Viable path planning for data collection robots in a sensing field with obstacles

Abstract Introducing mobile robots into wireless sensor networks to collect data can balance the energy dissipation through the entire network and then improve the network lifetime. In this context, path management is a crucial problem since it impacts on the system performance such as data delivery delay and energy consumption. In this paper, we consider the problem of planning paths for unicycle robots (with constant speed and bounded angular velocity) to visit a set of sensor nodes in a sensing field with obstacles while minimizing the path lengths. Several practical issues that have not been sufficiently addressed so far are taken into account. As the combination of concerns from robotics and sensor networks, a viable path is defined, which is smooth, collision free with sensor nodes and obstacles, closed and let the robot to read all the data from sensor nodes. To design the shortest viable path for the underlying robots, a Shortest Viable Path Planning (SVPP) algorithm is first developed for single robot. Due to the physical speed, using single robot to collect data in a large scale network may result in a long collection time. Then, a k -Shortest Viable Path Planning ( k -SVPP) algorithm to construct k paths of approximately equal lengths is proposed. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithms and show their advantages over the alternatives.

[1]  Turgay Korkmaz,et al.  Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks , 2015, Comput. Commun..

[2]  Joseph S. B. Mitchell,et al.  Approximation algorithms for TSP with neighborhoods in the plane , 2001, SODA '01.

[3]  Volkan Isler,et al.  Data gathering tours for mobile robots , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Jianping Pan,et al.  A Progressive Approach to Reducing Data Collection Latency in Wireless Sensor Networks with Mobile Elements , 2013, IEEE Transactions on Mobile Computing.

[5]  R. Ernst,et al.  Scenario Aware Analysis for Complex Event Models and Distributed Systems , 2007, RTSS 2007.

[6]  Elyes Ben Hamida,et al.  Strategies for data dissemination to mobile sinks in wireless sensor networks , 2008, IEEE Wireless Communications.

[7]  Xing Zhang,et al.  A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets , 2014 .

[8]  Paolo Toth,et al.  Vehicle Routing , 2014, Vehicle Routing.

[9]  Mani B. Srivastava,et al.  Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines , 2004, 25th IEEE International Real-Time Systems Symposium.

[10]  L. Dubins On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[11]  Charalampos Konstantopoulos,et al.  A Rendezvous-Based Approach Enabling Energy-Efficient Sensory Data Collection with Mobile Sinks , 2012, IEEE Transactions on Parallel and Distributed Systems.

[12]  Mario Fernando Montenegro Campos,et al.  An evolutionary approach for the dubins' traveling salesman problem with neighborhoods , 2012, GECCO '12.

[13]  Ümit Özgüner,et al.  Motion planning for multitarget surveillance with mobile sensor agents , 2005, IEEE Transactions on Robotics.

[14]  D. Struik Lectures on classical differential geometry , 1951 .

[15]  Alan M. Frieze,et al.  On the worst-case performance of some algorithms for the asymmetric traveling salesman problem , 1982, Networks.

[16]  Guoliang Xing,et al.  Rendezvous design algorithms for wireless sensor networks with a mobile base station , 2008, MobiHoc '08.

[17]  Raja Sengupta,et al.  A Resource Allocation Algorithm for Multivehicle Systems With Nonholonomic Constraints , 2007, IEEE Transactions on Automation Science and Engineering.

[18]  Waylon Brunette,et al.  Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks , 2003, Ad Hoc Networks.

[19]  Donghyun Kim,et al.  Minimizing data collection latency in wireless sensor network with multiple mobile elements , 2012, 2012 Proceedings IEEE INFOCOM.

[20]  Chul E. Kim,et al.  Approximation Algorithms for Some Routing Problems , 1978, SIAM J. Comput..

[21]  G. Laporte The traveling salesman problem: An overview of exact and approximate algorithms , 1992 .

[22]  Dimitrios Hatzinakos,et al.  Architecture of Wireless Sensor Networks With Mobile Sinks: Sparsely Deployed Sensors , 2007, IEEE Transactions on Vehicular Technology.

[23]  Sajal K. Das,et al.  Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey , 2011, TOSN.

[24]  A. Matveev,et al.  Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey , 2014, Robotica.

[25]  Weijia Jia,et al.  Rendezvous Planning in Mobility-Assisted Wireless Sensor Networks , 2007, RTSS 2007.

[26]  Natalija Vlajic,et al.  Strategies for improving performance of IEEE 802.15.4/ZigBee WSNs with path-constrained mobile sink(s) , 2011, Comput. Commun..

[27]  Frank Ortmeier,et al.  Robotic Task Sequencing Problem: A Survey , 2015, Journal of Intelligent & Robotic Systems.

[28]  J. C. Bean,et al.  An efficient transformation of the generalized traveling salesman problem , 1993 .

[29]  Chang-Gun Lee,et al.  Partitioning based mobile element scheduling in wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[30]  Andrey V. Savkin,et al.  Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments , 2012, Robotica.

[31]  Andrey V. Savkin,et al.  Navigation of a unicycle-like mobile robot for environmental extremum seeking , 2011, Autom..

[32]  Guoliang Xing,et al.  Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks , 2013, IEEE Trans. Mob. Comput..

[33]  Emilio Frazzoli,et al.  On the Dubins Traveling Salesman Problem , 2012, IEEE Transactions on Automatic Control.

[34]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[35]  Guihai Chen,et al.  Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks , 2014, Comput. Commun..

[36]  João Pedro Hespanha,et al.  Dubins Traveling Salesman Problem with Neighborhoods: A Graph-Based Approach , 2013, Algorithms.

[37]  F. Bullo,et al.  On the point-to-point and traveling salesperson problems for Dubins' vehicle , 2005, Proceedings of the 2005, American Control Conference, 2005..

[38]  Eugene L. Lawler,et al.  The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization , 1985 .

[39]  S. Darbha,et al.  Sampling-Based Roadmap Methods for a Visual Reconnaissance UAV ∗ , 2010 .

[40]  José D. P. Rolim,et al.  Biased Sink Mobility with Adaptive Stop Times for Low Latency Data Collection in Sensor Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[41]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[42]  Xiuzhen Cheng,et al.  Localized Outlying and Boundary Data Detection in Sensor Networks , 2007 .

[43]  Yuanyuan Yang,et al.  SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks , 2007, IEEE Trans. Parallel Distributed Syst..

[44]  Jan Faigl On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning , 2016, WSOM.

[45]  Yu Gu,et al.  The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[46]  Andrey V. Savkin,et al.  Safe Robot Navigation Among Moving and Steady Obstacles , 2015 .

[47]  Weifa Liang,et al.  Data Collection Maximization in Renewable Sensor Networks via Time-Slot Scheduling , 2015, IEEE Transactions on Computers.

[48]  Ye Xia,et al.  Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications , 2010, IEEE Transactions on Mobile Computing.

[49]  Andrey V. Savkin,et al.  Range-only measurements based target following for wheeled mobile robots , 2011, Autom..

[50]  Maria E. Orlowska,et al.  On the Optimal Robot Routing Problem in Wireless Sensor Networks , 2007 .

[51]  Jian Li,et al.  Analytical modeling and mitigation techniques for the energy hole problem in sensor networks , 2007, Pervasive Mob. Comput..

[52]  Andrey V. Savkin,et al.  A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance , 2011, Autom..

[53]  Krishna M. Sivalingam,et al.  Energy-efficient mobile data collection in Wireless Sensor Networks with delay reduction using wireless communication , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).

[54]  Donghyun Kim,et al.  Minimum Latency Multiple Data MULE Trajectory Planning in Wireless Sensor Networks , 2014, IEEE Transactions on Mobile Computing.

[55]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[56]  Bruce H. Krogh,et al.  Energy-efficient surveillance system using wireless sensor networks , 2004, MobiSys '04.

[57]  Hongke Zhang,et al.  Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks , 2011, IEEE Trans. Mob. Comput..

[58]  Jan Faigl,et al.  On the Dubins Traveling Salesman Problem with Neighborhoods , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[59]  Tim Roughgarden,et al.  Planning Tours of Robotic Arms among Partitioned Goals , 2006, Int. J. Robotics Res..

[60]  Ioannis Chatzigiannakis,et al.  Sink mobility protocols for data collection in wireless sensor networks , 2006, MobiWac '06.

[61]  George B. Dantzig,et al.  The Truck Dispatching Problem , 1959 .

[62]  Mani B. Srivastava,et al.  Mobile Element Scheduling with Dynamic Deadlines , 2007, IEEE Transactions on Mobile Computing.