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Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented in this paper. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set.
Since the beginning, the societal mandate for social work has been to help oppressed populations and to change conditions that interfere with their well being. This assignment suggests that advocacy for change is an essential component of practice. Specht and Courtney describe the profession’s mission as “…to help poor people, to improve community life, and to solve difficult social problems” (Specht & Courtney, 1994).
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