As autonomous systems tackle more real-world situations, mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. We argue there are two main drawbacks to these approaches. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent's dynamics, geometry, and uncertainty. We apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent's dynamics and the user's desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. A variety of 2D and 3D test cases are presented in the full paper including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.

As autonomous systems and robots are applied to more real world situations, they must reason about uncertainty when planning actions. Mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. However, the majority of these methods can only handle simple environment and agent models. We argue that there are two main drawbacks of current approaches to goal-directed motion planning under uncertainty. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent’s dynamics, geometry, and uncertainty. In this article, we apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the autonomous agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent’s dynamics and the user’s desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. These models allow computation of trajectory risk with more complex environments, agent dynamics, geometries, and models of uncertainty than past approaches. A variety of 2D and 3D test cases are presented including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.

Path planning under uncertainty is a difficult and often intractable problem. Autonomous agents must model and reason about complex stochastic processes to quickly derive high quality plans. Most approaches separate the model of uncertainty from the planning; a model is selected and then a controller derived. This work proposes an approach for fast path planning under uncertainty that scales the model of uncertainty such that good policies receive the most effort. To do this, we use an innovative form of the problem's chance constraint to formulate a convex, stochastic path planning problem from the non-convex problem. Next, a bound on the path's expected cost is developed that allows a trade-off between speed of computation and accuracy. The bound is trivially parallelized on a GPU. Finally, a modified branch and bound algorithm is introduced that scales computational effort for more promising solutions. The method is benchmarked against existing approaches including those using Boole's inequality, a MILP approach, and a parallelized sampling-based approach. It outperforms other approaches based on speed and the ability to meet the chance constraint while not being overly conservative.

This paper reports on a study to develop power supplies for small mobile robots performing long duration missions. It investigates the use of fuel cells to achieve this objective, and in particular Proton Exchange Membrane (PEM) fuel cells. It is shown through a representative case study that, in theory, fuel cell based power supplies will provide much longer range than the best current rechargeable battery technology. It also briefly discusses an important limitation that prevents fuel cells from achieving their ideal performance, namely a practical method to store their fuel (hydrogen) in a form that is compatible with small mobile field robots. A very efficient fuel storage concept based on water activated lithium hydride (LiH) is proposed that releases hydrogen on demand. This concept is very attractive because water vapor from the air is passively extracted or waste water from the fuel cell is recycled and transferred to the lithium hydride where the hydrogen is “stripped” from water and is returned to the fuel cell to form more water. This results in higher hydrogen storage efficiencies than conventional storage methods. Experimental results are presented that demonstrate the effectiveness of the approach.