Fuzzy Modeling for Control
Tracking Multiple Moving Objects for Real-Time Robot Navigation
Autonomous Robots
Motion Tracking with an Active Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking human motion in an indoor environment
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Object Tracking in Cluttered Background Based on Optical Flows and Edges
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Reactive navigation in dynamic environment using a multisensorpredictor
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An acquisition of operator's rules for collision avoidance using fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A framework for guaranteeing detection performance of a sensor network
Integrated Computer-Aided Engineering - Performance Metrics for Intelligent Systems
Cooperative control for groups of autonomous mobile minirobots
ICAI'08 Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Kinematics-based characterization of the collision course
International Journal of Robotics and Automation
AMF: a novel reactive approach for motion planning of mobile robots in unknown dynamic environments
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Resource allocation strategies for a multi sensor surveillance
CTS'05 Proceedings of the 2005 international conference on Collaborative technologies and systems
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Real-time motion planning in an unknown environment involves collision avoidance of static as well as moving agents. Strategies suitable for navigation in a stationary environment cannot be translated as strategies per se for dynamic environments. In a purely stationary environment all that the sensor can detect can only be a static object is assumed implicitly. In a mixed environment such an assumption is no longer valid. For efficient collision avoidance identification of the attribute of the detected object as static or dynamic is probably inevitable. Presented here are two novel schemes for perceiving the presence of dynamic objects in the robot's neighborhood. One of them, called the Model-Based Approach (MBA) detects motion by observing changes in the features of the environment represented on a map. The other CBA (cluster-based approach) partitions the contents of the environment into clusters representative of the objects. Inspecting the characteristics of the partitioned clusters reveals the presence of dynamic agents. The extracted dynamic objects are tracked in consequent samples of the environment through a straightforward nearest neighbor rule based on the Euclidean metric. A distributed fuzzy controller avoids the tracked dynamic objects through direction and velocity control of the mobile robot. The collision avoidance scheme is extended to overcome multiple dynamic objects through a priority based averaging technique (PBA). Indicating the need for additional rules apart from the PBA to overcome conflicting decisions while tackling multiple dynamic objects can be considered as another contribution of this effort. The method has been tested through simulations by navigating a sensor-based mobile robot amidst multiple dynamic objects and its efficacy established.