To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the search space. Encoding such information in a traditional two-dimensional cost map is limiting because it is unable to capture the influence of orientation on the roll and pitch estimates from sloped terrain. The research presented herein addresses this problem by encoding kinodynamic information in the edges of a recombinant motion planning search space based on the Efficiently Adaptive State Lattice (EASL). This approach, which we describe as a Kinodynamic Efficiently Adaptive State Lattice (KEASL), differs from the prior representation in two ways. First, this method uses a novel encoding of velocity and acceleration constraints and vehicle direction at expanded nodes in the motion planning graph. Second, this approach describes additional steps for evaluating the roll, pitch, constraints, and velocities associated with poses along each edge during search in a manner that still enables the graph to remain recombinant. Velocities are computed using an iterative bidirectional method using Eulerian integration that more accurately estimates the duration of edges that are subject to terrain-dependent velocity limits. Real-world experiments on a Clearpath Robotics Warthog Unmanned Ground Vehicle were performed in a non-flat, unstructured environment. Results from 2093 planning queries from these experiments showed that KEASL provided a more efficient route than EASL in 83.72% of cases when EASL plans were adjusted to satisfy terrain-dependent velocity constraints. An analysis of relative runtimes and differences between planned routes is additionally presented. These results reinforce the importance of considering kinodynamic constraints for motion planning in non-flat environments and illustrate how such information can be encoded in an adaptive recombinant motion planning search space.

Experimental design of autonomous systems involves defining experimental inputs to maximize the experimenter’s information gained, minimize costs, and balance risk. This effectively leads to improved understanding and trustworthiness, which are necessary for deployment in realworld settings. Since experimental design is inherently a human-in-the-loop, sequential decision making problem, and decisions are being made about complex systems, an investigation into decision-making quality and decision-supporting methods is warranted. In this work, we investigate a decision support system (DSS) to augment the human’s experimental design decision making abilities, and conduct an exploratory user study to investigate the potential for decision support. Our findings show that experimenters, including experienced field roboticists, make suboptimal decisions and mistakes during the experimental design process, which suggests robotics research could benefit from DSSs. Our proposed DSS shows promise in some select aspects of experimental design, including helping to reduce suboptimal decisions, and participants in the user study reported favorable opinions of using such a system, including a sense of usefulness and lack of burden. The broader implication of this work is the identification of decision support in experimental design as one way to help bridge the gap between academia and industry by way of accelerated, informative experimentation and increased system explainability.

Testing and evaluation of field robotic systems requires both experimentation in representative conditions and human supervision to effectively assess components, manage risk, and interpret results. Due to the complexity of robotic sys-tems, we argue this experimentation should be done adaptively by using insights gained from previous trials. Furthermore, we envision an advisory system that could assist experimenters with selecting trial configurations by learning and accounting for human preferences and risk tolerances; however, formal methods for human decision making in the context of field robotic experimentation remains an open question. In this work, we present and analyze a case study for how decisions were made during the testing and evaluation of an off-road, autonomous navigation system. From the perspective of active learning, we find that Bayesian Optimization is a promising mathematical framework for modeling human decision making in adaptive experimental design of field robotics and that a combination of the EI, KG, and PES acquisition functions would likely be useful for realizing an advisory system.