Biologically Inspired Climbing with a Hexapedal Robot

This paper presents an integrated, systems-level view of several novel design and control features associated with the biologically inspired, hexapedal, RiSE (Robots in Scansorial Environments) robot. RiSE is the first legged machine capable of locomotion on both the ground and a variety of vertical building surfaces including brick, stucco, and crushed stone at speeds up to 4 cm/s, quietly and without the use of suction, magnets, or adhesives. It achieves these capabilities through a combination of bioinspired and traditional design methods. This paper describes the design process and specifically addresses body morphology, hierarchical compliance in the legs and feet, and sensing and control systems that enable robust and reliable climbing on difficult surfaces. Experimental results illustrate the effects of various behaviors on climbing performance and demonstrate the robot\u27s ability to climb reliably for long distances

On the Comparative Analysis of Locomotory Systems with Vertical Travel

This paper revisits the concept of specific resistance, a dimensionless measure of locomotive efficiency often used to compare the transport cost of vehicles (Gabrielli & von Karman 1950), and extends its use to the vertical domain. As specific resistance is designed for comparing horizontal locomotion, we introduce a compensation term in order to offset the gravitational potential gained or lost during locomotion. We observe that this modification requires an additional, experimentally fitted model estimating the efficiency at which a system is able to transfer energy to and from gravitational potential. This paper introduces a family of such models, thus introducing methods to allow fair comparisons of locomotion on level ground, sloped, and vertical surfaces, for any vehicle which necessarily gains or loses potential energy during travel

Design, modelling and control of a novel agricultural robot with interlock drive system

A current problem in the design of small and lightweight autonomous agricultural robots is how to create sufficient traction on soil to pull an agricultural implement or load. One promising solution is offered by the interlock drive system, which penetrates spikes into the soil to create traction. The combination of soil penetrating spikes and a push-pull design offers new possibilities for vehicle control. By controlling the interlocking of the spikes and pushing and pulling them against the main frame, the vehicle can perform tight maneuvers. To validate this idea, we designed a robot, capable of creating high traction and performing headland turns. The navigation of the new robot system is performed by actively pushing the spikes, mounted on a slide into the soil, while the main frame is pushed back and pulled forward. The vehicle of 2-meter length was able to turn on the spot, and could follow a straight line, just using the spikes and the push-pull mechanism. The trajectory and the performed measurements suggest, that a vehicle which uses only spikes for traction and steering is fully capable of performing autonomous tasks in agriculture fields

Optimization Based Motion Planning for Multi-Limbed Vertical Climbing Robots

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.Comment: IROS 2019 Accepte

Gait Transitions for Quasi-Static Hexapedal Locomotion on Level Ground

As robot bodies become more capable, the motivation grows to better coordinate them—whether multiple limbs attached to a body or multiple bodies assigned to a task. This paper introduces a new formalism for coordination of periodic tasks, with specific application to gait transitions for legged platforms. Specifically, we make modest use of classical group theory to replace combinatorial search and optimization with a computationally simpler and conceptually more straightforward appeal to elementary algebra. We decompose the space of all periodic legged gaits into a cellular complex indexed using “Young Tableaux”, making transparent the proximity to steady state orbits and the neighborhood structure. We encounter the simple task of transitioning between these gaits while locomoting over level ground. Toward that end, we arrange a family of dynamical reference generators over the “Gait Complex” and construct automated coordination controllers to force the legged system to converge to a specified cell’s gait, while assessing the relative static stability of gaits by approximating their stability margin via transit through a “Stance Complex”. To integrate these two different constructs—the Gait Complex describing possible gaits, the Stance Complex defining safe locomotion—we utilize our compositional lexicon to plan switching policies for a hybrid control approach. Results include automated gait transitions for a variety of useful gaits, shown via tests on a hexapedal robot