This paper proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories. The bootstrapped predictions are incorporated in the MPC optimization using a risk-aware methodology so as to provide probabilistic guarantees on obstacle avoidance. We validate our methods using simulations of a multi-rotor drone that avoids various moving obstacles.

Mid-Air Deployment (MAD) of a rotorcraft during Entry, Descent and Landing (EDL) onMars eliminates the need to carry a propulsion or airbag landing system. This reduces the total mass inside the aeroshell by more than 100 kg and simplifies the aeroshell architecture. MAD's lighter and simpler design is likely to bring the risk and cost associated with the mission down. Moreover, the lighter entry mass enables landing in the Martian highlands, at elevations inaccessible to current EDL technologies. This paper proposes a novel MAD concept for a Mars helicopter. We suggest a minimum science payload package to perform relevant science in the highlands. A variant of the Ingenuity helicopter is proposed to provide increased deceleration during MAD, and enough lift to fly the science payload in the highlands. We show in simulation that the lighter aeroshell results in a lower terminal velocity (30 m/s) at the end of the parachute phase of the EDL, and at higher altitudes than other approaches. After discussing the aerodynamics, controls, guidance, and mechanical challenges associated with deploying at such speed, we propose a backshell architecture that addresses them to release the helicopter in the safest conditions. Finally, we implemented the helicopter model and aerodynamic descent perturbations in the JPL Dynamics and Real-Time Simulation (DARTS) framework. Preliminary performance evaluation indicates landing and helicopter operations can be achieved up to +5 km MOLA (Mars Orbiter Laser Altimeter reference).

For future helicopter-only Mars missions, NASA-JPL has proposed a novel entry, descent, and landing technique, in which the rotorcraft is deployed from the aeroshell in mid-air before landing. However, this approach is likely to subject the rotorcraft to unfavorable vortex ring state aerodynamics during deployment. To address this, the performance of a variable-pitch multirotor in axial descent was investigated using two parallel approaches: an experimental free-flight wind tunnel campaign and analogous computational efforts utilizing the tool RotCFD. Results indicated significant mean thrust losses of up to 20% compared to hover conditions, as well as heavily amplified rotor thrust fluctuations and vehicle attitude oscillations with increasing descent rate. Meanwhile, discrepancies between computations and experiments were observed, primarily regarding the descent rates where maximum thrust losses occur. Additional studies performed within the computational environment indicated that the vehicle fuselage and rotor-rotor interactions have significant impacts on the rotor performance in descent.

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This paper proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories. The bootstrapped predictions are incorporated in the MPC optimization using a riskaware methodology so as to provide probabilistic guarantees on obstacle avoidance. We validate our methods using simulations of a 3-dimensional multi-rotor drone that avoids various moving obstacles, such as a thrown ball and a frisbee with air drag.