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This work presents an online trajectory generation algorithm using a sinusoidal jerk profile. The generator takes initial acceleration, velocity and position as input, and plans a multi-segment trajectory to a goal position under jerk, acceleration, and velocity limits. By analyzing the critical constraints and conditions, the corresponding closed-form solution for the time factors and trajectory profiles are derived. The proposed algorithm was first derived in Mathematica and then converted into a C++ implementation. Finally, the algorithm was utilized and demonstrated in ROS & Gazebo using a UR3 robot. Both the Mathematica and C++ implementations can be accessed at https://github.com/Haoran-Zhao/Jerk-continuous-online-trajectory-generator-with-constraints.git

For biomedical applications in targeted therapy delivery and interventions, a large swarm of micro-scale particles (“agents”) has to be moved through a maze-like environment (“vascular system”) to a target region (“tumor”). Due to limited on-board capabilities, these agents cannot move autonomously; instead, they are controlled by an external global force that acts uniformly on all particles. In this work, we demonstrate how to use a time-varying magnetic field to gather particles to a desired location. We use reinforcement learning to train networks to efficiently gather particles. Methods to overcome the simulation-to-reality gap are explained, and the trained networks are deployed on a set of mazes and goal locations. The hardware experiments demonstrate fast convergence, and robustness to both sensor and actuation noise. To encourage extensions and to serve as a benchmark for the reinforcement learning community, the code is available at Github.

This paper investigates using a sampling-based approach, the RRT*, to reconfigure a 2D set of connected tiles in complex environments, where multiple obstacles might be present. Since the target application is automated building of discrete, cellular structures using mobile robots, there are constraints that determine what tiles can be picked up and where they can be dropped off during reconfiguration. We compare our approach to two algorithms as global and local planners, and show that we are able to find more efficient build sequences using a reasonable amount of samples, in environments with varying degrees of obstacle space.

This paper investigates the scheduling problem related to engaging a swarm of attacking drones with a single defensive turret. The defending turret must turn, with a limited slew rate, and remain facing a drone for a dwell time to eliminate it. The turret must eliminate all the drones in the swarm before any drone reaches the turret. In 2D, this is an example of a Traveling Salesman Problem with Time Windows (TSPTW) where the turret must visit each target during the window. In 2D, the targets and turret are restricted to a plane and the turret rotates with one degree of freedom. In 3D, the turret can pan and tilt, while the drones attempt to reach a safe zone anywhere along the vertical axis above the turret. This 3D movement makes the problem more challenging, since the azimuth angles of the turret to the drones vary as a function of time. This paper investigates the theoretical optimal solution for simple swarm configurations. It compares heuristic approaches for the path scheduling problem in 2D and 3D using a simulation of the swarm behavior. It provides results for an improved heuristic approach, the Threat-Aware Nearest Neighbor.

This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9 mm to 4.6 mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods.

This paper investigates the pursuit-evasion problem of a defensive gun turret and one or more attacking drones. The turret must "visit" each attacking drone once, as quickly as possible, to defeat the threat. This constitutes a Shortest Hamiltonian Path (SHP) through the drones. The investigation considers situations with increasing fidelity, starting with a 2D kinematic model and progressing to a 3D dynamic model. In 2D we determine the region from which one or more drones can always reach a turret, or the region close enough to it where they can evade the turret. This provides optimal starting angles for n drones around a turret and the maximum starting radius for one and two drones.We show that safety regions also exist in 3D and provide a controller so that a drone in this region can evade the pan-tilt turret. Through simulations we explore the maximum range n drones can start and still have at least one reach the turret, and analyze the effect of turret behavior and the drones’ number, starting configuration, and behaviors.

Soil strength testing and collecting soil cores from wetlands is currently a slow, manual process that runs the risk of disturbing and contaminating soil samples. This paper describes a method using an instrumented dart deployed and retrieved by a drone for performing core sample tests in soft soils. The instrumented dart can simultaneously conduct free- fall penetrometer tests. A drone-mounted mechanism enables deploying and reeling in the dart for sample return or for multiple soil strength tests. Tests examine the effect of dart tip diameter and drop height on soil retrieval, and the requisite pull force to retrieve the samples. Further tests examine the dart’s ability to measure soil strength and penetration depth. Hardware trials demonstrate that the drone can repeatedly drop and retrieve a dart, and that the soil can be discretely sampled.

Quadcopters are increasingly popular for robotics applications. Being able to efficiently calculate the set of positions reachable by a quadcopter within a time budget enables collision avoidance and pursuit-evasion strategies.This paper examines the set of positions reachable by a quadcopter within a specified time limit using a simplified 2D model for quadcopter dynamics. This popular model is used to determine the set of candidate optimal control sequences to build the full 3D reachable set. We calculate the analytic equations that exactly bound the set of positions reachable in a given time horizon for all initial conditions. To further increase calculation speed, we use these equations to derive tight upper and lower spherical bounds on the reachable set.

Beamforming by scattering from an array of scatterers carried by a drone is explored. By positioning the vertical heights of the scatterers on the drone, beam focusing can be achieved in a desired direction. Various horizontal layouts of the scatterers on the drone can be used, with a “double-cross” layout used here for the case of 9 scatterers. The formation of a null in the pattern in a desired direction is also possible using optimization of the scatterer positions.