The use of soft robots in future space exploration is still a far-fetched idea, but an attractive one. Soft robots are inherently compliant mechanisms that are well suited for locomotion on rough terrain as often faced in extra-planetary environments. Depending on the particular application and requirements, the best shape (or body morphology) and locomotion strategy for such robots will vary substantially. Recent developments in soft robotics and evolutionary optimization showed the possibility to simultaneously evolve the morphology and locomotion strategy in simulated trials. The use of techniques such as generative encoding and neural evolution were key to these findings. In this paper, we improve further on this methodology by introducing the use of a novelty measure during the evolution process. We compare fitness search and novelty search in different gravity levels and we consistently find novelty-based search to perform as good as or better than a fitness--based search, while also delivering a greater variety of designs. We propose a combination of the two techniques using fitness-elitism in novelty search to obtain a further improvement. We then use our methodology to evolve the gait and morphology of soft robots at different gravity levels, finding a taxonomy of possible locomotion strategies that are analyzed in the context of space-exploration.

Since the first demonstration of the Virtual Robot Competition, USARSim has been used as the simulation interface and environment. The underlying simulation platform, Unreal Engine, has seen three major upgrades (UT2004, UT3 and UDK). These upgrades required a whole new USARSim simulator to be built from scratch. Yet, between those versions the USARSim interface has not been modified, which made USARSim a stable platform for more than 10 years. This stability allowed developers to concentrate on their control and perception algorithms. This paper describes a new prototype of the USARSim interface; implemented as plugin to Gazebo, the simulation environment native to ROS. This plugin would facilitate a shift of the maintenance of the simulation environment to the Open Source Robotics foundation and attract new teams to the Virtual Robot Competition. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The capability to follow a person in a domestic environment is an important prerequisite for a robot companion. In this paper, a tracking algorithm is presented that makes it possible to follow a person using a small robot. This algorithm can track a person while moving around, regardless of the sometimes erratic movements of the legged robot. Robust performance is obtained by fusion of two algorithms, one based on salient features and one on color histograms. Re-initializing object histograms enables the system to track a person even when the illumination in the environment changes. By being able to re-initialize the system on run time using background subtraction, the system gains an extra level of robustness.

For electronic payments, the communication link has to be reliable. Dedicated short-range communication (DSRC) is a proposed solution for the automatic debiting of vehicles in an electronic toll collection (ETC) system without disturbing the traffic flow. The requirements on the reliability of such a system are high, which implies that only large-scale simulations with a lot of detail are effective for analyzing occasional errors. In this paper, a hierarchical approach is worked out that allows such simulations of the communication link, with a 80% reduction of the computational effort compared to simulation with full detail.