Minimalist Robot Navigation and Coverage Using a Dynamical System Approach

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

doi:10.1109/irc.2017.23

Cited by 15 publications

(24 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alam et al (2017, 2018) described navigation, coverage, and localization algorithms for a robot that rotates a fixed amount relative to the robot’s prior heading. Owing to the chosen state space discretization, periodic trajectories may exist that require bounce angles not allowed by the discretization.…”

Section: Related Workmentioning

confidence: 99%

A visibility-based approach to computing non-deterministic bouncing strategies

Nilles

Ren

Becerra

et al. 2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

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. We define bounce rules governing how the robot should reorient after reaching a boundary, such as reorienting relative to its heading prior to collision, or relative to the normal of the boundary. We then generate plans as sequences of rules, using the bounce visibility graph generated from a polygonal environment definition, while assuming we have unavoidable non-determinism in our actuation. Our planner can be queried to determine the feasibility of tasks such as reaching goal sets and patrolling (repeatedly visiting a sequence of goals). If the task is found feasible, the planner provides a sequence of non-deterministic interaction rules, which also provide information on how precisely the robot must execute the plan to succeed. We also show how to compute stable cyclic trajectories and use these to limit uncertainty in the robot’s position.

show abstract

Section: Related Workmentioning

confidence: 99%

A visibility-based approach to computing non-deterministic bouncing strategies

Nilles

Ren

Becerra

et al. 2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…In mobile agent research, there are two types of problems: coverage and exploration. Coverage is concerned with patrolling an area, i.e., how to organize the agents so they can maximize the area they can view while still being able to be in contact range of the other agents in a chain [1][2][3][4]. This assumes that the agents already have full understanding of the area to be able to determine the best course of action to achieve these goals.…”

Section: Introductionmentioning

confidence: 99%

Balanced Parallel Exploration of Orthogonal Regions

Clements

Busch

et al. 2019

Algorithms

View full text Add to dashboard Cite

We consider the use of multiple mobile agents to explore an unknown area. The area is orthogonal, such that all perimeter lines run both vertically and horizontally. The area may consist of unknown rectangular holes which are non-traversable internally. For the sake of analysis, we assume that the area is discretized into N points allowing the agents to move from one point to an adjacent one. Mobile agents communicate through face-to-face communication when in adjacent points. The objective of exploration is to develop an online algorithm that will explore the entire area while reducing the total work of all k agents, where the work is measured as the number of points traversed. We propose splitting the exploration into two alternating tasks, perimeter and room exploration. The agents all begin with the perimeter scan and when a room is found they transition to room scan after which they continue with perimeter scan until the next room is found and so on. Given the total traversable points N, our algorithm completes in total O ( N ) work with each agent performing O ( N / k ) work, namely the work is balanced. If the rooms are hole-free the exploration time is also asymptotically optimal, O ( N / k ) . To our knowledge, this is the first agent coordination algorithm that considers simultaneously work balancing and small exploration time.

show abstract

“…This chapter provides the solutions to navigation and coverage tasks using single or multiple bouncing robots. The preliminary version of this work appeared in [ABS17].…”

Section: Bouncing Robot Navigation and Coveragementioning

confidence: 99%

“…Alternatively, we can model the nondeterministic cell-to-cell transitions by using GCM which uses a probabilistic cell transition map. We have implemented and used GCM to model the imperfect rotation of the robot for solving the coverage problem [ABS17], and we believe that it can be used to account for the non-determinism in our localization method.…”

Section: Future Directions and Extensionsmentioning

confidence: 99%

“…More specifically, we apply the generalized cell mapping (GCM)[Hsu13] for finding the long-term behavior of drifters from their projected motion along with uncertainties in the form of a Markov Chain. Our second and third contributions[ABS17,ARBR17] are adapted to solve the deployment and planning problems in a marine environment.5.2 BackgroundMany works have been proposed to study the behavior of aquatic robots and the long-term planning of their trajectories. A path planning and trajectory design for buoyancy-driven AUVs, called gliders, was proposed for analyzing dynamic features using ocean model predictions in the Southern California coastal ocean [SCL + 10].…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Dynamical System Approach for Resource-Constrained Mobile Robotics

Alam¹

Self Cite

View full text Add to dashboard Cite

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

show abstract

Minimalist Robot Navigation and Coverage Using a Dynamical System Approach

Cited by 15 publications

References 15 publications

A visibility-based approach to computing non-deterministic bouncing strategies

A visibility-based approach to computing non-deterministic bouncing strategies

Balanced Parallel Exploration of Orthogonal Regions

A Dynamical System Approach for Resource-Constrained Mobile Robotics

Contact Info

Product

Resources

About