Reactive navigation in real environments using partial center of area method

Sánchez, José Luis Guzmán; López, Félix de la Paz; Troncoso, José Manuel Cuadra; Sierra, Daniel de Santos

doi:10.1016/j.robot.2010.05.009

Cited by 19 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Methods based on the reactive paradigm perform well with holonomic robots in spatially restricted scenarios with static obstacles while fulfilling safety and legal requirements. Common methods are the potential field method [1], velocity obstacle method [2], dynamic window approach [3], or partial center of area method [4]. However, non-holonomic robots might be difficult to integrate with the reactive paradigm since they need to plan their movement ahead in order to avoid collision with obstacles.…”

Section: A Reactive Paradigmmentioning

confidence: 99%

Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

Pintos Gómez de las Heras,

Martínez-Tomás,

Cuadra Troncoso

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms.

show abstract

Section: A Reactive Paradigmmentioning

confidence: 99%

Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

Pintos Gómez de las Heras,

Martínez-Tomás,

Cuadra Troncoso

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Required turns are made in a manner so as to keep oneself at approximately the same relative position compared to others. (b) The vehicle always attempts to move such that its separation from obstacles or road boundaries is as large as possible [34]. However the attempt to maximize the separation is limited to a value of sm.…”

Section: State Reductionmentioning

confidence: 99%

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Kala

Warwick

2014

Appl Intell

View full text Add to dashboard Cite

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

show abstract

“…The above authors identified scenarios called roaming trails, where a robot was disallowed to move to a position from which it cannot proceed as per the deliberative plan. In a similar work, Álvarez‐Sánchez et al placed the heuristic that robot's immediate move is toward the center of area of obstacle‐free cone bounded by humans and obstacles. This was inspired by human motion amid a large crowd.…”

Section: Related Workmentioning

confidence: 99%

“…This is a valid strategy in the environments involving dynamic obstacles and humans. The dynamic obstacles may clear with time, while the humans are usually cooperative in motion and move to create space for other's motion . Stopping is, however, not a good strategy when operating in an environment with multiple different robots.…”

Section: Introductionmentioning

confidence: 99%

Navigating Multiple Mobile Robots without Direct Communication

Kala

2014

Int. J. Intell. Syst.

View full text Add to dashboard Cite

A bulk of research is being done for the autonomous navigation of a mobile robot. Multirobot motion planning techniques often assume a direct communication among the robots, which makes them practically unusable. Similarly, approaches assuming the robot moving amid humans assume cooperation of humans, which may not be the case if the human is replaced by a robot. In this paper, a deliberative planning at the higher level with a new cell decomposition technique is presented, along with a reactive planning technique at the finer level, which uses fuzzy logic. Coordination among the robots in the absence of direct communication and knowledge of other robot's intent is a complex research question, which is solved using a simple fuzzy-based modeling. Experimental results show that the multiple robots maintain comfortable distances from the obstacles, navigate by near optimal paths, can easily escape previously unseen obstacles, and coordinate with each other to avoid collision as well as maintain a large separation. This work displays a simple and easy to interpret system for solving complex coordination problem in multirobotics. C 2014 Wiley Periodicals, Inc.

show abstract

Reactive navigation in real environments using partial center of area method

Cited by 19 publications

References 8 publications

Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Navigating Multiple Mobile Robots without Direct Communication

Contact Info

Product

Resources

About