  Manipulators can be added to legged robots, allowing them to interact with
and change their environment. Legged mobile manipulation planners must consider
how contact forces generated by these manipulators affect the system. Current
planning strategies either treat these forces as immutable during planning or
are unable to optimize over these contact forces while operating in real-time.
This paper presents the Stability and Task Oriented Receding-Horizon Motion and
Manipulation Autonomous Planner (STORMMAP) that is able to generate continuous
plans for the robot's motion and manipulation force trajectories that ensure
dynamic feasibility and stability of the platform, and incentivizes
accomplishing manipulation and motion tasks specified by a user. STORMMAP uses
a nonlinear optimization problem to compute these plans and is able to run in
real-time by assuming contact locations are given a-priori, either by a user or
an external algorithm. A variety of simulated experiments on a quadruped with a
manipulator mounted to its torso demonstrate the versatility of STORMMAP. In
contrast to existing state of the art methods, the approach described in this
paper generates continuous plans in under ten milliseconds, an order of
magnitude faster than previous strategies.
