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The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand. The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.

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Spacecraft relative motion planning is concerned with the design and execution of maneuvers relative to a nominal target. These types of maneuvers are frequently utilized in missions such as rendezvous and docking, satellite inspection and formation flight where exclusion zones representing spacecraft or other obstacles must be avoided. The presence of these exclusion zones leads to non-linear and non-convex constraints which must be satisfied. In this paper, a novel approach to spacecraft relative motion planning with obstacle avoidance and thrust constraints is developed. This approach is based on a graph search applied to a virtual net of closed (periodic) natural motion trajectories, where the natural motion trajectories represent virtual net nodes (vertices), and adjacency and connection information is determined by conditions defined in terms of safe, positively-invariant tubes built around each trajectory. These conditions guarantee that transitions from one natural motion trajectory to another natural motion trajectory can be completed without constraint violations. The proposed approach improves the flexibility of a previous approach based on the use of forced equilibria, and has other advantages in terms of reduced fuel consumption and passive safety. The resulting maneuvers, if planned on-board, can be executed directly or, if planned off-board, can be used to warm start trajectory optimizers to generate further improvements.