Underactuated robots often require involved routines for trajectory planning due to their complex dynamics. Flapping-wing aerial vehicles have unsteady aerodynamics and periodic gaits that complicate the planning procedure. In this paper, we improve upon existing methods for flight planning by introducing a two-stage optimization routine to plan flapping flight trajectories. The first stage solves a trajectory optimization problem with a data-driven fixed-wing approximation model trained with experimental flight data. The solution to this is used as the initial guess for a second stage optimization using a flapping-wing model trained with the same flight data. We demonstrate the effectiveness of this approach with a bat robot in both simulation and experimental flight results. The speed of convergence, the dependency on the initial guess, and the quality of the solution are improved, and the robot is able to track the optimized trajectory of a dive maneuver.

Bat flight has been an underdeveloped area of bio-inspired robotics because of the vast complexities of biological bat flight and the over 40 degrees of freedom present in their bodies. The robotic flapping system Bat Bot (B2) has been shown to exhibit fundamental properties of biological bat flight with its articulated wings, its deformable membrane, and its controllable hindlimbs. However, the system is limited in performance by its relatively large mass for the thrust it produces. In an effort to further pursue this important area of flapping flight, we have made several important hardware improvements to the system based on biological inspiration. These include passive wrist joints to reduce negative lift in the upstroke and a novel elastic fiber membrane to mimic the anistropic nature of bat skin for performance and durability. The redesigned flapping mechanism and structure have reduced the weight by 22%, increased the flapping amplitude, lowered mechanical slackness, and improved mass distribution. These hardware improvements are functional together in free-flight tests. This new system Bat Bot 2.0 (B2.0) provides insights into the important elements of design of bat robots, and it brings the goal of complex bat flight maneuvers closer to reality.

Planning flight trajectories is important for practical application of flying systems. This topic has been well studied for fixed and rotary winged aerial vehicles, but far fewer works have explored it for flapping systems. Bat Bot (B2) is a bio-inspired flying robot that mimics bat flight, and it possesses the ability to follow a designed trajectory with its on-board electronics and sensing. However, B2's periodic flapping and its complex aerodynamics present major challenges in modeling and planning feasible flight paths. In this paper, we present a generalized approach that uses a model with direct collocation methods to plan dynamically feasible flight maneuvers. The model is made to be both accurate through collection of load cell force data for parameter selection and computationally inexpensive such that it can be used efficiently in a nonlinear solver. We compute the trajectory of launching B2 to a desired altitude and a banked turn maneuver, and we validate our methods with experimental flight results of tracking the launch trajectory with a PD controller.