Stochastic Path Planning for Autonomous Underwater Gliders with Safety Constraints

© 2019 IEEE. Autonomous underwater gliders frequently execute extensive missions with high levels of uncertainty due to limitations of sensing, control and oceanic forecasting. Glider path planning seeks an optimal path with respect to conflicting objectives, such as travel cost and safety, that must be explicitly balanced subject to these uncertainties. In this paper, we derive a set of recursive equations for state probability and expected travel cost conditional on safety, and use them to implement a new stochastic variant of FMT-{ast } in the context of two types of objective functions that allow a glider to reach a destination region with minimum cost or maximum probability of arrival given a safety threshold. We demonstrate the framework using three simulated examples that illustrate how user-prescribed safety constraints affect the results

Decentralised Mission Monitoring with Spatiotemporal Optimal Stopping

© 2018 IEEE. We consider a multi-robot variant of the mission monitoring problem. This problem arises in tasks where a robot observes the progress of another robot that is stochastically following a known trajectory, among other applications. We formulate and solve a variant where multiple tracker robots must monitor a single target robot, which is important because it enables the use of multi-robot systems to improve task performance in practice, such as in marine robotics missions. Our algorithm coordinates the behaviour of the trackers by computing optimal single-robot paths given a probabilistic representation of the other robots' paths. We employ a decentralised scheme that optimises over probability distributions of plans and has useful analytical properties. The planned trajectories collectively maximise the probability of observing the target throughout the mission with respect to probabilistic motion and observation models. We report simulation results for up to 8 robots that support our analysis and indicate that our algorithm is a feasible solution for improving the performance of mission monitoring systems

Communication-aware information gathering with dynamic information flow

© The Author(s) 2014. We are interested in the problem of how to improve estimation in multi-robot information gathering systems by actively controlling the rate of communication between robots. Communication is essential in such systems for decentralized data fusion and decision-making, but wireless networks impose capacity constraints that are frequently overlooked. In order to make efficient use of available capacity, it is necessary to consider a fundamental trade-off between communication cost, computation cost and information value. We introduce a new problem, dynamic information flow, that formalizes this trade-off in terms of decentralized constrained optimization. We propose algorithms that dynamically adjust the data rate of each communication link to maximize an information gain metric subject to constraints on communication and computation resources. The metric is balanced against the communication resources required to transmit data and the computation cost of processing sensor data to form observations. The optimization process selectively routes raw sensor data or processed observation data to zero, one or many robots. Our algorithms therefore allow large systems with many different types of sensors and computational resources to maximize information gain performance while satisfying realistic communication constraints. We also present experimental results with multiple ground robots and multiple sensor types that demonstrate the benefit of dynamic information flow in comparison to simpler bandwidth-limiting methods

An approach to autonomous science by modeling geological knowledge in a Bayesian framework

© 2017 IEEE. Autonomous Science is a field of study which aims to extend the autonomy of exploration robots from low level functionality, such as on-board perception and obstacle avoidance, to science autonomy, which allows scientists to specify missions at task level. This will enable more remote and extreme environments such as deep ocean and other planets to be studied, leading to significant science discoveries. This paper presents an approach to extend the high level autonomy of robots by enabling them to model and reason about scientific knowledge on-board. We achieve this by using Bayesian networks to encode scientific knowledge and adapting Monte Carlo Tree Search techniques to reason about the network and plan informative sensing actions. The resulting knowledge representation and reasoning framework is anytime, handles large state spaces and robust to uncertainty making it highly applicable to field robotics. We apply the approach to a Mars exploration mission in which the robot is required to plan paths and decide when to use its sensing modalities to study a scientific latent variable of interest. Extensive simulation results show that our approach has significant performance benefits over alternative methods. We also demonstrate the practicality of our approach in an analog Martian environment where our experimental rover, Continuum, plans and executes a science mission autonomously

Variable temperature study of the crystal and magnetic structures of the giant magnetoresistant materials LMnAsO (L=La, Nd)

Peer reviewedPublisher PD

Efficient active SLAM based on submap joining

This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy

Fast path planning for precision weeding

Agricultural robots have the potential to reduce herbicide use in agriculture and horticulture through autonomous precision weeding. One of the main challenges is how to efficiently plan paths for a robot arm such that many individual weeds can be processed quickly. This paper considers an abstract weeding task among obstacles and proposes an efficient online path planning algorithm for an industrial manipulator mounted to a mobile robot chassis. The algorithm is based on a multi-query approach, inspired by industrial bin-picking, where a database of high-quality paths is computed offline and paths are then selected and adapted online. We present a preliminary implementation using a 6-DOF arm and report results from simulation experiments designed to evaluate system performance with varying database and obstacle sizes. We also validate the approach using a Universal Robots UR5 manipulator and ROS interface