Aleksandr Kushleyev

Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams

We present an algorithm for the generation of optimal trajectories for teams of heterogeneous quadrotors in three-dimensional environments with obstacles. We formulate the problem using mixed-integer quadratic programs (MIQPs) where the integer constraints are used to enforce collision avoidance. The method allows for different sizes, capabilities, and varying dynamic effects between different quadrotors. Experimental results illustrate the method applied to teams of up to four quadrotors ranging from 65 to 962 grams and 21 to 67 cm in width following trajectories in three-dimensional environments with obstacles with accelerations approaching 1g.

Influence of Aerodynamics and Proximity Effects in Quadrotor Flight

The dynamic response and performance of a micro UAV is greatly influenced by its aerodynamics which in turn is affected by the interactions with features in the environment in close proximity. In the paper we address the modeling of quadrotor robots in different flight conditions that include relative wind velocity and proximity to the ground, the ceiling and other robots. We discuss the incorporation of these models into controllers and the use of a swarm of robots to map features in the environment from variations in the aerodynamics.

Time-bounded lattice for efficient planning in dynamic environments

For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the state-space, with time as an additional variable. Moreover, re-planning needs to be invoked more often since the trajectories of moving obstacles need to be constantly re-estimated. In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide real-time guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning - a time-bounded lattice - that merges together short-term planning in time with longterm planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.

Path planning for non-circular micro aerial vehicles in constrained environments

Operating micro aerial vehicles (MAVs) outside of the bounds of a rigidly controlled lab environment, specifically one that is unstructured and contains unknown obstacles, poses a number of challenges. One of these challenges is that of quickly determining an optimal (or nearly so) path from the MAVs current position to a designated goal state. Past work in this area using full-size unmanned aerial vehicles (UAVs) has predominantly been performed in benign environments. However, due to their small size, MAVs are capable of operating in indoor environments which are more cluttered. This requires planners to account for the vehicle heading in addition to its spatial position in order to successfully navigate. In addition, due to the short flight times of MAVs along with the inherent hazards of operating in close proximity to obstacles, we desire the trajectories to be as cost-optimal as possible. Our approach uses an anytime planner based on A* that performs a graph search on a four-dimensional (4-D) (x,y,z, heading) lattice. This allows for the generation of close-to-optimal trajectories based on a set of precomputed motion primitives along with the capability to provide trajectories in real-time allowing for on-the-fly re-planning as new sensor data is received. We also account for arbitrary vehicle shapes, permitting the use of a non-circular footprint during the planning process. By not using the overly conservative circumscribed circle for collision checking, we are capable of successfully finding optimal paths through cluttered environments including those with narrow hallways. Analytically, we show that our planner provides bounds on the sub-optimality of the solution it finds. Experimentally, we show that the planner can operate in real-time in both a simulated and real-world cluttered environments.

Search-based planning for a legged robot over rough terrain

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.

Experimental characterization of radio signal propagation in indoor environments with application to estimation and control

We study radio signal propagation in indoor environments using low-power devices leveraging the Zigbee and Bluetooth specifications. We present results from experiments where two robots equipped with radio signal devices and enabled to control and localize autonomously in an indoor hallway and laboratory environment densely sample RSSI at various times over several days. We show that simulated RSSI measurements using existing radio signal models and experimentally gathered RSSI measurements match closely, suggesting that for robotics applications requiring predicted RSSI, low-power radio signal devices are a well-posed sensing modality.

The University of Pennsylvania MAGIC 2010 multi-robot unmanned vehicle system

In this report, we describe the technical approach and algorithms that have been used by the University of Pennsylvania in the MAGIC 2010 competition. We have constructed and deployed a multi-vehicle robot team, consisting of intelligent sensor and disrupter unmanned ground vehicles that can survey, map, recognize, and respond to threats in a dynamic urban environment with minimal human guidance. The custom hardware systems consist of robust and complementary sensors, integrated electronics, computation, and highly capable propulsion and actuation. The mapping, navigation, and planning software is organized hierarchically, allowing autonomous decisions to be made by the robots while enabling human operators to interact with the robot team in an efficient and strategic manner. The ground control station integrates information coming from the robots as well as metadata feeds to focus the attention of the operators and respond rapidly to emerging threats. These systems were developed and tested by the UPenn team to complete two phases of the MAGIC 2010 challenge in a safe and timely manner.

Planning for landing site selection in the aerial supply delivery

In the aerial supply delivery problem, an unmanned aircraft needs to deliver supplies as close as possible to the desired location. This involves choosing, flying to, sensing, and landing at a safe landing site that is most accessible from the goal. The problem is often complicated by the fact that the availability of these landing sites may be unknown before the mission begins. Therefore, the aircraft needs to compute a sequence of actions that will minimize the expected value of the objective function. The problem of computing this sequence corresponds to planning under uncertainty in the environment. In this paper, we show how it can be solved efficiently via a recently developed probabilistic planning framework, called Probabilistic Planning with Clear Preferences (PPCP). We show that the problem satisfies the Clear Preferences assumption required by PPCP, and therefore all the theoretical guarantees continue to hold. The experimental results in simulation demonstrate that our approach can solve large-scale problems in realtime while experiments on our custom quad-rotor helicopter provide a proof of concept for the planner.