Swarmanoid control

Example videos

Adaptive eyebot movements to guide foot-bots

In this video, eye-bots guide the foot-bots between a nest (top right) and target (bottom left) location in the arena. The eye-bots form a communication network between them, and derive the direction to send foot-bots in from the next hop in the shortest route between the nest and the target in this network. On the ground there are a number of items that form obstacles for the foot-bots, but not for the eye-bots (e.g., bookshelves, tables, etc.). Since the eye-bots are unaware of these obstacles, they might send foot-bots in the direction of obstacles, or they might be positioned above an obstacles, which would make it impossible for foot-bots to approach them in order to get instructions. In this video, the eye-bots adapt their position in order to place themselves in a location where they can best give directions to the foot-bots. They derive good positions from the location where they see more foot-bots. In this video, arrows above the eye-bots show the directions in which eye-bots are sending foot-bots, and circles below the eye-bots show the area on the ground where foot-bots and eye-bots can see each other (a different color is used for the eye-bots indicating the nest and target locations). Finally, a short line above each foot-bot shows its intended movement direction.

Adaptive swarm navigation in a dead end situation

In this video, we have the same situation, but with more complex obstacles. Here, eye-bots do not learn new positions (in fact, they remain static), but they learn to adapt the direction they are sending foot-bots in. The eye-bots maintain two policies for guiding the foot-bots, one pointing towards the target and one pointing towards the source (represented by pink and blue lines above the robots in the videos). The eye-bots sample from these policies to give instructions to the foot-bots, and they observe foot-bot behavior to update their policies. Specifically, if an eye-bot sees a foot-bot that is travelling from the source to the target, it increases the policy pointing to the source for the direction that the foot-bot is coming from. Also, it decreases the policy pointing to the target for this same direction. When an eye-bot sees foot-bots performing obstacle avoidance behavior, they decrease both policies in the direction of the location of these foot-bots, as obstacle avoidance behavior points to the presence of obstacles or to congestion between foot-bots. In the specific experiment we carry out here, we can see how the system learns to send the foot-bots around some obstacles on the ground.

Swarmanoid robots find shortest path over double bridge

Here, we use the same system as before in a different context. The foot-bots are given the choice between two different paths between nest and target, a long one and a short one. The experiment is designed to resemble that of Deneubourg et Al. in their experiments with ants. Our Swarmanoid adaptive navigation system is able to find the shortest path in a majority of the cases.

Energy Efficient Deployment of Eye-bots

A major challenge with aerial robotics is the limited flight autonomy of current systems. Therefore, efficient strategies for minimising energy consumption are required to realise the autonomous deployment of aerial robots for tasks such as search and exploration. The video present a swarm search behaviour that exploits eye-bots' ability to stick to the ceiling, saving energy. The video shows several deployment mechanisms which can further decrease energy consumption by reducing wasted flight time of the swarm. We aim to reduce wasted flight time via two premises: 1) exploiting environment information as it is acquired through robot deployment to better guide flying robots 2) obeying the "law of Diminishing Returns" as robot group size increases.

Self-Organised Recruitment and Deployment of Foot-bots with Eye-bots

In this video, tasks are activated in sequence. An eye-bot requests 5 to 10 robots to execute the task it is coordinating. The request is relayed to the closest eye-bot in the recruitment area, which takes care of recruiting the needed foot-bots. When the team is formed, the recruiting eye-bot delivers it to the requesting eye-bot. After the execution of the task, the foot-bots are returned to the recruitment area. At this point, another eye-bot requests foot-bots for its task (9 to 13) and also in this case recruitment, delivery and return are successful.

Self-Organised Recruitment and Deployment of Foot-bots with Eye-bots

In this video, we show that the recruitment system is successful also when dealing with multiple parallel and asynchronous requests. Initially, two eye-bots request foot-bots at the same time. One eye-bot requests 5 to 10 foot-bots, the other 7 to 13. The requests are relayed to two eye-bots in the recruitment area. While the two foot-bot teams are formed in parallel, a third eye-bot requests 10 to 12 foot-bots. This new request triggers the redistribution of the already recruited foot-bots. Eventually, one team is formed and, when the team leaves the recruitment area, further redistribution takes place, thus allowing another group to be formed and sent to task execution. The third team is formed when the first is returned to the recruitment area.

Self-Organised Recruitment and Deployment of Foot-bots with Eye-bots

In this video, we show how deadlocks are solved in the system. There are 30 available foot-bots in the recruitment area and four simultaneous recruitment requests (min=12, max=13) are formulated at the same time. The eye-bots form their teams in parallel, but soon a deadlock happens -- no eye-bot can satisfy the minimum requested quota. When eye-bots detect convergence to a quota which is less than the minimum, it has a small probability to spike the leaving probability sent to the foot-bots. This simple mechanism is sufficient to allow the system to overcome the deadlock and continue functioning.

Phat-bot creation and navigation

Groups of three foot-bots coordinate in order to effectively grip and transport a hand-bot towards a target location (we term the resulting robot aggregate a "phat-bot").

Handbot bar lifting

Two hand-bots cooperate lifting a bar. The bar is grasped with hand-bot hands, and then is lifted by using hand-bot's rope. The controller of each hand-bot independently tries to keep bar inclination within a certain range, resulting in a more or less coordinated bar lifting.

Coming soon

High quality graphics

This is a concept video in which we depict a possible future application of the swarmanoid. The swarmanoid is deployed into a partially collapsed building to find and retrieve a target object. In the video, we show coordination of the eye-bots and foot-bots to complete the task.

Swarmanoid project started
on October 1, 2006
The project terminated
on September 30, 2010
Last modified:
July 28, 2010. 11:19:08 am
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