Path planning for a mobile robot in a dynamic environment

Bodhale, Dhananjay; Afzulpurkar, Nitin; Thành, Nguyễn Trường

doi:10.1109/robio.2009.4913329

Cited by 33 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This first criterion is the perspective of a view used by the navigation method (reference or egocentric) and the second is the method used for predicting the future positions of dynamic obstacles (model-based or data-driven). The [4], [5], [11]- [13] [2], [3], [6], [14]- [18] Egocentric view [19], [9] This work classification of state-of-the-art algorithms and our proposed method is summarized in Table I. In a reference view approach, the position of a robot and other obstacles are assumed to be given or measured by external devices.…”

Section: Related Workmentioning

confidence: 99%

Real-time navigation in crowded dynamic environments using Gaussian process motion control

Choi

Kim

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this paper, we propose a novel Gaussian process motion controller that can navigate through a crowded dynamic environment. The proposed motion controller predicts future trajectories of pedestrians using an autoregressive Gaussian process motion model (AR-GPMM) from the partiallyobservable egocentric view of a robot and controls a robot using an autoregressive Gaussian process motion controller (AR-GPMC) based on predicted pedestrian trajectories. The performance of the proposed method is extensively evaluated in simulation and validated experimentally using a Pioneer 3DX mobile robot with a Microsoft Kinect sensor. In particular, the proposed method shows over 68% improvement on the collision rate compared to a reactive planner and vector field histogram (VFH).

show abstract

Section: Related Workmentioning

confidence: 99%

Real-time navigation in crowded dynamic environments using Gaussian process motion control

Choi

Kim

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…The mathematical model of atmospheric threat is formula (2). r is the radius of the threat cylinder; d is the distance that a point to the central axis of cylindrical.…”

Section: Atmosphere Threatmentioning

confidence: 99%

“…Nowadays, most of route planning algorithm is based on the certain and static environment such as Dijkstra [1] algorithm and A* algorithm [2][3] . With the development of the air-defense system and the transformation of combat thinking, the autonomous route planning is imperative.…”

Section: Introductionmentioning

confidence: 99%

Autonomous route planning for UAV when threats are uncertain

Zhang

Pei

2014

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference

View full text Add to dashboard Cite

Route planning in 3-D uncertainty environment, especially when the threats are uncertain, is the trend of the UAV (unmanned aerial vehicle) route planning. Against the opacity of enemy threat information, the threat probability model is adopted to determine the location of the threat, and D-S evidence synthesis algorithm is used to determine the threat strength, then to calls D* route planning algorithm getting UAV flight route and forming the "flying, judging and planning" autonomous planning pattern. Paper also analyzes various threats and then establishes 3-D models for these threats. The method turns out to be feasible by simulation.

show abstract

“…However the study of new hybrid navigation technique could be a better option for real environments. This technique is the join of a global navigation path planning method based on camera [1], and a sensor-based obstacle evasion system [2] which works on dynamic environments due to its local navigation [1]. For observable and static environments there are several path planning methods such as: path maps, cell decompose and artificial potential fields.…”

Section: Introductionmentioning

confidence: 99%

Path planning for mobile robots based on visibility graphs and A* algorithm

2015

View full text Add to dashboard Cite

One of most worked issues in the last years in robotics has been the study of strategies to path planning for mobile robots in static and observable conditions. This is an open problem without pre-defined rules (non-heuristic), which needs to measure the state of the environment, finds useful information, and uses an algorithm to select the best path. This paper proposes a simple and efficient geometric path planning strategy supported in digital image processing. The image of the environment is processed in order to identify obstacles, and thus the free space for navigation. Then, using visibility graphs, the possible navigation paths guided by the vertices of obstacles are produced. Finally the A* algorithm is used to find a best possible path. The alternative proposed is evaluated by simulation on a large set of test environments, showing in all cases its ability to find a free collision plausible path.

show abstract

Path planning for a mobile robot in a dynamic environment

Cited by 33 publications

References 6 publications

Real-time navigation in crowded dynamic environments using Gaussian process motion control

Real-time navigation in crowded dynamic environments using Gaussian process motion control

Autonomous route planning for UAV when threats are uncertain

Path planning for mobile robots based on visibility graphs and A* algorithm

Contact Info

Product

Resources

About