In recent years, the frequent occurrence of various disasters, characterized by uneven spatiotemporal distribution and complex, changing conditions, has posed significant threats and challenges to public safety. For instance, statistical data indicate that in 2022 alone, natural disasters resulted in hundreds of millions of affected individuals, numerous fatalities, and economic losses exceeding hundreds of billions. Disasters can be broadly categorized into natural disasters, such as earthquakes and forest fires; accident disasters, including industrial accidents and chemical leaks; public health incidents, like infectious diseases; and social security events, encompassing wars and terrorist attacks. With the increasing complexity of disasters, the difficulty of prevention and control has escalated, introducing new challenges for public safety operations.
Following a disaster, the rapid rescue of survivors and provision of essential medical assistance are paramount. Empirical evidence suggests that if trapped individuals do not receive aid within 48 hours, their survival rates decrease dramatically. However, collapsed structures often create confined spaces that are inaccessible to human rescuers or even search dogs. Moreover, disasters can induce drastic environmental changes and secondary hazards, exposing rescue personnel to high temperatures, toxic substances, radioactivity, and risks of additional collapses. Certain disasters, such as earthquakes, may propagate to adjacent areas if not addressed promptly, leading to more severe consequences. In contrast, bionic robots, with their robust load-bearing capacity and superior adaptability to complex post-disaster environments, can be equipped with various sensors to assist rescuers in acquiring real-time situational awareness and locating survivors efficiently.
Traditional search and rescue robots predominantly utilize tracked or wheeled mechanisms. Tracked robots offer improved terrain adaptability but are often bulky, lack flexibility, and have limited capability to navigate narrow spaces. Wheeled robots, while more compact and agile, feature relatively complex structures and poor obstacle-crossing performance. Consequently, research has increasingly focused on integrating bionics into robot design, leading to the development of bionic robots such as snake-like and legged variants. These bionic robots, inspired by natural organisms, demonstrate enhanced adaptability to diverse terrains and environments. For example, quadruped robots modeled after canines can perform search and rescue tasks in disaster sites, traversing uneven ground and overcoming obstacles. Primate-inspired robots are capable of executing various ground and manipulation tasks, such as vehicle operation, building entry, door opening, valve turning, and tool retrieval. Another quadruped bionic robot exhibits strong thrust and carrying capacity, enabling it to walk on rugged terrain while transporting heavy equipment. Inspired by insects, tailless, hover-capable flapping-wing robots have been proposed for entry into confined or hazardous spaces to conduct search and rescue missions. Micro flying robots mimicking hummingbird flight can be deployed to survey disaster areas inaccessible to humans, such as collapsed buildings or flood zones. Despite their potential, these bionic robots face limitations, including restricted movement in narrow or rugged terrains, insufficient performance in high-temperature resistance, waterproofing, and endurance, which hinder their effectiveness in diverse rescue scenarios. Additionally, control and navigation technologies require further refinement to enhance autonomy.
Therefore, based on the current application landscape of bionic robots in disaster search and rescue, we analyze the functionalities of various bionic robots and their roles in tasks such as disaster reconnaissance, personnel search and rescue, and material transport. We examine the value of biological inspiration in the structural and algorithmic design of search and rescue robots, summarize existing research challenges, and explore future development trends and research directions to provide theoretical and practical insights for the advancement of bionic robots.
Applications of Bionic Robots in Disaster Response
Disaster response operations can be segmented into three phases based on timing: pre-disaster, during-disaster, and post-disaster. Pre-disaster efforts target foreseeable events and involve tasks like evacuation and resource relocation. During-disaster response includes immediate actions such as firefighting, flood control, and chemical leak containment. Post-disaster activities focus on survivor search and rescue. These phases may overlap but universally demand robots capable of adapting to complex and hazardous disaster environments.
Bionic robots integrate the superior structures and physical properties of biological systems with robotics technology. By emulating natural organisms, bionic robots outperform traditional search and rescue robots in flexibility, environmental adaptability, and task execution efficiency. The deployment of autonomously intelligent bionic robots for disaster search and rescue has emerged as a promising yet challenging domain in robotics.
In disaster rescue operations, bionic robots primarily undertake three categories of tasks:
- Obstacle traversal: Leveraging flexible mobility, bionic robots can navigate narrow passages and debris to access disaster sites for search and rescue.
- Assisted detection: Equipped with sensors, these robots detect trapped individuals via acoustic, thermal, or physiological signals and perform environmental monitoring to provide rescuers with critical real-time data.
- Material transport: Compact bionic robots can deliver small packages of medicine, food, water, and communication devices to stranded persons to sustain basic needs.
Research Status of Bionic Robots
Bionic robots are classified based on their biological prototypes, including legged, snake-like, flying, amphibious, and swarm robots. Each category exploits animal-inspired designs to achieve strong terrain adaptability and fulfill specific roles in search and rescue missions, though inherent deficiencies persist. Table 1 provides a comprehensive analysis of these bionic robot types in disaster applications.
| Category | Bionic Prototype | Function | Advantages | Disadvantages | Specific Applications |
|---|---|---|---|---|---|
| Legged Robots | Quadrupeds, insects, kangaroos | Walking, jumping | Superior adaptability to rugged terrain compared to wheeled/tracked robots | Vulnerable foot ends under high stress, limited load capacity | Mine rescue, terrain navigation in ruins |
| Snake-like Robots | Snakes, earthworms | Crawling, peristalsis | Excellent traversal in narrow spaces and pipes | Poor 3D mobility, inadequate for highly rugged terrain | Collapsed building inspection, pipeline exploration |
| Flying Robots | Insects, birds | Flying | Rapid aerial mobility independent of ground conditions | Small size, low efficiency, short endurance | Aerial reconnaissance, inaccessible area surveys |
| Amphibious Robots | Amphibians | Land walking, underwater swimming | Integrated operation in land-water environments | Complex leg structures, large size, unstable mode transitions | Flood rescue, wetland operations |
| Swarm Robots | Bees, ants | Information sharing, coordination | Collaborative efficiency, enhanced robustness | Challenging human-robot and inter-robot interaction control | Large-area search, distributed sensing |
Legged Robots
Animals such as dogs, cheetahs, and spiders utilize leg-based locomotion to adapt to varied rugged terrains by adjusting gaits, enabling traversal over rocky surfaces, through forests, and up steep slopes. Inspired by these organisms, legged bionic robots offer greater degrees of freedom and superior adaptability to unstructured environments compared to wheeled or tracked counterparts. For instance, a hydraulic-driven wheel-legged coal mine rescue robot模仿多腿动物 was developed to address the challenges and risks of manual rescue in underground mines. This bionic robot comprises a main body and wheel-leg mechanisms that pivot around fixed hinges via swing motors, allowing posture adjustments and high terrain adaptability. Another example is a transformable wheel-legged bionic robot dubbed “Land Devil Ray,” inspired by insect and arthropod locomotion. It employs a multi-link structure that transitions between circular wheeled and wheel-legged mobility modes to suit different terrains. A highly mobile crawling bionic robot, HIbot, draws inspiration from hexapod insects and features a lightweight design with a single drive motor, weighing approximately 440g and lacking control components. This bionic robot can crawl across outdoor environments like gravel and grass, offering benefits of portability, low cost, high mobility, and reliability in search and rescue tasks.
In mountainous or canyon rescues, jumping bionic robots provide distinct advantages for obstacle clearance. Nature’s jumpers, such as kangaroos, can leap over obstacles multiples of their size and evade threats agilely. By incorporating jumping, bionic robots enhance flexibility in specific non-structured environments. For example, a bionic robot Zbot模仿袋鼠跳跃动作 was designed and optimized using a gear five-bar mechanism to decouple knee and ankle joints. This bionic robot possesses jumping capability and complex terrain adaptability, mimicking kangaroos in absorbing landing impact energy and reutilizing it in subsequent jumps, thereby promoting energy efficiency. It is suitable for field search and life rescue missions.
The energy dynamics of a jumping bionic robot can be modeled using mechanical energy conservation. The total energy \( E \) is given by:
$$ E = \frac{1}{2} m v^2 + m g h $$
where \( m \) is mass, \( v \) is velocity, \( g \) is gravitational acceleration, and \( h \) is height. During landing, energy absorption can be characterized by a damping coefficient, and the energy release in subsequent jumps can be optimized to minimize losses, enhancing the bionic robot’s endurance.
Snake-like Robots
Reptiles like snakes, earthworms, and inchworms exhibit remarkable mobility across diverse environments, capable of slipping through narrow channels and climbing uneven surfaces. Snake-like bionic robots, modeled after these creatures, typically feature modular designs with high degrees of freedom and no need for wheels or legs. These bionic robots can maneuver through tight gaps in collapsed structures, offering high stability and agility. For instance, a bionic earthworm robot was designed by incorporating soil-penetration traits and post-disaster spatial constraints. Through simulation and trajectory planning, it achieved functional bionics of earthworm movement, applicable to rescuing buried individuals in mines. Another multi-jointed snake-like bionic robot, Polybot G1v4, adopts a novel gait with flexible, modular, and adjustable body architecture. This bionic robot can climb multiple steps simultaneously and perform rescue operations in hazardous, confined, and uneven terrains. An autonomous snake-like bionic robot tailored for urban search and rescue can navigate unknown environments, generate 3D maps, and plan obstacle-avoidance paths without prior knowledge. It is deployable for locating trapped persons, delivering medical supplies and food, and assessing structural integrity.
The serpentine motion of a snake-like bionic robot can be described using a sinusoidal wave model. The position of the i-th segment is expressed as:
$$ x_i = x_0 + \sum_{j=1}^{i} l \cos(\theta_j) $$
$$ y_i = y_0 + \sum_{j=1}^{i} l \sin(\theta_j) $$
where \( (x_i, y_i) \) denotes the segment position, \( l \) is segment length, and \( \theta_j \) is the joint angle, often following \( \theta_j = A \sin(\omega t + \phi j) \) for amplitude \( A \), frequency \( \omega \), and phase shift \( \phi \). This model facilitates efficient control of the bionic robot’s undulatory movements.
Flying Robots
Inspired by avian flight, flying bionic robots are agile and swift, capable of reaching areas inaccessible to rescuers and providing visual oversight of obscured scenes. Although still in developmental stages for search and rescue, relevant research and concepts have emerged. For example, a bionic flapping-wing micro aerial vehicle模仿蜂鸟 was designed, with a control strategy based on wing joint mechanical impedance to efficiently regulate lift and thrust. This bionic robot holds potential for reconnaissance, surveillance, search and rescue, and mapping. Another micro autonomous aerial vehicle (MAV) inspired by birds incorporates two flapping wings and a chassis, achieving autonomous navigation via a Pixhawk flight controller. It exhibits exceptional mobility and rapid response in search and rescue tasks.
The lift generation in a flapping-wing bionic robot can be approximated using aerodynamic principles. The lift force \( L \) is proportional to the square of the flapping frequency \( f \) and wing area \( S \):
$$ L = \frac{1}{2} \rho C_L S (2 \pi f R)^2 $$
where \( \rho \) is air density, \( C_L \) is lift coefficient, and \( R \) is wing length. Optimizing these parameters is crucial for enhancing the bionic robot’s flight performance.
Amphibious Robots
Amphibious bionic robots, modeled after amphibians, combine terrestrial and aquatic capabilities, adapting to environments like shorelines, wetlands, and marshes. They are promising for public safety, particularly in flood rescue and supply delivery. A comparative study of 13 bionic robot models with natural animal prototypes evaluated propulsion modes, speed, efficiency, maneuverability, and stability. It highlighted that amphibious bionic robots can operate in extreme or inaccessible settings, such as seabeds and pipelines, performing search and rescue, communication, and material transport. Their development introduces new tools for complex environment operations. For instance, genetic algorithms were applied to optimize the structure and parameters of an amphibious bionic robot with adaptive landing and obstacle-crossing abilities, improving its performance for disaster relief. Another amphibious bionic robot模仿泥跳鱼 features flip-legs that enable locomotion on varied terrains and aquatic propulsion. By adjusting leg stiffness distribution, this bionic robot adapts to land and water conditions, executing tasks like field exploration and disaster search and rescue.
The motion of an amphibious bionic robot involves distinct models for land and water. On land, legged kinematics apply, while in water, the thrust force \( F_t \) can be modeled as:
$$ F_t = \frac{1}{2} \rho C_d A v^2 $$
where \( \rho \) is fluid density, \( C_d \) is drag coefficient, \( A \) is reference area, and \( v \) is velocity. This equation aids in designing efficient propulsion systems for the bionic robot.
Swarm Robots
Social animals like bees and ants demonstrate collective behaviors where individuals interact locally to achieve group objectives such as foraging and nest building. Inspired by these systems, researchers have developed control algorithms for robot coordination. Swarm bionic robots outperform solitary units in handling complex environments and task efficiency. Moreover, swarm strategies enhance robustness, as individual failures do not compromise the entire system.
For example, an algorithm模仿蚁群觅食 was proposed for robot group search and rescue in disasters, accidents, or missing person cases. It enables bionic robot groups to efficiently scan target areas, acquire location data, and coordinate actions to boost success rates. Another ant colony optimization algorithm模仿蚂蚁觅食 facilitates optimal path finding by simulating ant foraging behavior. In search and rescue, drone swarms utilize this algorithm for collaborative area scanning, monitoring, and data relay to command centers, improving mission efficiency. Additionally, human-machine collaboration algorithms inspired by bacterial and bee behaviors were devised for drone groups to work with human teams. These algorithms allow bionic robot swarms to detect and locate victims and transmit information to rescuers.
In ant colony optimization for path planning, the probability \( P_{ij} \) of selecting path from node i to j is:
$$ P_{ij} = \frac{ [\tau_{ij}]^\alpha [\eta_{ij}]^\beta }{ \sum_{k \in \text{allowed}} [\tau_{ik}]^\alpha [\eta_{ik}]^\beta } $$
where \( \tau_{ij} \) is pheromone level, \( \eta_{ij} \) is heuristic information (e.g., inverse distance), and \( \alpha \), \( \beta \) are influence parameters. Pheromone updates based on solution quality ensure adaptive routing for the bionic robot swarm.
Existing Research Problems and Deficiencies
Despite performance advantages over traditional search and rescue robots, current bionic robots exhibit several shortcomings. For legged bionic robots, stability and control complexity often limit designs to quadruped or hexapod configurations. However, during locomotion, foot ends endure impact forces magnitudes higher than static loads, leading to durability issues and constrained load capacity. While static stability control is mature, maintaining dynamic balance on complex disaster terrains requires enhancement.
Snake-like bionic robots excel in planar motions like serpentine crawling and peristalsis but suffer from poor 3D mobility due to structural, flexibility, and control limitations. Improving 3D obstacle negotiation to adapt to highly rugged, non-structured environments remains a critical challenge for these bionic robots.
Research on flying bionic robots primarily focuses on lift-generating mechanisms, with less emphasis on flight control strategies. Additionally, small form factors restrict space for batteries, sensors, and control modules, resulting in low efficiency and endurance for these bionic robots.
Amphibious bionic robots necessitate mode switching between land and water, leading to complex leg structures and larger sizes. Stability during transitions is suboptimal, and underwater operation demands strength and waterproofing to withstand pressure, while terrestrial performance requires adaptation to adjacent environments like mudflats and wetlands.
Swarm bionic robots face challenges in managing multi-robot coordination, such as estimating group performance and designing controllers for desired outcomes. Disaster sites’ high risks and complexity often require human supervision, complicating human-robot interaction. When robots are damaged or added, re-planning paths and reallocating tasks dynamically are ongoing difficulties for bionic robot swarms.
Future Development Trends
With advancements in bionics and related fields, bionic robots are transitioning from research to practical applications, becoming vital tools for disaster and emergency response. Addressing complex disaster environments necessitates careful consideration of robot performance aspects, including materials and sealing. Recent trends in bionic robot development include miniaturization, intelligence, and swarmization, as summarized in Table 2.
| Trend | Description | Impact on Bionic Robots |
|---|---|---|
| Miniaturization | Development of smaller robots for better access to narrow spaces, reduced cost and energy use | Enhanced flexibility and deployment in confined areas |
| Intelligence | Integration of AI for autonomous adaptation and decision-making | Improved task efficiency and environmental interaction |
| Swarmization | Coordination of multiple robots for collaborative tasks | Increased system robustness and mission capability |
Miniaturization: In disaster scenarios, large machinery may cause environmental damage, such as debris collapse. Small bionic robots offer low cost, energy efficiency, and superior flexibility, driving a trend toward compact designs.
Intelligence: The development of bionic robots involves interdisciplinary knowledge from biology, robotics, materials science, and artificial intelligence. As technologies evolve, creating intelligent bionic robots that autonomously adapt to complex post-disaster environments is becoming a priority. For instance, control systems can employ PID controllers for motion regulation:
$$ u(t) = K_p e(t) + K_i \int_0^t e(\tau) d\tau + K_d \frac{de(t)}{dt} $$
where \( u(t) \) is control output, \( e(t) \) is error, and \( K_p \), \( K_i \), \( K_d \) are gains. Such models enhance the bionic robot’s responsiveness and stability.
Swarmization: Swarm bionic robots represent a burgeoning research direction. Optimizing human-robot and inter-robot interactions to improve group efficiency in search and rescue tasks is a key focus. For example, reinforcement learning can be applied to optimize swarm behaviors:
$$ Q(s,a) \leftarrow Q(s,a) + \alpha [r + \gamma \max_{a’} Q(s’,a’) – Q(s,a)] $$
where \( Q(s,a) \) is action-value, \( \alpha \) is learning rate, \( r \) is reward, and \( \gamma \) is discount factor. This approach enables bionic robot swarms to learn cooperative strategies adaptively.
Conclusion
Bionic robots demonstrate significant potential in complex disaster rescue operations due to their enhanced flexibility and environmental adaptability. Consequently, research has increasingly incorporated bionics into robot design, yielding diverse bionic robot types with unique strengths, such as navigating narrow spaces and exhibiting high mobility and stability on rugged terrain. However, many bionic robots remain in the experimental-to-practical transition phase, with unresolved issues limiting broader application in real-world rescues.
Current research indicates trends toward miniaturization, intelligence, and swarmization in bionic robots. Miniaturized bionic robots offer greater accessibility to confined areas, intelligent systems boost autonomy and efficiency, and swarm configurations enhance collaborative performance. These directions suggest that future bionic robots will be more refined, smart, and synergistic, providing reliable and effective support for disaster response. As technology progresses, bionic robots are poised to play an increasingly pivotal role in rescue domains.