manipulation: add TransitionPlanner::planPathSingle to work around eigenpy (1,N) Ref<MatrixXd> corruption#124
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…genpy (1,N) Ref<MatrixXd> corruption When a (1,N) numpy array is passed as matrixIn_t (Eigen::Ref<const MatrixXd>), numpy marks it as both C- and F-contiguous (layout is irrelevant for a single row). eigenpy's is_arr_layout_compatible_with_mat_type<MatrixXd> checks only the F-contiguous flag, sees true, and creates a direct Map with NumpyMapStride = Stride<0,0>. The compile-time-zero strides override the actual numpy strides, so only element (0,0) maps correctly; all (0,c) for c > 0 read garbage memory. planPath(qInit, qGoals, reset) is preserved unchanged and works correctly for multi-row goals matrices (rows > 1), where C- and F-contiguous flags differ and eigenpy correctly allocates a copy. planPathSingle(qInit, qGoal, reset) is added as a safe single-goal alternative: it accepts two 1D ConfigurationIn_t (Ref<const VectorXd>, handled correctly by eigenpy's IsVectorAtCompileTime specialisation) and builds a C++-owned MatrixXd(1,n) internally before calling planPath.
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When a (1,N) numpy array is passed as matrixIn_t (Eigen::Ref), numpy marks it as both C- and F-contiguous (layout is irrelevant for a single row). eigenpy's is_arr_layout_compatible_with_mat_type checks only the F-contiguous flag, sees true, and creates a direct Map with NumpyMapStride = Stride<0,0>. The compile-time-zero strides override the actual numpy strides, so only element (0,0) maps correctly; all (0,c) for c > 0 read garbage memory.
planPath(qInit, qGoals, reset) is preserved unchanged and works correctly for multi-row goals matrices (rows > 1), where C- and F-contiguous flags differ and eigenpy correctly allocates a copy.
planPathSingle(qInit, qGoal, reset) is added as a safe single-goal alternative: it accepts two 1D ConfigurationIn_t (Ref, handled correctly by eigenpy's IsVectorAtCompileTime specialisation) and builds a C++-owned MatrixXd(1,n) internally before calling planPath.