In the field of robotics, the development of bionic robots represents a significant advancement, reflecting a nation’s technological prowess and industrial automation capabilities. As a typical example of bionic robots, multi-legged robots offer rich gaits and redundant limb structures, ensuring high flexibility and reliability. Since the 1980s, various designs have emerged, from simple hydraulic-driven quadruped robots to more sophisticated servo-driven models. However, there remains a need for compact bionic robots capable of operating in confined spaces. Inspired by spiders, I designed a bionic eight-legged robot that emphasizes miniaturization and obstacle-crossing abilities for diverse environments. This article details the structural design and obstacle simulation of this bionic robot, leveraging tools like SolidWorks and its Motion module to validate performance.
The bionic robot is based on spider morphology, which features a cephalothorax with eight legs symmetrically distributed. This configuration provides non-interfering movement spaces, enhancing stability and agility. I adopted a decagonal body structure for the bionic robot, mirroring the spider’s advantages. This design allows vertical connection of leg root joints to the body, expanding motion range, improving stability through symmetric leg distribution, and reducing leg collisions. The body consists of two overlapping decagonal plates, with a gap for servos and control circuits, and the top plate can carry sensors or payloads. This compact structure enables the bionic robot to navigate narrow areas effectively.
For leg design, I simplified spider leg anatomy by merging the hip joint into the femur and omitting the tarsus, as it primarily serves a gripping function with limited mobility. The simplified leg comprises two joints: a root joint (hip) and a knee joint, driven by servos. To transmit motion, I selected linkage mechanisms, such as parallelogram crank-link and slider-link systems, due to their minimal backlash, high rigidity, and ability to maintain specific configurations—ideal for short-distance power transmission in bionic robots. Each leg uses two servos arranged in a cross-assembly pattern to optimize space and torque. The leg model includes linkages that convert rotary servo motion into leg articulation, enabling precise control over foot trajectories.
The bionic robot’s obstacle-crossing capability is critical for its application in complex environments. I analyzed two primary methods: stride-over for low-width obstacles and crawl-over for higher ones. The robot’s legs are divided into two groups (Group 1: L1, R1, L3, R3; Group 2: L2, R2, L4, R4) that alternate between support and swing phases during locomotion. This gait ensures continuous stability and propulsion. Using SolidWorks Motion, I simulated these movements to determine maximum obstacle dimensions. The servo-driven joints follow periodic functions, with root joint rotation limited to ±22.5° to avoid leg interference, and knee joint rotation set to ±45° for optimal stride.
For stride-over mode, the bionic robot must clear obstacles without body contact. The foot trajectory relative to the body’s coordinate origin was analyzed. The vertical displacement (z-axis) of the foot tip ranges from 5 mm to -79 mm, implying a maximum obstacle height of 79 mm to prevent body collision. Horizontally, the foot tip’s x-axis displacement is approximately 60 mm during the lift phase, so a safe obstacle width is set to 50 mm. The motion equations for servo drives are defined as follows:
Group 1 root joint drive: $$22.5 \times \sin(2 \times \pi \times t + 0.5 \times \pi)$$
Group 1 knee joint drive: $$25 \times \sin(2 \times \pi \times t + \pi)$$
Group 2 knee joint drive: $$25 \times \sin(2 \times \pi \times t)$$
Group 2 root joint drive: $$22.5 \times \sin(2 \times \pi \times t + 1.5 \times \pi)$$
where \( t \) represents time. These equations ensure coordinated leg movements for stable walking. The simulation confirmed that the bionic robot can stride over a 79 mm high and 50 mm wide obstacle, as shown by the center-of-mass displacement plot. The robot’s body remains elevated throughout, validating the design.
In crawl-over mode, the bionic robot traverses obstacles by maintaining continuous support and avoiding tipping. This requires obstacles with lower heights. Through iterative simulation in SolidWorks Motion, I adjusted obstacle heights from 50 mm downward. The bionic robot successfully crawled over obstacles up to 40.5 mm high, as it ensured at least three legs in support phase at any time, preventing instability. The motion process involved gradual body lifting and leg adjustment, with the bionic robot adapting its gait to the obstacle profile. This demonstrates the bionic robot’s versatility in handling varied terrain.
To summarize key parameters, I provide the following tables. Table 1 outlines the bionic robot’s structural specifications, while Table 2 details obstacle-crossing capabilities based on simulation results.
| Component | Description | Dimensions/Parameters |
|---|---|---|
| Body | Decagonal plates, overlapping | Diameter: 120 mm, Height: 20 mm |
| Legs | Eight legs, symmetric distribution | Length per leg: 80 mm, Joints: 2 per leg |
| Servos | Micro servo motors, cross-assembled | Torque: 2.5 kg·cm, Rotation: ±45° |
| Linkage Mechanism | Parallelogram crank-link system | Transmission ratio: 1:1, Material: Aluminum |
| Weight | Total robot mass | Approx. 300 g |
| Mode | Max Obstacle Height (mm) | Max Obstacle Width (mm) | Conditions |
|---|---|---|---|
| Stride-Over | 79 | 50 | Knee joint ±45°, Root joint ±22.5° |
| Crawl-Over | 40.5 | Unlimited (continuous surface) | Same joint limits, maintained support |
| General Walking | N/A | N/A | Flat terrain, speed: 10 mm/s |
The simulation process involved several steps. First, I modeled the bionic robot in SolidWorks, ensuring accurate assembly of legs and body. Then, I applied motion drivers to servos using the equations above. For obstacle testing, I created configurable ground parts with obstacles of varying heights. The Motion analysis computed dynamics and collisions, outputting displacement and force data. The bionic robot’s performance was evaluated based on center-of-mass stability and foot-ground contact. Results indicate that the bionic robot can handle obstacles up to 40.5 mm in crawl mode, making it suitable for rough environments.
Further analysis of the bionic robot’s kinematics reveals insights into leg trajectory optimization. The foot tip position relative to the body can be expressed using forward kinematics. For a leg with two rotary joints, the coordinates are given by:
$$x = L_1 \cos(\theta_1) + L_2 \cos(\theta_1 + \theta_2)$$
$$z = L_1 \sin(\theta_1) + L_2 \sin(\theta_1 + \theta_2)$$
where \( L_1 \) and \( L_2 \) are link lengths (40 mm each), \( \theta_1 \) is the root joint angle, and \( \theta_2 \) is the knee joint angle. By plugging in the servo drive functions, I derived the foot path, which informs obstacle clearance. This mathematical model supports the simulation findings, emphasizing the bionic robot’s design efficiency.
In terms of applications, this bionic robot can be deployed in search-and-rescue missions, pipeline inspection, or planetary exploration, where compact size and obstacle negotiation are crucial. The use of servo motors and linkages simplifies control, while the modular body allows for customization. Future improvements may include integrating sensors for autonomous navigation or enhancing materials for higher payloads. The bionic robot represents a step toward versatile multi-legged systems.
To conclude, I successfully designed and simulated a bionic eight-legged robot with notable obstacle-crossing abilities. The spider-inspired structure ensures stability and compactness, while the linkage-based leg mechanism provides precise movement. Simulations confirm that the bionic robot can stride over obstacles up to 79 mm high and crawl over those up to 40.5 mm high, demonstrating adaptability for confined spaces. This work contributes to the field of bionic robots by offering a design framework for miniaturized multi-legged platforms. Continued research will focus on dynamic gait optimization and real-world testing to further advance bionic robot capabilities.