For effective operation in challenging outdoor environments, mobile unmanned robots face stiff and competing demands including payload capacity, driving speed, range, as well as the ability to traverse rough terrain. To address these issues we introduce the hybrid wheel-leg quadrupedal robot WaLTER. WaLTER utilizes a unique combination of continuously rotating distal leg joints, actuated wheels, and a roll body DOF to efficiently drive on flat ground and effectively tumble over stairs and difficult, broken terrain. We developed an intuitive teleoperation scheme and employed deep reinforcement learning as proof of concept control techniques for the novel morphology. To test its capabilities, we constructed a multi-body simulation in MuJoCo and a 2.1 kg physical prototype for experimentation on traversability and energy economy. Our testing demonstrated the ability to traverse rougher terrain relative to larger-wheeled counterparts and reliable stair-climbing while maintaining a 4 km range on a 24.4 Wh battery (COT: 1.21).

Legged animals have developed a variety of modes of locomotion to adapt to the diverse and unknown terrain challenges posed in the natural world. Legged robots, however, have been largely limited to specializing in one domain, with few that have endeavored to bridge the gap between two. In this work we present the Scansorial, Terrestrial, and Aquatic Robot Quadruped (STARQ), a novel legged robot capable of bridging three different domains with three modes of locomotion: walking, climbing, and swimming. In this study we describe model-based design techniques as well as design innovations that have made multimodal locomotion possible including waterproof hips for 2-DOF high torque legs, legs capable of effective power transmission in three modes, and bi-directionally compliant feet for walking and attaching to vertical surfaces. To demonstrate the robot's capabilities we present locomotion test data including speed and cost of transport in each of these domains. We also demonstrate the capability to transition from walking to swimming in a natural environment.

Heterogeneous robotic systems in the field often encounter bodies of water with unknown traversability properties. One approach to measuring depth, current, soil composition, etc. is via an in situ underwater sensor being dragged by cable attached to a maneuvering airborne multicopter - which entails a novel motion planning and control problem with mixed resistive media. In this work we propose a framework to plan trajectories for future characterization sensors and payloads moving through mixed (air-water) media while considering uncertainty in the depth of the underwater ground surface. The methodology is applied to example underactuated systems with suspended payloads of increasing levels of complexity, including a cable robot and 4- and 8-DOF multicopter systems. Simulation studies employing trajectory optimization indicate that under certain payload configurations and task constraints, there are maneuvers in which it is more efficient to drag the payloads through water than through air. The paper also includes preliminary experiments with a testbed cable robot platform.

We present a real-time motion planner that avoids multiple moving obstacles without knowing their dynamics or intentions. This method uses convex optimization to generate trajectories for linear plant models over a planning horizon (i.e. model-predictive control). While convex optimizations allow for fast planning, obstacle avoidance can be challenging to incorporate because Euclidean distance calculations tend to break convexity. By using a half-space convex relaxation, our planner reasons about an approximated distance-to-obstacle measure that is linear in its decision variables and preserves convexity. Further, by iteratively updating the relaxation over the planning horizon, the half-space approximation is improved, enabling nimble avoidance maneuvers. We further augment avoidance performance with a soft penalty slack-variable for-mulation that introduces a piecewise quadratic cost. As a proof of concept, we demonstrate the planner on double-integrator models in both single-agent and multi-agent tasks-avoiding multiple obstacles and other agents in 2D and 3D environments. We show extensions to legged locomotion by bipedally walking around obstacles in simulation using the Linear Inverted Pendulum Model (LIPM). We then present two sets of hardware experiments showing real-time obstacle avoid-ance with quadcopter drones: (1) avoiding a 10m/s swinging pendulum and (2) dodging a chasing drone.