Genetic programming based automatic gait generation for quadruped robots

This paper introduces a new approach to develop a fast gait for quadruped robot using genetic programming (GP). Several recent approaches have focused on the genetic algorithm (GA) to generate a gait automatically and shown significant improvements over previous results. Most of current GA based approaches use pre-selected parameters, but it is difficult to select the appropriate parameters for the optimization of gait. To overcome these problems of GA based approach, we proposed an efficient approach which optimizes joint angle trajectories using genetic programming. Our GP based method has obtained much better results than GA based approaches for experiments of Sony AIBO ers-7 in Webots environment. The elite archive mechanism(EAM) was introduced to prevent premature convergence problems in GP and has shown improvements

Swing Leg Motion Strategy for Heavy-load Legged Robot Based on Force Sensing

The heavy-load legged robot has strong load carrying capacity and can adapt to various unstructured terrains. But the large weight results in higher requirements for motion stability and environmental perception ability. In order to utilize force sensing information to improve its motion performance, in this paper, we propose a finite state machine model for the swing leg in the static gait by imitating the movement of the elephant. Based on the presence or absence of additional terrain information, different trajectory planning strategies are provided for the swing leg to enhance the success rate of stepping and save energy. The experimental results on a novel quadruped robot show that our method has strong robustness and can enable heavy-load legged robots to pass through various complex terrains autonomously and smoothly

A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability

For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the proble

Doctor of Philosophy

dissertationThis dissertation defines a new class of climbing robots, steering-plane bipeds, which encompasses a large number of existing climbing robots. Three major levels of motion planning are characterized which are common to this class of robots, namely, path planning, step planning, and gait planning. The unified presentation of related motion planning techniques is more generally applicable and more thorough than related algorithms in other literature, while more explicitly identifying limitations and tradeoffs due to alternate design choices within the class of steering-plane bipeds. A novel spline-based method for generating gaits is presented which uses separate path and time rate controls, and explicitly defined foot approach and departure directions that allows 1) a nominal guarantee of collision-free foot trajectories when close to the desired step configuration, 2) independent control of gait shape and speed, and 3) a unified representation of the four gait families of steering-plane bipeds: flipping, inchworm, step-through, and spinning gaits. This dissertation presents a thorough examination of the variations within each gait family, rather than merely presenting a representative instance of each. Concrete case studies applying the techniques of this dissertation are presented for optimizing the gaits for overall speed, energy efficiency, and minimum gripping force and moment. The results highlight that many common gaits in the literature are far from optimal. Results and general rules of thumb for gait planning are extracted that allow guidance for obtaining good results even if using alternate planning techniques without optimization

Optimization And Learning For Rough Terrain Legged Locomotion

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and \u27certificates\u27 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior

Design of Compliance Assisted Gaits for a Quadrupedal Amphibious Robot

The goal of this thesis was to develop an amphibious legged quadrupedal robot and associated gaits. Gaits of interest included walking, swimming, and smoothly transitioning between the two. Compliance was employed in the robot's legs to achieve swimming. Various types and configurations of compliant legs were evaluated using physical experiments and simulation. Three primary, two secondary, and two transition gaits were developed. An algorithm was developed to determine the appropriate course of action based on the current gait performance and the desired performance. The robot developed in this thesis met the goals of the design and demonstrated the technical feasibility of using compliance in amphibious legged robots