Real-world applications of path planning must contend with complicated constraint and objective functions imposed by the surrounding operational and regulatory environment. Traditional methods such as PRM* and RRT* have asymptotic guarantees, but often struggle in practice with complex blackbox objective/constraint functions, especially in compute-limited situations. Continuous Belief Tree Search (CBTS) addresses these limitations by maintaining local estimates of the objective function in order to sample new nodes from continuous space, often giving high-quality solutions more quickly. However, CBTS requires careful tuning of a control duration parameter, which introduces a tradeoff between compute time and path cost/feasibility. In environments with complex costs and constraints, there may be no single control duration that gives good paths in short compute time. This paper proposes Trust Region CBTS (TR-CBTS), an extension of CBTS with an adaptive control duration parameter inspired by trust region methods. TR-CBTS adjusts control duration based on information from recently sampled candidate nodes, allowing longer control duration where possible to speed up compute time, and shortening control duration when precise navigation in environments with complex, unknown constraint and objective functions. We show TR-CBTS outperforms existing comparable planners for a realistic robotic path planning application in autonomous ship routing.

Improving the safety and efficiency of maritime shipping has the potential to reduce carbon emissions and improve profitability. Stochastic ship routing, the problem of finding a safe, efficient, and timely route for a ship is difficult in large part because of uncertainty in weather forecasts, which come as an ensemble, a collection of many possible future weather conditions. Previous safety-aware ship routing methods have used either conservative interpretations of the ensemble by assuming the worst-case weather conditions, leading to excessive fuel consumption, or by using the average weather conditions, leading to potentially unsafe routes. In this paper, we investigate the use of the well-known Conditional Value-at-risk (CVaR) in the objective and constraint functions for ship routing problems, which allows a range of risk tolerances between the average and the worst case. We illustrate the advantages of using CVaR for the problem of ship routing in several simulation examples using real weather forecasts.

Climbing robots have the potential to revolutionize the maintenance and inspection operations of many types of vertical structures. In nature, parrots exhibit a remarkable capacity for manipulation during climbing behaviors, for which robotics can benefit from studying. In this paper we present a novel tripedal robot that is inspired by the morphology of these impressive birds, which use their legs and beak in a tripedal fashion when climbing. We propose several foot placement, trajectory generation, and control methods for this system along with performance evaluation in simulation. A video of select simulations and live bird data is included in the supplementary material, and can also be found at https://youtu.be/vRVGralyQgQ.

Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We de ne this decomposition as the scout–task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout–task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.

We present a method to estimate two-dimensional, time-invariant oceanic flow fields based on data from both ensemble forecasts and online measurements. Our method produces a realistic estimate in a computationally efficient manner suitable for use in marine robotics for path planning and related applications. We use kernel methods and singular value decomposition to find a compact model of the ensemble data that is represented as a linear combination of basis flow fields and that preserves the spatial correlations present in the data. Online measurements of ocean current, taken for example by marine robots, can then be incorporated using recursive Bayesian estimation. We provide computational analysis, performance comparisons with related methods, and demonstration with real-world ensemble data to show the computational efficiency and validity of our method. Possible applications in addition to path planning include active perception for model improvement through deliberate choice of measurement locations.

Estimating ocean flow fields in 3D is a critical step in enabling the reliable operation of underwater gliders and other small, low-powered autonomous marine vehicles. Existing methods produce depth-averaged 2D layers arranged at discrete vertical intervals, but this type of estimation can lead to severe navigation errors. Based on the observation that real-world ocean currents exhibit relatively low vertical velocity components, we propose an accurate 3D estimator that extends our previous work in estimating 2D flow fields as a linear combination of basis flows. The proposed algorithm uses data from ensemble forecasting to build a set of 3D basis flows, and then iteratively updates basis coefficients using point measurements of underwater currents. We report results from experiments using actual ensemble forecasts and synthetic measurements to compare the performance of our method to the direct 3D extension of the previous work. These results show that our method produces estimates with dramatically lower error metrics, with and without measurement noise.

Dynamic walking robots have the potential for efficient and lifelike locomotion, but computing efficient gaits and tracking them is difficult in the presence of under-modeling. Iterative Learning Control (ILC) is a method to learn the control signal to track a periodic reference over several attempts, augmenting a model with online data. Terminal ILC (TILC), a variant of ILC, allows other performance objectives to be addressed at the cost of ignoring parts of the reference. However, dynamic walking robot gaits are not necessarily periodic in time. In this paper, we adapt TILC to jointly optimize final foot placement and energy efficiency on dynamic walking robots by indexing by a phase variable instead of time, yielding “phase-indexed TILC” (θ - TILC). When implemented on a five-link walker in simulation, θ- TILC learns a more energy-efficient walking motion compared to traditional time-indexed TILC.