Lower bounds have been established for a variety of motion planning problems and also a wide variety of planning problems in general. To interpret these bounds a basic understanding of the theory of computation is required [462,891]. This fascinating subject will be unjustly summarized in a few paragraphs. A problem is a set of instances that are each carefully encoded as a binary string. An algorithm is formally considered as a Turing machine, which is a finite-state machine that can read and write bits to an unbounded piece of tape. Other models of computation also exist, such as integer RAM and real RAM (see ); there are debates as to which model is most appropriate, especially when performing geometric computations with real numbers. The standard Turing machine model will be assumed from here onward. Algorithms are usually formulated to make a binary output, which involves accepting or rejecting a problem instance that is initially written to the tape and given to the algorithm. In motion planning, this amounts to deciding whether a solution path exists for a given problem instance.