining the rocker and upper plate, the customized steel plate is combined with the track, motor, and servo. On this basis, Jeston Nano and Stm32 main control boards and related peripherals are added.

[0041] Figure 2 The rocker arm movement and wheel rotation are demonstrated: the rocker arms on the left and right sides of the car and the wheels at the front end of the rocker arms are independently controlled by different motors and rockers, and different front swing, rear swing and front wheel rotation operations can be performed.

[0042] Figure 3 The radar map is shown. The car rotates 360 degrees with itself as the center. The surrounding environment is scanned to obtain the distribution of obstacles, and the results are transmitted back to the host computer in real time. The upper right corner is the map result. At the same time, using the SLAM algorithm, after completing the scan of a location, the car will plan the next exploration path for itself based on the existing map.

[0043] Figure 4 The trolley goes up and down the stairs: it is difficult to cross the stairs with the tracks alone, so a rocker arm is designed for this purpose. Taking the stairs as an example, when encountering a step, the rocker arm swings forward to contact the step surface to provide support. Figure 4 (a), using the motor drive and friction, the car body is continuously lifted and moved forward until the gravity line of the car can just pass through the outer corner line of the step, such as Figure 4 (b) At this time, the rocker arm swings back until it touches the ground, lifting the rear half of the vehicle body. Figure 4 (c) The vehicle body moves forward as a whole and completes the step. Figure 4 (d) The rocker arm returns to its original position.

[0044] Figure 5 The real-time image of the car sent back by the camera is clear and smooth, which is conducive to search and rescue. The upper right corner of the picture is the real-time image received by the host computer.

[0045] In summary, the present invention can cross a gully with a length of about 350mm and climb a step with a height of about 100mm. It has flexible movement, stable center of gravity, and small positioning error. At the same time, the system has the characteristics of multi-source information fusion, including camera detection, radar composition, personnel perception, two-way voice interaction and other functions. In a complex earthquake search and rescue environment, it can significantly improve the search and rescue efficiency and reliability in a complex post-disaster environment.

Claims

1. A multi-source cooperative rocker-crawler robot for earthquake search and rescue, the features of which include: (1) Mechanical structure module: A triple motion modal structure (innovative track-rocker arm structure) made of high-strength and lightweight composite materials, including a front swingable rocker arm structure (servo motor driven, 0-180° dynamic adjustment), a controllable wheel set at the front end of the rocker arm to assist in motion control, a bottom dual-track differential drive system (modular design, independent drive wheels), and a rear auxiliary balancing wheel set; (2) The motion control algorithm is characterized in that the motion control method includes: Differential steering mathematical model: (3) The specific steps for overcoming obstacles are as follows: As shown in Figure 4, first align the rocker arm, then input the PWM wave to the high torque gear servo to control its rotation angle, rotate the rocker arm forward so that its front end presses down on the obstacle to support the robot body, and then drive the robot body to climb using the tracks. During this period, control the rocker arm to stabilize the center of the robot. When the climb is halfway through, rotate the rocker arm to support the robot body backward, and the robot continues to drive the tracks to climb until the obstacle is successfully crossed; (4) Perception system: A multi-source sensor array that integrates Intel RealSense D435i depth camera, RPLIDAR laser radar, millimeter-wave life detector (detection distance 10m), and MQ-4 gas sensor (supporting methane / carbon monoxide detection); (5) Control system: A layered architecture based on the lower computer (responsible for motor drive and sensor acquisition) based on the STM32F103 main control chip and the upper computer (running the ROS system) based on the Jetson Nano, communicating through USB-C.

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