Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
A message ferrying approach for data delivery in sparse mobile ad hoc networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Intelligent fluid infrastructure for embedded networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Novel self-configurable positioning technique for multihop wireless networks
IEEE/ACM Transactions on Networking (TON)
Mobile Element Based Differentiated Message Delivery in Wireless Sensor Networks
WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
Adaptive sink mobility in event-driven multi-hop wireless sensor networks
InterSense '06 Proceedings of the first international conference on Integrated internet ad hoc and sensor networks
A Parallel Implementation of the Simplex Function Minimization Routine
Computational Economics
SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Using predictable observer mobility for power efficient design of sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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This paper presents simple and effective technologies that support autonomous robotic exploration in wireless sensor networks where neither location information nor global network knowledge is known by the robot or sensors. Under our proposed approach, the sensor network is divided into convex partitions. In each partition, a small constant number of sensor nodes are identified as landmarks to establish a virtual coordinates system. Then, the robot employs a progressive refinement algorithm based on inaccurate virtual coordinates to discover the targets. Both the coordinates establishment and the robotic exploration are based on local information obtained in sensor networks. The algorithms are light-weight, with very limited computation and communication overhead introduced to the robot and sensors. The proposed approach is prototyped and experimentally evaluated by using an iRobot Create(R) Programmable robot and 36 Crossbow MICAz motes, providing useful empiric insights under realistic environments. Simulations are further carried out to study its performance in large scale networks.