Path planning for mobile manipulator robots under non-holonomic and task constraints

This paper presents a path planner, which enables a nonholonomic mobile manipulator to move its end-effector on an observed surface with a constrained orientation, given start and destination points. A partial point cloud of the environment is captured using a vision-based sensor, but no prior knowledge of the surface shape is assumed. We consider the multi-objective optimisation problem of finding robot paths which account for the nonholonomic constraints of the base, maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g. demolition of legacy nuclear plants, where dismantling does not require a precise path. We show how our approach embeds the nonholonomic constraints of the mobile platform into an extended Jacobian, while additionally encoding the constraint that the end-effector must remain in contact with the cut surface throughout the motion. We use this constrained Jacobian to plan a time-series of robot configurations. Additionally, we show how our novel cost function is suitable for combining with a variety of well-known path planners, such as RRT*. We present several empirical experiments in different scenarios, where a simulated non-holonomic mobile manipulator follows a trajectory, which is generated on noisy point clouds derived from real depth-camera images of real objects. Our planner (RRT*-CRMM) enables successful task completion by optimising the path over the travelled distance, the manipulability of the arm, and the movements of the mobile base.