Control

The Scenario
	The scenario It is a search and retrieve task which gives an example of the possible activities a swarm-bot could carry out. Moreover, this scenario shows how the s-bots can rely on self-organisation and self-assembly to achieve a given task. The scenario solved by 12 real s-bots

Basic Behavioral Capabilities
	Co-ordinated motion This concerns the design of control policies for the development of all the behavioural skills required either (i) by a single s-bot to co-ordinate its motion with respect to the movement of other s-bots located nearby (e.g., an aggregation of s-bots which negotiate a common direction of movement); or (ii) by a swarm-bot in order to achieve particular results without incurring into undesired circumstances (e.g., reaching a certain target area without crashing into obstacles, or falling into troughs, etc.). Experiments with real robots Experiments in simulation
	Hole/obstacle avoidance This task has been designed for studying all-terrain collective navigation strategies. It can be considered an instance of the broader family of all-terrain navigation tasks, which generally tackle the problem of navigating in complex environments presenting obstacles, rough terrains, holes, gaps or narrow passages. Compared to other problems in the all-terrain navigation family, the hole/obstacle avoidance task represents a relatively simple instance, but it is still very interesting for the study of collective navigation behaviours for a swarm-bot. Experiments with real robots Experiments in simulation
	Passing over a hole One of the main features a swarm-bot can exhibit is the ability to assemble in physical structures that can solve problems a single individual cannot cope with. One example of such a problem is passing over a trough that would block the navigation of a single robot. In similar situations, physical connections serve as support for those s-bots that are suspended over the gap, so that the swarm-bot as a whole can continue moving. Experiments in simulation
	Moving on rough terrain All-terrain navigation requires the ability to cope with a generic rough terrain, comprising slopes and obstacles that have to be climbed. The swarm-bot has been designed intentionally for this purpose, as it presents mechanical features that allow a single individual to cope with moderately rough terrains. In this section, we show how cooperation among individual s-bots is beneficial for rough terrain navigation, whenever the individual abilities are too constraining. Experiments with real robots Experiments in simulation
	Aggregation It concerns the design of control policies for the development of all the behavioural skills required by single s-bots to get close and eventually to physically connect to each other or to an already formed swarm-bot (i.e., s-bots physically connected) by means of a gripper. Experiments in simulation
	Self-assembly This section addresses the problem of synthesizing controllers for groups of s-bots capable of adaptively connecting to each other, forming an assembled structure---referred to as swarm-bot and/or to an object (called the prey). In particular, we focus on controlling a group of s-bots in a fully autonomous way in order to locate, approach and connect to another sbot or the prey. Experiments with real robots
	Functional Self-assembly This section addresses the problem of synthesizing controllers for groups of s-bots capable of adaptively connecting to each other, forming an assembled structure---referred to as swarm-bot and/or to an object (called the prey). In particular, we focus on functional self-assembling, that is, the self-organized creation of a physically connected structure, which should be functional to the accomplishment of a particular task, by designing controllers for s-bots capable of forming a swarm-bot any time environmental contingencies prevent the single s-bot to achieve its goal. Experiments in simulation Experiments with real robots
	Adaptive division of labour A swarm of robots can generally perform several different tasks concurrently. The performance of the final results depends on the number of robots that are participating in each task. If they are too few, they could not succeed. If they are too many, they could interfere with each other. This section illustrates our studies about self-organised solutions for the division of labour in a swarm-bot. Experiments in simulation
Finding Object / Goal
	Finding object/goal This concerns the design of control policies for the development of all the behavioural skills required by the s-bots in order to establish a path that links a starting area to a target area by exploiting the s-bots as beacons. Experiments with real robots Experiments in simulation
Cooperative Transport
	Cooperative transport This concerns the design of control policies for the development of all the behavioural skills required by a group of s-bots in order to approach an object and to cooperativelly transport it towards a target location (e.g., the nest). Experiments with real robots Experiments in simulation