Detection, tracking and avoidance of multiple dynamic objects

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 In a mixed environment such an assumption is no longer valid.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.