Why animals still outrun robots
We have seen videos of a cheetah sprinting across the savannah, eortlessly manoeuvring around obstacles at high speed and marvelled at the combination of grace and speed. Now, picture a robot attempting the same feat. While advances in robotics have been significant, the robot’s performance is clunky and slow in comparison.
In a study published in Science Robotics, researchers Samuel A Burden et al, explore why animals still outpace the most advanced robots. The study digs deep to understand the mechanics of movement, comparing the locomotive systems of animals and robots across five key areas: power, frame, actuation, sensing and control.
FUEL OF MOVEMENT
In the race between animals and robots, one critical area where the gap is most evident is in the subsystem of power.
Animals rely on fats and carbohydrates for their energy needs. Since the energy density of biological fuels is remarkably high, it allows animals to operate over long distances without needing to refuel. For instance, the fat stores in an animal can provide more than twice the energy per unit mass compared to the best lithiumion batteries.
Moreover, animals metabolise these fuels with an e©ciency that engineers can only envy. The oxidative metabolism in animals converts fats to usable energy (ATP) with e©ciencies around 70 per cent. In contrast, the robot’s internal combustion engines convert fuel to movement at about 25 per cent e©ciency.
SKELETON VS FRAMES
Animals have evolved skeletal structures that are highly optimised for their specific modes of movement. For example, vertebrates have bones made of collagen and hydroxyapatite, creating structures that are both strong and lightweight.
Robotic frames, made from materials like carbon fibre, aluminium or steel are chosen for their strength and lightness, but they fail to match the adaptive nature of biological frames. For example, carbon fibre oers high stiness and agility, inspired by nature
can be tailored for directional strength, but it lacks the multifunctional capabilities of biotissues.
MUSCLES VS MOTORS
Animals use muscles for actuation. Muscles help in adjusting stiness and they rapidly change their length which allows animals to move easily. They can also achieve impressive power densities due to their ability to contract and expand quickly, storing and releasing energy in the process. This dynamic ability contributes greatly to the agility and speed of animals
Robotic actuation on the other hand primarily relies on electric motors and piezoelectric actuators. Electric motors are favoured in many robots as they can be precisely controlled. Though some highend electric motors can match or even exceed the power density of muscles, they often fall short when it comes to torque density without the use transmission mechanisms such as gearboxes. This can introduce ine©ciencies and reduced response time. Piezoelectric actuators on the other hand oer very fine control at small scales and can operate quickly, but they do not scale well to the larger forces.
SENSORY SUPREMACY
Animals excel in this domain as well due to their highly developed, sophisticated sensory systems. They have photoreceptors and mechanoreceptors distributed throughout their bodies which help in situational awareness and body control. These sensors guide body movements and help avoid obstacles.
Robots, by contrast, typically rely on a more limited set of sensors, often centralised which reduce their ability to adapt to new or complex environments.
The answer to robotic grace and
Common sensors such as include cameras and LIDAR (Light Detection and Ranging) serve as the robot’s eyes. While these tools are powerful for navigation and object recognition, they do not fully replicate the sensory inputs an animal gets.
CONTROL
The neural architecture in animals allows for rapid processing of a vast array of sensory data and the generation of context-specific responses. This allows for automate repetitive motions like walking or running without constant brain intervention. In addition, neuroplasticity allows animals to master complex tasks through practice.
Robots, on the other hand, traditionally use systems that often rely on pre-programmed responses and have limited ability to learn from experience or adapt in real-time. While advancements in machine learning and AI have significantly improved robotic control, these systems still lack the fluidity and adaptability of biological control systems.
BRIDGING THE GAP
Animals excel in locomotion due to their integrated systems that combine sensory inputs, neural processing and adaptive actuation in a seamless manner. In contrast, robots often have disjointed systems leading to slower, less adaptable and more rigid movements.
One promising approach to bridge the gap is the development of bio-inspired designs that replicate the natural integration seen in animal locomotion. This can potentially narrow the performance gap, leading to robots that can move with the same grace, e©ciency and resilience as their biological counterparts.