An Effective Motion Planning and Collision Avoidance Algorithm for Nonlinear Tensegrity Robots

Tensegrity robots offer advantages including lightweight, low power consumption, low shape-control cost, and robust environmental adaptability, demonstrating significant application potential in many fields such as space exploration, medical rehabilitation, and biomimicry. However, due to their unique mechanical properties and strong geometric nonlinearity characteristics, efficient motion planning and collision avoidance remain technical challenges. To address this challenge, this article initially presents an effective method for the nonlinear mechanical modeling and the calculation of balanced configurations during the motion process of tensegrity robots. This is achieved by transforming the cable state transition between tension and slack into a linear complementary problem. Then, an optimization method is developed to determine the driving force required to propel the robot to the target positions. Next, a motion planning algorithm within the actuation space is put forward to account for the possible collisions between the internal rods and external obstacles. Finally, the study is validated through both numerical simulations and experiments. The results indicate that the proposed algorithm facilitates an effective motion planning procedure for clustered tensegrity structures, enabling the acquisition of a continuous motion trajectory while guaranteeing collision avoidance.