References
- Aine, S. and Likhachev, M. (2016). Truncated incremental search, Artificial Intelligence 234: 49-77.
- Belter, D., Łabecki, P., Fankhauser, P. and Siegwart, R. (2016). RGB-D terrain perception and dense mapping for legged robots, International Journal of Applied Mathematics and Computer Science 26(1): 81-97, DOI: 10.1515/amcs-2016-0006.
- Hart, P.E., Nilsson, N.J. and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systems Science and Cybernetics 4(2): 100-107.
- Hernández, C., Asín, R. and Baier, J.A. (2015). Reusing previously found A* paths for fast goal-directed navigation in dynamic terrain, 19th AAAI Conference on Artificial Intelligence, Austin, TX, USA, pp. 1158-1164.
- Hernández, C., Baier, J.A. and Asín, R. (2014). Making A* run faster than D*-Lite for path-planning in partially known terrain, Proceedings of the 24th International Conference on Automated Planning and Scheduling, Portsmouth, NH, USA, pp. 504-508.
- Hernández, C., Meseguer, P., Sun, X. and Koenig, S. (2009). Path-Adaptive A* for incremental heuristic search in unknown terrain, Proceedings of the 19th International Conference on Automated Planning and Scheduling, Thessaloniki, Greece, pp. 358-361.
- Hernández, C., Sun, X., Koenig, S. and Meseguer, P. (2011). Tree Adaptive A*, 10th International Conference on Autonomous Agents and Multiagent Systems, Taipei, Taiwan, Vol. 1, pp. 123-130.
- Koenig, S. and Likhachev, M. (2001). Improved fast replanning for robot navigation in unknown terrain, Technical Report GIT-COGSCI-2002/3, Georgia Institute of Technology, Atlanta, GA.
- Koenig, S. and Likhachev, M. (2005a). Adaptive A*, Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, Utrecht, The Netherlands, pp. 1311-1312.
- Koenig, S. and Likhachev, M. (2005b). Fast replanning for navigation in unknown terrain, IEEE Transactions on Robotics 21(3): 354-363.
- Koenig, S., Likhachev, M. and Furcy, D. (2004). Lifelong planning A*, Artificial Intelligence 155(1): 93-146.
- Koenig, S. and Sun, X. (2009). Comparing real-time and incremental heuristic search for real-time situated agents, Autonomous Agents and Multi-Agent Systems 18(3): 313-341.
- Likhachev, M., Ferguson, D.I., Gordon, G.J., Stentz, A. and Thrun, S. (2005). Anytime Dynamic A*: An anytime, replanning algorithm, Proceedings of the 15th International Conference on Automated Planning and Scheduling, Monterey, CA, USA, pp. 262-271.
- Podsędkowski, L. (1998). Path planner for nonholonomic mobile robot with fast replanning procedure, 1998 IEEE International Conference on Robotics and Automation, Lueven, Belgium, Vol. 4, pp. 3588-3593.
- Podsędkowski, L., Nowakowski, J., Idzikowski, M. and Vizvary, I. (2001). A new solution for path planning in partilly known or unknown environment for nonholonomic mobile robots, Robotics and Autonomous Systems 34(2): 145-152.
- Przybylski, M., Koguciuk, D., Siemia˛tkowska, B., Harasymowicz-Boggio, B. and Chechli´nski, Ł. (2015).Integration of qualitative and quantitative spatial data within a semantic map for service robots, in R. Szewczyk et al. (Eds.), Progress in Automation, Robotics and Measuring Techniques. Volume 2: Robotics, Springer, Cham, pp. 223-232.
- Przybylski, M. and Siemia˛tkowska, B. (2012). A new CNN-based method of path planning in dynamic environment, in L. Rutkowski et al. (Eds.), Artificial Intelligence and Soft Computing, ICAISC 2012, Lecture Notes in Computer Science, Vol. 7268, Springer, Berlin/Heidelberg, pp. 484-492.
- Stentz, A. (1994). Optimal and efficient path planning for partially-known environments, Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, CA, USA, Vol. 4, pp. 3310-3317.
- Stentz, A. (1995). The Focussed D* algorithm for real-time replanning, Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, Vol. 2, pp. 1652-1659.
- Sturtevant, N.R. (2012). Benchmarks for grid-based pathfinding, IEEE Transactions on Computational Intelligence and AI in Games 4(2): 144-148.
- Sun, X. and Koenig, S. (2007). The Fringe-Saving A* search algorithm-a feasibility study, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 2391-2397.
- Sun, X., Koenig, S. and Yeoh, W. (2008). Generalized Adaptive A*, Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, Estoril, Portugal, Vol. 1, pp. 469-476.
- Trovato, K.I. (1990). Differential A*: An adaptive search method illustrated with robot path planning for moving obstacles and goals, and an uncertain environment, International Journal of Pattern Recognition and Artificial Intelligence 4(2): 245-268.
- Trovato, K.I. and Dorst, L. (2002). Differential A*, IEEE Transactions on Knowledge and Data Engineering 14(6): 1218-1229.
- van Toll, W. and Geraerts, R. (2015). Dynamically Pruned A* for re-planning in navigation meshes, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, pp. 2051-2057.