行動式機器人空間認知與運動規劃

從冷酷的機器到可愛的電子寵物，隨著科技的發展，機器人持續不斷衝擊著我們的生活觀點。在不久的將來，機器人將頻繁出現在人類生活環境中，像是學校、醫院、辦公大樓、博物館、以及一般家庭等等區域。為了要使得機器人更容易被人們所接受，機器人必須理解環境中的人類行為與相對應的空間關係。本論文目的旨在發展機器人對於人類社會空間認知的學習方法，以及強健的行動式機器人運動規劃演算法。對於空間的呈現方式，提出幾種不同的新穎架構。在拓墣層次上，本論文提出SLAMMOT-SP的架構用以同步完成SLAMMOT與場景的預測。在認知科學層次上，為了建構廣義的行人模型，本論文進而發展SBCM、PEG與運動單元學習等概念。同時也進一步推導出空間推論與行為預測的機率模型。此外在機器人運動規劃上，為了滿足任意時間回傳軌跡、快速重新規劃軌跡、與不確定性考量等因素，本論文提出幾種不同的運動規劃演算法如DAO*、DDAO*與任意時間預測型A*演算法。最後本論文展示藉由結合空間認知與相對應之運動規劃演算法，機器人能夠展現預測行人意圖與預測行人未來長期軌跡的能力，同時機器人也能表現出符合人類社交規範的行為。

關鍵字

環境認知；行為理解；運動規劃；同步建圖與定位；行動式機器人

並列摘要

From cold working machines to lovely electric pets, the robots are likely to continue to impact various aspects of our lives. In the near future, robots will consistently appear in human communities in schools, hospitals, offices, museums, and households etc. For robots to be socially accepted by humans, robots must have the ability to understand human behaviors and spatial relationships within environments. This dissertation attempts to develop the learning methods for spatial understanding of human society and the robust motion planning algorithms of mobile robots. New frameworks in different spatial representations are established. On the topological level, SLAMMOT-SP is introduced for simultaneous SLAMMOT and scene prediction. On the cognitive level, SBCM, PEG, and the concept of motion primitive learning are proposed to model generalized pedestrian behaviors. The probability models for spatial reasoning and behavior prediction are also derived. Moreover, several planning algorithms, DAO*, DDAO*, and predictive anytime A*, are presented to satisfy the requirements of anytime, fast replanning, and uncertainty concerns. Finally, we demonstrate that the robot is capable of predicting the intentions and long-term trajectories of pedestrians, and further behaving socially acceptable motions by combining planning algorithms with spatial reasoning.

並列關鍵字

Scene Understanding ； Behavior Understanding ； Motion Planning ； SLAM ； Mobile Robot

參考文獻

[4] A. G. Barto, S. J. Bradtke, and S. P. Singh, "Learning to Act Using Real-Time Dynamic Programming," Artificial Intelligence, vol. 72, pp. 81-138, 1995.

[5] P. Beeson, J. Modayil, and B. Kuipers, "Factoring the Mapping Problem: Mobile Robot Map-Building in the Hybrid Spatial Semantic Hierarchy," Int. J. of Robotics Research, vol. 29, pp. 428-459, April 1, 2010.

[6] K. Belghith, F. Kabanza, L. Hartman, and R. A. N. R. Nkambou, "Anytime Dynamic Path-Planning with Flexible Probabilistic Roadmaps," Proc. IEEE Int. Conf. on Robotics and Automation, pp. 2372-2377, 2006.

[7] M. Bennewitz, W. Burgard, G. Cielniak, and S. Thrun, "Learning Motion Patterns of People for Compliant Robot Motion," Int. J. Robotics Research, vol. 24, pp. 31-48, 2005.

[8] A. Birk and S. Carpin, "Merging Occupancy Grid Maps from Multiple Robots," Proc. of the IEEE, vol. 94, pp. 1384-97, 2006.

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行動式機器人空間認知與運動規劃

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