Researchers at IIT Bombay are using robots to understand how animals find their way back home from unfamiliar places, a skill called homing.
Nitin Kumar, assistant professor at the Department of Physics at IIT Bombay, explained, “The primary goal of our research group is to understand the physics of active and living systems. We achieve this by performing experiments on centimetre-sized self-propelled programmable robots. In simple words, we model these robots to mimic the dynamics of living organisms, both at the individual and collective levels.”
Homing is crucial for many life-forms, from birds flying thousands of kilometres during migration to ants finding their way back to their colonies after foraging. Humans have even harnessed this ability to train homing pigeons to deliver messages over long distances.
Dr. Kumar’s team has now developed a robot that mimics foraging and homing behaviour. This robot is designed to move on its own and use light as a guide to return home. In a new study, they have reported some principles of homing based on their studies with the robot.
The foraging robot is programmed to move semi-randomly, like how animals might wander when looking for food. This type of movement is called active brownian (AB) motion. The robot’s direction changes frequently due to rotational diffusion, a mechanism that introduces a certain level of randomness to its path. When the robot needs to return home, it shifts to a different mode that doesn’t include randomness inputs.
The researchers shone a beam of light on the robot; the light’s intensity changed gradually. The robot was programmed to follow this gradient light to find its way back, mimicking the way some animals use the Sun or other environmental light sources to find their way.
“The homing motion is similar to the AB model, except the robot undergoes frequent course corrections whenever it deviates significantly from its intended homing direction, as expected in actual living organisms” Dr. Kumar said.
In their study, the team determined the time the robot took to return home after being forced to deviate more and more from its homing path. They observed that the reorientation rate — the frequency with which the robot adjusted its direction to return home — was related to the degree of randomness in its path. They reported an optimal reorientation rate for a particular value of randomness; beyond this rate, the frequency of reorientations negated the effects of randomness and ensured the robot got home.
According to the researchers, this suggests animals may have evolved to reorient themselves at an optimal rate to efficiently find their way home, regardless of the noise or unpredictability in their environment.
“The observation of a finite upper limit on return times indicates that the homing motion is inherently efficient,” Dr. Kumar said. Our results demonstrated that if animals are always aware of the direction of their home and always correct their course whenever they deviate from the intended direction, they will surely get home within a finite time.”
To validate their findings, the researchers built a theoretical model that would predict how long a robot would take to reach home depending on its behaviour. The model was able to successfully explain the robot’s behaviour and also captured specific features of its homing path, Dr. Kumar said.
The model highlights the importance of reorientation as a strategy, showing that frequent course corrections are vital for efficient navigation, he added.
The team also ran computer simulations in which the robot’s movement mimicked certain animals. They matched the experimental results, reinforcing the idea that randomness and reorientation worked hand-in-hand to optimise homing. “When we applied this model to the trajectories of a real biological system of a flock of homing pigeons, it showed a good agreement with our theory, validating our hypothesis of enhanced efficiency due to frequent course corrections,” Dr. Kumar said.
This said, light-based cues are one of many involved in real-world navigation; others include social interactions, changing landscapes, and other environmental factors. “In our future research, we aim to model these scenarios in our experiment by using a combination of spatiotemporal variations in light intensity and physical obstacles,” Dr. Kumar said.
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