Coordinated motion planning for multiple car-like robots using probabilistic roadmaps

Švestka, P.; Overmars, Mark H.

doi:10.1109/robot.1995.525508

Cited by 112 publications

(73 citation statements)

References 4 publications

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“…Svestka and Overmars [3] presented an approach for multiple nonholonomic car-like robots motion planning in the same static workspace by using probabilistic roadmaps, in which the roadmaps for the composite robot are derived from roadmaps for the underlying simple robots, and the latter is computed by a probabilistic single-robot learning method. The authors introduced the notion of super-graphs for multi-robot path planning, and their implementation covered the construction of the simple roadmap and the super-graphs.…”

Section: Related Worǩmentioning

confidence: 99%

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ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Yan

Jouandeau

Chérif

2013

Advances in Intelligent Systems and Computing

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In this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning.Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches.

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Section: Related Worǩmentioning

confidence: 99%

“…One ( Figure 3(a)) is to let robots pass the waypoint one by one [3]. The weakness of this strategy is that one robot must wait for another robot to pass.…”

Section: Waiting Situation Problemmentioning

confidence: 99%

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Yan

Jouandeau

Chérif

2013

Advances in Intelligent Systems and Computing

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“…Considering the presence of other vehicles, it is more generally a multi-robot path planning problem [7,8].…”

Section: Introductionmentioning

confidence: 99%

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Kala

Warwick

2015

Electronics

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Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one's driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios.

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“…Centralized approaches [20] are non-implementable in real time, due to which planning needs to be performed in a decentralized manner [21][22][23]. Pure decentralized approaches cannot though model strong cooperation between the vehicles which is only possible with centralized approaches.…”

Section: Introductionmentioning

confidence: 99%

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Kala

Warwick

2014

Appl Intell

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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.

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Coordinated motion planning for multiple car-like robots using probabilistic roadmaps

Cited by 112 publications

References 4 publications

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

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