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Algorithms for the control of the coordinated movement of a swarm-bot on rough terrain

Coordinated motion is a basic ability required of a swarm-bot. To allow the swarm-bot to move, the constituent s-bots must coordinate their actions to choose a common direction of motion. This coordination is not self-evident, as each s-bot is controlled independently. The required coordination is achieved primarily through use of the s-bot's traction sensor, which is placed at the turret-chassis junction of an s-bot. The traction sensor returns the direction (i.e., the angle with respect to the chassis' orientation) and the intensity of the force of traction (henceforth called "traction'') that the turret exerts on the chassis. Traction results from the movements of the s-bot's own chassis as well as the movements of other both s-bots connected to it. Note that the turret of each s-bot physically integrates the forces that are applied to the s-bot by the other s-bots. As a consequence, the traction sensor provides the s-bot with an indication of the average direction toward which the group as a whole is trying to move. More precisely, it measures the mismatch between the direction in which the s-bot's own chassis is trying to move and the direction in which the whole group is trying to move.

Our experimental work has focused on the evolution of artificial neural networks capable of controlling the behaviour of a swarm-bot in a coordinated manner. The robot's controllers is a "perceptron", that is a feed-forward neural network with four sensory units, plus a bias unit, directly connected to two motor units. The network takes as input the traction sensor reading: that is, the first and third sensory units encode the dimension of traction parallel to the chassis, while the second and fourth units encode the dimension of traction orthogonal to the chassis. The output of the network, appropriately scaled, is used to set the angular velocity of the left and right robot's wheels and the turret-chassis motor.

The connection weights of the neural controller are evolved. The evolutionary process run in a simulated world, instantiated by making use of the swarm-bot3D simulator. At generation 0, a population consisting of 100 randomly generated genotypes is initialised. Each genotype encodes the connection weights of 100 corresponding neural controllers. In the genotype each connection weight is represented by eight bits that are transformed into a number within [-10, 10]. Each genotype encodes the weights of four identical neural controllers that are used to control the s-bots assembled into a swarm-bot. Each swarm-bot is subsequently tested in five epochs, each lasting 150 time steps of 100ms each. At the beginning of each epoch the orientations of the chassis of the s-bots are randomly assigned. The 20 best genotypes of each "generation" are "reproduced" by generating five copies each, with 3% of their bits replaced by a new randomly selected value.

In this kind of experiments, the problem that the s-bots have to solve is that their traction systems (wheels plus tracks) might have different initial directions or might mismatch while moving. In order to coordinate, s-bots should be able to collectively choose a common direction of movement whilst only having access only to local information. Each s-bot's controller (i.e., an artificial neural network) takes as input the readings of its traction sensor and as output sets the status of the s-bot's actuators.

The results obtained show that evolution can find simple and effective solutions that allow the s-bots to move in a coordinated way independently of the topology of the swarm-bot. Moreover, it was found that the evolved s-bot controllers also exhibit obstacle avoidance behaviour (when placed in an environment with obstacles), and scale well to swarm-bots of a larger size. Additionally, they are robust to environmental changes such as varying terrain roughness or presence of moderately sized holes (i.e., holes too big for a single s-bot, but small enough to be passed over by the swarm-bot itself). Building on the coordinated motion behaviour, we were also able to synthesise controllers that allow the s-bots in swarm-bot formation to sense the presence of big holes and avoid them.

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