Sensor based terrain guidance of distributed cooperative mobile robots

Abstract

Navigation in outdoor terrain is difficult due to lack of easily and uniquely identifiable landmarks. This problem is further complicated for a system with multiple robots navigating a

<title>Supervised tactical mobility behavior modeling and control of multi-agent robotic vehicles</title>

“…Cooperative deployment activities of MRV's are diverse in nature and hence generalization of their activities is difficult to formulate. For our research investigation purposes, we have designed a set of generic group behavior schemes that are suitable for cooperative deployment activities that encompass coordinated tasks such as: multi-agent robot target searching, tactical marching and formation, multiple target tracking, follow-the-leader schemes, path and road following, and others [17,19,21]. We have developed our cooperative navigational algorithms robotic system platform independent.…”

“…The individual robot navigational techniques handle obstacle detection and avoidance. The navigational methods that we have developed for this purpose include: an adaptive fuzzy-logic-based approach [18], or virtual wall-following [20], a hybrdi reflexive and reactive technique [19] for staying off any obstacle, a neural-net approach based on a two-layer back propagation [18J, and others [21]. Each of our individual navigational schemes uses a specific sensory arrangement for extraction of range information from obstacles around the robot.…”

<title>Behavior-based tactical navigational and mobility control of tandem ground robotic vehicles</title>

“…Potential Fields, Virtual Target Following, and Wall Following navigational techniques [12]. Adaptive Neural-network-based navigation [14], and Fuzzy-Rule-based Navigation HI. IS].…”

“…This loop initially prioritizes mobility assignment of UGV and then using a fuzzy-logic based steering and gap controller generates steering and motion commands for each UGV [14]. The generated steering and motion commands from the fuzzylogic-based steering and gap controller is tested against available kinematic and dynamic model of the UGVs (i.e., in our case against kinematic and dynamic models of Trilobot that we have developed [12]) to ensure motion parameters are within admissible range priori to converting them to motor/actuator commands.…”