Research Highlights

Soft robotics technology creates new ways for legged robots to interact with and adapt to their environment. We have developed (i) a new 2-degree-of-freedom soft pneumatic actuator, and (ii) a novel soft robotic hexapedal robot called SoRX that leverages the new actuators.

Simulation and physical testing confirm that the proposed actuator can generate cyclic foot trajectories that are appropriate for legged locomotion. Consistent with other hexapedal robots (and animals), SoRX employs an alternating tripod gait to propel itself forward. Experiments reveal that SoRX can reach forward speeds of up to 0.44 body lengths per second, or equivalently 101 mm/s. With a size of 230 mm length, 140 mm width and 100 mm height, and weight of 650 grams, SoRX is among the fastest tethered soft pneumatically-actuated legged robots to date.

The motion capabilities of SoRX are evaluated through five experiments: running, step climbing, and traversing rough terrain, steep terrain, and unstable terrain. Experimental results show that SoRX is able to operate over challenging terrains in open-loop control and by following the same alternating tripod gait across all experimental cases.

Current work in progress focuses on the design of a new quadrupedal robot. The robot will be an accessible (low-cost, open-source design, COTS parts) medium-scale (<7kg weight) legged robot capable of autonomous navigation in unstructured terrains. A single-leg development platform (featured in this video) is used to evaluate multiple single-leg gait patterns as well as controllers such as for single-leg jumping.

Collision detection and recovery for aerial robots remain a challenge because of the limited space for sensors and local stability of the flight controller. We introduced a novel collision-resilient quadrotor that features a compliant arm design to enable free flight while allowing for one passive degree of freedom to absorb shocks.

We further proposed a novel collision detection and characterization method based on Hall sensors, as well as a new recovery control method to generate and track a smooth trajectory after a collision occurs.

Experimental results demonstrate that the robot can detect and recover from high-speed collisions with various obstacles such as walls and poles. Moreover, it can survive collisions that are hard to detect with existing methods based on IMU data and contact models, for example, when colliding with unstructured surfaces, or being hit by a moving obstacle while hovering.

We introduced a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts:

1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs.

2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment.

We have derived bounds for the identified approximation's sensitivity to noisy data and showed that adding the higher-level controller maintains the original system's stability. Our approach requires only a small amount of observations on states and inputs, and it thus works online; that feature makes our approach appealing to robotics applications where real-time operation is critical. The efficacy of the DHC structure was demonstrated in simulation and was also validated experimentally using aerial robots with approximately-known mass and moment of inertia parameters and that operate under the influence of ground effect.

We investigated how the behavior of small-scale Unmanned Aerial Vehicles (UAVs) is influenced by the system’s close proximity to the ground/rigid surfaces both at hover and in forward flight. We performed an extensive experimental study where a quadrotor is tasked with flying in forward velocities in the range of 0-8 m/s, and at altitudes that range between 0.05-0.5 m. Experimental data were used to evaluate four existing ground effect models. Results suggest that existing models for helicopters and in-hover multi-rotors cannot fully describe the forward motion of a quadrotor when it operates close to ground.

We introduced two new data-driven models for rotorcraft operating in ground effect both at hover and in forward flight, and evaluated the proposed models with another quadrotor of different size. The proposed models simultaneously consider several operating conditions, which are parameterized by the vehicle’s forward velocity and altitude. The models link the thrust produced when operating in ground effect (IGE) and hovering out of ground effect (OGE) as forward velocities vary. This information can be incorporated into flight controllers for robust and adaptive UAV flight, and can benefit motion planners for safe and energy efficient near-ground trajectory planning.

This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBAP utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time.

The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single- and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.

Online coverage planning can be useful in applications like field monitoring and search and rescue. Without prior information of the environment, achieving resolution-complete coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) remains a challenge. We have proposed a hierarchical, hex-decomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Gazebo simulations and hardware experiments with a non-holonomic wheeled robot show that our approach can successfully trade-off between coverage and exploration speed and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned.

Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This work contributes to the emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. We have introduced a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model. This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal.

To make the algorithm online, we have also developed a state expansion pruning technique that significantly reduces the search space while ensuring completeness; the pruning technique itself can be adapted to collision-avoidance-based planners as well.

The proposed algorithm has been evaluated experimentally with a built-in-house holonomic wheeled robot that can withstand collisions. We have performed an extensive parametric study to investigate trade-offs between (user-tuned) levels of risk, deliberate collision decision making, and trajectory statistics such as time to reach the goal and path length.

Collisions with the environment may, at cases, be beneficial for robot motion planning and control. We have introduced a state-feedback closed-loop control approach with integrated collision exploitation. We developed a stochastic switching framework to model the transition between states of free motion and in collision with the environment, and formulated an optimal steering problem to compute the control input related to state feedback.

Collisions are found beneficial in terms of increasing task success probability when steering a robot from an initial to a target spatial distribution. In certain cases, collisions may help reduce the control energy for the task when compared to optimal steering with collision avoidance. We provide a mathematical basis to explain this finding, perform several parametric analyses in simulation to validate the theoretical analysis, and conduct realistic physics simulations to quantify the impact of realistic constraints (bounded control input, physical impact of collision, and friction) on control energy and success probability. Further, we validate the proposed approach experimentally with an omni-directional collision resilient wheeled robot built in-house.

We developed a model-based control strategy for miniature legged robots tasked with navigation in cluttered environments. The approach used a new model for crawling locomotion to derive closed-form expressions of state propagation which in turns supported the development of a feedback control navigation strategy. The strategy consisted of a waypoint tracking controller that steers the system along desired paths and an outer control loop that updates the reference path to account for uncertainty. The proposed strategy enabled noise-resilient navigation for miniature legged robots for the first time. The strategy was tested extensivel in simulation and was experimentally validated on an eight-legged robot that navigates in obstacle-cluttered environments.