Automatic design is a promising approach to realizing robot swarms. Given a mission to be performed by the swarm, an automatic method produces the required control software for the individual robots. Automatic design has concentrated on missions that a swarm can execute independently, interacting only with a static environment and without the involvement of other active entities. In this paper, we investigate the design of robot swarms that perform their mission by interacting with other robots that populate their environment. We frame our research within robot shepherding: the problem of using a small group of robots—the shepherds— to coordinate a relatively larger group—the sheep. In our study, the group of shepherds is the swarm that is automatically designed, and the sheep are pre-programmed robots that populate its environment. We use automatic modular design and neuroevolution to produce the control software for the swarm of shepherds to coordinate the sheep. We show that automatic design can leverage mission-specific interaction strategies to enable an effective coordination between the two groups.

We investigate the automatic design of robot swarms that perform composite missions—that is, missions specified as the composition of consecutive sub-missions. Automatic design through performance optimization has become a viable and appealing approach to designing robot swarms. First, a user defines a mission by specifying a performance measure: a function indicating to what extent the swarm has attained its goal. An optimization process then generates suitable control software for the robots by maximizing the performance measure. The definition of a performance measure is a challenging task that requires expert input, which hinders the automatic nature of the approach. Recently, inverse reinforcement learning was introduced to minimize the need for human intervention in the automatic design of robot swarms. However, this method was only applied to single-objective missions. In this paper, we extend the method to address composite missions, by formulating and solving the design problem as a multi-objective optimization problem. We conduct simulations with a swarm of twenty e-puck robots that perform twelve composite missions. We compare the performance of the swarm when the robots operate with control software produced manually or using inverse reinforcement learning.

Automatic design is a promising approach to generating control software for robot swarms. So far, automatic design has relied on mission-specific objective functions to specify the desired collective behavior. In this paper, we explore the possibility to specify the desired collective behavior via demonstrations. We develop Demo-Cho, an automatic design method that combines inverse reinforcement learning with automatic modular design of control software for robot swarms. We show that, only on the basis of demonstrations and without the need to be provided with an explicit objective function, Demo-Cho successfully generated control software to perform four missions. We present results obtained in simulation and with physical robots.

This paper presents a pedestrian trajectory prediction technique. Its mail novelty is that it does not require any previous observation or knowledge of pedestrian trajectories, thus making it useful for autonomous surveillance applications. The prediction requires only a set of possible goals, a map of the scenario and the initial position of the pedestrian. Then, it uses two different path planing algorithms to find the possible routes and transforms the similarity between observed and planned routes into probabilities. Finally, it applies a motion model to obtain a time-stamped predicted trajectory. The system has been used in combination with a pedestrian detection and tracking system for real-world tests as well as a simulation software for a large number of executions.