A new algorithm for robot curve-following amidst unknown obstacles, and a generalization of maze-searching

Sankaranarayanan, Aswin C.; Masuda, I.

doi:10.1109/robot.1992.220067

Cited by 15 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional obstacle avoidance techniques can be adapted to control the way how drones can prevent exposure to such intangible areas. We adapt strategies from the field of robotics [5], e.g., potentialfunction-based control [6], [7], curve following [8] strategies and bug algorithms [9]. There are also studies (some of which are very recent) on various collision and obstacle avoidance approaches for drones [10]- [13].…”

Section: Related Workmentioning

confidence: 99%

Risky Zone Avoidance Strategies for Drones

Barbeau

García-Alfaro

Kranakis

2021

2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

We consider the problem of a drone having to traverse a terrain. Traversal of the terrain exposes the drone to certain risks, e.g., concentration of chemicals, severe thunderstorm wind gusts or any disturbing weather phenomenon. The goal of the drone is to navigate the terrain while minimizing the amount of risk. We develop a framework for quantifying the exposure to risk factors in a circular zone model. We propose risky zone avoidance navigation strategies for rectilinear or curvilinear drone trajectories. We validate the work using numeric simulations.

show abstract

Section: Related Workmentioning

confidence: 99%

Risky Zone Avoidance Strategies for Drones

Barbeau

García-Alfaro

Kranakis

2021

2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

show abstract

“…(3) Reverse condition: A robot may change its following direction if it returned a visited position (identified a loop) [3; 5; 9]. (4) Topologic condition: This is designed under the Jordan's curve theorem by using the segment q and its intersecting obstacles [7]. (5) Geometric condition: This is designed by obstacle geometry such as a variation of tangential vectors [8; 9; 10].…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are categorized into the Metric algorithm. As contrasted with this, the algorithm gurvI is designed by the conditions (2) and (4) [7], and therefore it is called the Topologic algorithm. The algorithms Topology I and II have the conditions (3) and (5) [9], and the algorithm Robust includes the conditions (2) and (5) [8].…”

Section: Introductionmentioning

confidence: 99%

On the Sensor-Based Navigation Under Position and Orientation Errors

Noborio¹,

Yoshioka²

2000

Auto

View full text Add to dashboard Cite

A mobile robot usually estimates its position and orientation by a dead reckoning system. However under several kinds of uncertainties, e.g. inner/outer sensor errors and/or a slip, a mobile robot unfortunately misunderstands true position and orientation in a real world as false position and orientation in a computer world. Due to the difference, a mobile robot governed by sensor-based navigation sometimes gets lost in an unknown 2-D environment. On the observation, in this paper, we firstly improve previous metric sensor-based navigation algorithms in order for a mobile robot to deal with such uncertainties. Secondly, we name a class of errors a homeomorpic error, where true and false positions in real and computer worlds are projected each other by the homeomorphism. Finally, we theoretically show that generation of the homeomorpic error is the necessary and sufficient condition for a mobile robot not to enter into any limit cycle in every type of sensor-based navigation algorithm.

show abstract

“…Because the environment of the construction site is unstructured and changing, it is reasonable to apply the path planning algorithm in an unknown environment to the construction site. The path planning algorithm in an unknown environment includes Bugl [7], Bug2 [7], VisBug [8], Curvl [12], Azimuth [9], Tangent [5,6], and DistBug [2].…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Algorithm for Automated Construction Equipment

Lee¹,

Adams²

1999

Proceedings of the 16th IAARC/IFAC/IEEE International Symposium on Automation and Robotics in Construction

View full text Add to dashboard Cite

This paper presents a new sensor-based path planning algorithm, the ACE algorithm, in unknown environment with multiple moving obstacles. The ACE algorithm is designed for an automated construction equipment (ACE). The algorithm is based on the practical assumptions that fit to the construction environment. It is assumed that the robot can measure instantaneous velocity of obstacles in a range of vision and has a cache memory to memorize a generated path from a tracking point. The ACE algorithm is an extension of the Tangent algorithm [5, 6]. The ACE algorithm guarantees reachability in an unknown environment with not only multiple moving obstacles but also composite obstacles. If an obstacle that is being tracked moves and/or if the robot returns to the generated path after a tracking point in clockwise tracking direction, the ACE algorithm terminates Tracking mode and resumes Toward Destination mode.

show abstract

A new algorithm for robot curve-following amidst unknown obstacles, and a generalization of maze-searching

Cited by 15 publications

References 4 publications

Risky Zone Avoidance Strategies for Drones

Risky Zone Avoidance Strategies for Drones

On the Sensor-Based Navigation Under Position and Orientation Errors

A Path Planning Algorithm for Automated Construction Equipment

Contact Info

Product

Resources

About