With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO), to address the perception and planning challenges, respectively. The HALSS framework processes point cloud information to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target contingency planner that adaptively replans as new perception information is received. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success whilst being more fuel efficient compared to a non-adaptive DDTO approach.

As the scope and complexity of modern cyber-physical systems increase, newer and more challenging mission requirements will be imposed on the optimal control of the underlying unmanned systems. This paper proposes a solution to handle complex temporal requirements formalized in Signal Temporal Logic (STL) specifications within the Successive Convexification (SCvx) algorithmic framework. This SCvx-STL solution method consists of four steps: 1) Express the STL specifications using their robust semantics as state constraints. 2) Introduce new auxiliary state variables to transform these state constraints as system dynamics, by exploiting the recursively defined structure of robust STL semantics. 3) Smooth the resulting system dynamics with polynomial smooth min- and max- functions. 4) Convexify and solve the resulting optimal control problem with the SCvx algorithm, which enjoys guaranteed convergence and polynomial time subproblem solving capability. Our approach retains the expressiveness of encoding mission requirements with STL semantics, while avoiding the usage of combinatorial optimization techniques such as Mixed-integer programming. Numerical results are shown to demonstrate its effectiveness.

The contribution of this paper is the application of compound state-triggered constraints (STCs) to real-time quadrotor path planning. Originally developed for rocket landing applications, STCs are made up of a trigger condition and a constraint condition that are arranged such that satisfaction of the former implies satisfaction of the latter. Compound STCs go a step further by allowing multiple trigger and constraint conditions to be combined via Boolean “and” or “or” operations. The logical implications embodied by STCs can be formulated using continuous variables, and thus enable the incorporation of discrete decision making into a continuous optimization framework. In this paper, compound STCs are used to solve quad-rotor path planning problems that would typically require the use of computationally expensive mixed-integer programming techniques. Two scenarios are considered: (1) a quad-rotor flying through a hoop, and (2) a pair of quadrotors carrying a beam-like payload through an obstacle course. Successive convexification is used to solve the resulting non-convex optimization problem. Monte-Carlo simulation results show that our approach can reliably generate trajectories at rates upwards of 3 and 1.5 Hz for the first and second scenarios, respectively.

In this paper, we employ convex optimization to perform real-time 3-dimensional path planning on-board a quad-rotor and demonstrate its real-time capabilities. Building on our previous work, we make the following modifications: (1)we assume the obstacles are mobile, and (2)we introduce a simple framework to continuously recompute and update the trajectory. The contribution of this paper is to demonstrate the feasibility of real-time on-board convex-optimization-based path planning. For multi-rotors with fixed-pitch propellers, this path planning problem has two sources of non-convexity. First, since fixed-pitch actuators produce uni-directional thrust, the commanded total thrust must be maintained above a non-zero minimum in order to retain sufficient independent attitude control authority. The second source of non-convexity is due to the keep-out zones that envelop each obstacle. To circumvent the non-convexities introduced by these control and state constraints, we employ lossless and successive con-vexification, respectively. Consequently, we cast the original problem as a sequence of Second-Order Cone Programming problems, which can be solved quickly and reliably on-board. We conclude by presenting indoor flight demonstration and timing results of a scenario with three mobile obstacles. In this scenario, our algorithm assumes that the obstacles move with constant acceleration, and is re-executed regularly to account for uncertainties in the motion of the obstacles. The results show that new trajectories can be computed at rates in excess of 10 Hz, quickly enough to adapt to the uncertainty introduced in our flight demonstration.

In this paper, we apply lossless and successive convexification techniques and employ real-time on-board convex optimization to perform constrained motion planning for quadrotors. In general, this problem is challenging for real-time on-board applications due to its non-convex nature. The ability to generate feasible trajectories quickly and reliably is central to operating high-performance aerial robots in populated spaces. Motivated by our earlier research on convexification of non-convex optimal control problems, we use these convexification techniques to cast the problems into one (or a sequence of) Second-Order Cone Programming (SOCP) problem(s). In doing so, we are able to attain our solutions by leveraging modern advances in Interior Point Method (IPM) algorithms. Here, we focus on 3-degree-of-freedom trajectory generation, whereby closed-loop control is used to track a translational trajectory computed on-board at the onset of the maneuver. To the best of our knowledge, this is the first demonstration of convexification techniques used in real-time on-board trajectory generation for high-performance quad-rotor flight. We present two example scenarios: (1) a case where lossless convexification is used to increase the available control envelope, thus enabling an agile flip maneuver, and (2) a case where both convexification techniques are used to compute a path through a flight space containing ellipsoidal keep-out zones. We present flight demonstration results obtained using the Autonomous Control Laboratory's (ACL's) custom quad-rotor platforms and SOCP optimization software. Additionally, computation timing statistics for the example scenarios obtained using a series of mobile ARM and Intel processors show a minimum mean computation time of 36.5 and 122.2 milliseconds, respectively.