Dynamically Feasible Trajectory Generation for Soft Robots

Potential applications for large-scale soft robots include interacting with humans while carrying a heavy load, navigating in clutter, executing impact tasks like hammering a nail into a wall, and so much more. Because of their compliance and lack of fragile gear trains, soft robots are uniquely suited to these tasks. However, we expect that path planning may be more constrained by soft robot kinematics and dynamics than traditional rigid robots. Generating dynamically feasible trajectories for soft robots (especially large-scale soft robots with higher payloads) is critical to the success of low-level controllers tracking reference trajectories. This paper introduces an optimization method to generate task and joint space trajectories for soft robots that satisfy kinematic and dynamic constraints which are unique to large-scale soft robots. The method presented in this paper is an offline trajectory generator that is then fed to a low-level PID joint angle controller. We conduct two experiments to validate this method on a continuum pneumatic soft robot of length 1.19 meters in both simulation and on hardware. We show that this is a viable method of planning trajectories for soft robots with a reported median magnitude of error of 0.032 meters between the planned and actual end effector trajectories.