Space-Efficient Filters for Mobile Robot Localization from Discrete Limit Cycles

Alam, Tauhidul; Bobadilla, Leonardo; Shell, Dylan A.

doi:10.1109/lra.2017.2737047

Cited by 14 publications

(27 citation statements)

References 21 publications

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“…This is also the total deviation of the generated trajectory from the original trajectory of the drifter. For each of the five number of steps (20,40,60,80,100) in the compass observation sequence, we ran the simulation 50 times using the deterministic method and recorded the estimation error. Then, we compared the results of estimation error for the final location of the trajectory and for the whole trajectory with respect to these various number of steps (20,40,60,80,100) in the compass observation sequence which is illustrated in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…These closely related works are applicable for deep-diving and expensive AUVs that estimate the position through several sensor data fusion. Moreover, a number of closely related limited sensing localization methods for mobile robots have been proposed in [19], [20], [21] where the robots entail less memory and computation to solve their localization task. The authors of this stream of research consider the motion model of simple ground robots whereas we take into account the dynamics of an underwater drifter combined with the water flow pattern.…”

Section: Introductionmentioning

confidence: 99%

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An Underactuated Vehicle Localization Method in Marine Environments

Alam

Reis

Bobadilla

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

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The underactuated vehicles are apposite for the longterm deployment and data collection in spatiotemporally varying marine environments. However, these vehicles need to estimate their positions (states) with intrinsic sensing in their long-term trajectories. In previous studies, autonomous underwater vehicles have commonly used vision and range sensors for autonomous state estimation. Inspired by the intrinsic sensing and the persistent deployment, we investigate the localization problem (state estimation) for an inexpensive and underactuated drifting vehicle called a drifter. In this paper, we present a localization method for the drifter making use of the observations of a proprioceptive sensor, i.e., compass. We create the water flow pattern within a given region from ocean model predictions, develop a stochastic motion model, and analyze the persistent water flow behavior. Given a distribution of initial deployment states of the drifter at a particular depth of the water column within the region and the water flow pattern, our method finds attractors and their transient groups at the given depth as the persistent behavior of the water flow. A most-likely localized trajectory of the drifter for a sequence of compass observations is generated based on the persistent behavior of the water flow and hidden Markov model. Our simulation results based on data from ocean model predictions substantiate good performance of our proposed localization method with a low error rate of the state estimation in the long-term trajectory of the drifter.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Underactuated Vehicle Localization Method in Marine Environments

Alam

Reis

Bobadilla

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

Self Cite

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“…In our first contribution, we use the SCM to synthesize combinatorial filters for solving the localization task [ABS18]. In the second contribution, we use the same SCM method for solving the navigation task using a single bouncing robot and extend this SCM method to tackle the coverage task in multi-robot settings.…”

Section: Motivation and Challengesmentioning

confidence: 99%

“…We consider that each robot knows the map of the environment and its initial configuration. If the initial configuration is unknown, the global robot localization can be solved using our first contribution [ABS18]. Let the set of bouncing angles for a robot be Φ by which it can rotate reliably.…”

Section: Robot Modelmentioning

confidence: 99%

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A Dynamical System Approach for Resource-Constrained Mobile Robotics

Alam¹

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The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. vii

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A Visibility-Based Approach to Computing Nondeterministic Bouncing Strategies

Nilles

Ren

Becerra

et al. 2020

Springer Proceedings in Advanced Robotics

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Inspired by motion patterns of some commercially available mobile robots, we investigate the power of robots that move forward in straight lines until colliding with an environment boundary, at which point they can rotate in place and move forward again; we visualize this as the robot "bouncing" off boundaries. Different boundary interaction rules can be defined for such robots, such as one that orients the robot relative to its heading prior to collision, or relative to the normal of the boundary. We introduce a new data structure, the bounce visibility graph, which is generated from a polygonal environment definition. The bounce visibility graph can be queried to determine the feasibility of path-based tasks such as navigation and patrolling, assuming we have unavoidable nondeterminism in our actuation. If the task is feasible, then this approach synthesizes a strategy (a sequence of nondeterministic rotations). We also show how to compute stable cyclic trajectories and use these to limit uncertainty in the robot's position. 3

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Space-Efficient Filters for Mobile Robot Localization from Discrete Limit Cycles

Cited by 14 publications

References 21 publications

An Underactuated Vehicle Localization Method in Marine Environments

An Underactuated Vehicle Localization Method in Marine Environments

A Dynamical System Approach for Resource-Constrained Mobile Robotics

A Visibility-Based Approach to Computing Nondeterministic Bouncing Strategies

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