Extensive, Long-Term Task and Motion Planning with Signal Temporal Logic Specification for Autonomous Construction

We propose a hierarchical task and motion plan- ning (TAMP) for autonomous construction that manipulates deformable objects, such as terrain excavation. The TAMP is required to generate an efficient task plan to meet high- level construction goals at sites with environmental diversity while ensuring motion feasibility. The difficulty, however, is to manipulate deformable objects containing nonlinear dynamics with a target given by a continuous value as the task speci- fication. Optimization-based TAMP with signal temporal logic specifications in robotics is promising because of its continuous task specification and formulation as a nonlinear programming problem. The key to its application to extensive, long-term planning at real construction sites is a computationally efficient and stable formulation. We introduce a new expression for deformable objects with a simple differentiable function and a system model that can represent mode transitions based on machine action on the objects. This allows TAMP to be formulated as simultaneously selecting an action for objects and planning the motion to execute it. Furthermore, a hierarchical method that gives appropriate initial values is combined to improve optimality for large-scale nonlinear problems. From the verification by numerical experiments, the proposed method can generate a plan that minimizes the time to meet the task goal, even when the area is expanded.