Controlling stochastic switching

Using control to shape stochastic escape and switching dynamics

Dhanushka Kularatne, Eric Forgoston, and M. Ani Hsieh

Abstract: We present a strategy to control the mean stochastic switching times of general dynamical systems with multiple equilibrium states subject to Gaussian white noise. The control can either enhance or abate the probability of escape from the deterministic region of attraction of a stable equilibrium in the presence of external noise. We synthesize a feedback control strategy that actively changes the system’s mean stochastic switching behavior based on the system’s distance to the boundary of the attracting region. With the proposed controller, we are able to achieve a desired mean switching time, even when the strength of noise in the system is not known. The control method is analytically validated using a one-dimensional system, and its effectiveness is numerically demonstrated for a set of dynamical systems of practical importance.

Status: Published online. doi: 10.1063/1.5090113

Come see the ScalAR Lab ICRA 2019 Presentations!

Tahiya Salam will be presenting her RA-L paper entitled “Adaptive Sampling and Reduced Order Modeling of Dynamic Processes by Robot Teams” on Wednesday, May 22, from 11:30am-12:45pm in the WeBT1-03 Interactive Session in 220.

Daniel Mox will be presenting his ICRA 2019 paper entitled “Evaluating the Effectiveness of Perspective Aware Planning with Panoramas” on Tuesday, May 21, from 11:00am-12:15pm in the TuAT1-16 Interactive Session in 220.

ICRA 2019 will be held in Montreal, Canada from May 20-24, 2019. For more info go to

Collaborative Exploration by Human-Robot Teams

This work focuses on developing collaborative exploration strategies that allow robots and humans to learn and co-adapt to exploit synergies within human-robot teams. Many existing collaborative strategies allocate tasks by leveraging each member’s strengths. By allowing robots to learn from their human teammates, humans can transfer some of their tasks to their robotic teammates enabling them to focus on tasks that are particularly challenging for robots, e.g., complex manipulation tasks. The goal of this work is to develop a framework that enables robots to learn domain specific knowledge to improve its execution of a collaborative human-robot tasks.

Initial results include a neural network-aided robot exploration strategy where domain expert knowledge, in conjunction with geometric and topological features around the exploration frontier, is used to improve the performance of a robot-only exploration strategy. The idea is for a robot to learn and apply domain expert knowledge during its selection of next best candidate location for exploration and mapping. We compared our approach to a baseline information-theoretic mapping and exploration strategy where a single robot is tasked to map and explore an unknown office-like environment. Our results shows how domain knowledge can improve the overall performance of the untrained strategy. In particular, our strategy shows a significant increase in the acquisition rate of semantic and topological information of the workspace during exploration.

This work is currently extended to enhance geology practice and workflow. We are collaborating with cognitive scientists and geologists to develop strategic data-collection routines and support scientific inferences in natural environments. Aerial robots equipped with onboard odometry, cameras, and lidars are particular well suited for these tasks since geologists are often interested in rock formations located in regions that are difficult to access by foot. Furthermore, collection of large amounts of surface and three dimensional data can be automated rather than rely on sparse manual measurements. Initial data collection efforts are focused in the Mecca Hills region in Southern California.

Coordinating AGVs for Automated Warehouses

Coordination of multiple AGVs: a quadratic optimization method

Valerio Digani, M. Ani Hsieh, Lorenzo Sabattini, and Cristian Secchi

Abstract: This paper presents an optimization strategy to coordinate a fleet of Automated Guided Vehicles (AGVs) traveling on ad-hoc pre-defined roadmaps. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Program where the optimization is performed in a centralized manner. The proposed method is validated by means of simulations and experiments for different industrial warehouse scenarios. The performance of the proposed strategy is then compared with a recently proposed decentralized coordination strategy that relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughput and significantly minimizes the time vehicles spend negotiating traffic under different scenarios.

Status: Published in Autonomous Robots.  Preprint to come.

Planning Optimal Paths in General Flows

Going With The Flow: A Graph Based Approach to Optimal Path Planning in General Flows

Dhanushka Kularatne, Subhrajit Bhattacharya, and M. Ani Hsieh

Abstract: Autonomous surface and underwater vehicles (ASVs and AUVs) used for ocean monitoring are typically deployed for long periods of time and must operate with limited energy budgets. Coupled with the increased accessibility to ocean flow data, there has been a significant interest in developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow. In this paper, we present a graph search based method to plan time and energy optimal paths in static and time-varying flow fields. We also use tools from topological path planning to generate optimal paths in different homotopy classes to facilitate simultaneous exploration of the environment by multi-robot teams. The proposed strategy is validated using analytical flow models, actual ocean data, and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present an alternative approach using a Riemannian metric based approximation for the cost functions in the static flow case for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based strategy while requiring less computational time.

Status: Accepted and to appear in Autonomous Robots. Preprint to come.

Cooperative Transport by ASVs

Differential Geometric Approach to Trajectory Planning: Cooperative Transport by a Team of Autonomous Marine Vehicles

Hadi Hajieghrary, Dhanushka Kularatne, and M. Ani Hsieh

Abstract: In this paper we addressed the cooperative transport problem for a team of autonomous surface vehicles (ASVs) towing a single buoyant load. We consider the dynamics of the constrained system and decompose the cooperative transport problem into a collection of subproblems. Each subproblem consists of an ASV and load pair where each ASV is attached to the load at the same point. Since the system states evolve on a smooth manifold, we use the tools from differential geometry to model the holonomic constraint arising from the cooperative transport problem and the non-holonomic constraints arising from the ASV dynamics. We then synthesize distributed feedback control strategies using the proposed mathematical modeling framework to enable the team transport the load on a desired trajectory. We experimentally validate the proposed strategy using a team of micro ASVs.

Status: To be presented at ACC 2018.  Preprint to come.

Path Planning with Forecast Uncertainties

Optimal Path Planning in Time-Varying Flows with Forecasting Uncertainties

Dhanushka Kularatne, Hadi Hajieghrary, and M. Ani Hsieh

Abstract: Uncertainties in flow models have to be explicitly considered for effective path planning in marine environments. In this paper, we present two methods to compute minimum expected cost policies and paths over an uncertain flow model. The first method based on a Markov Decision Process computes a minimum expected cost policy while the second graph search based method, computes a minimum expected cost path. A transition probability model is developed to compute the probability of transition from one state to another under a given action. In addition, a method to compute the expected cost of a path when it is executed in an uncertain flow field is also presented. The two methods are used to compute minimum energy paths in an ocean environment and the results are analyzed in simulations.

Status: To be presented at ICRA 2018 in Brisbane, Australia.  Preprint to come.