About: Legged robot is a research topic. Over the lifetime, 1970 publications have been published within this topic receiving 21381 citations. The topic is also known as: walking robot.
Papers published on a yearly basis
TL;DR: An intelligent trial-and-error algorithm is introduced that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans, and may shed light on the principles that animals use to adaptation to injury.
Abstract: An intelligent trial-and-error learning algorithm is presented that allows robots to adapt in minutes to compensate for a wide variety of types of damage. Autonomous mobile robots would be extremely useful in remote or hostile environments such as space, deep oceans or disaster areas. An outstanding challenge is to make such robots able to recover after damage. Jean-Baptiste Mouret and colleagues have developed a machine learning algorithm that enables damaged robots to quickly regain their ability to perform tasks. When they sustain damage — such as broken or even missing legs — the robots adopt an intelligent trial-and-error approach, trying out possible behaviours that they calculate to be potentially high-performing. After a handful of such experiments they discover, in less than two minutes, a compensatory behaviour that works in spite of the damage. Robots have transformed many industries, most notably manufacturing1, and have the power to deliver tremendous benefits to society, such as in search and rescue2, disaster response3, health care4 and transportation5. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets6 to deep oceans7. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility6,8. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots6,8. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage10,11, but current techniques are slow even with small, constrained search spaces12. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.
TL;DR: The authors developed a force model for arbitrarily-shaped legs and bodies moving freely in granular media, and used this "terradynamics" to predict a small legged robot's locomotion using various leg shapes and stride frequencies.
Abstract: The theories of aero- and hydrodynamics predict animal movement and device design in air and water through the computation of lift, drag, and thrust forces. Although models of terrestrial legged locomotion have focused on interactions with solid ground, many animals move on substrates that flow in response to intrusion. However, locomotor-ground interaction models on such flowable ground are often unavailable. We developed a force model for arbitrarily-shaped legs and bodies moving freely in granular media, and used this “terradynamics” to predict a small legged robot’s locomotion on granular media using various leg shapes and stride frequencies. Our study reveals a complex but generic dependence of stresses in granular media on intruder depth, orientation, and movement direction and gives insight into the effects of leg morphology and kinematics on movement.
18 Dec 2019
TL;DR: This work used low-voltage stacked DEAs with an operating voltage below 450 volts and used them to propel an insect-sized soft untethered and autonomous legged robot to develop a subgram robot capable of autonomous navigation, independently following printed paths.
Abstract: Insects are a constant source of inspiration for roboticists. Their compliant bodies allow them to squeeze through small openings and be highly resilient to impacts. However, making subgram autonomous soft robots untethered and capable of responding intelligently to the environment is a long-standing challenge. One obstacle is the low power density of soft actuators, leading to small robots unable to carry their sense and control electronics and a power supply. Dielectric elastomer actuators (DEAs), a class of electrostatic electroactive polymers, allow for kilohertz operation with high power density but require typically several kilovolts to reach full strain. The mass of kilovolt supplies has limited DEA robot speed and performance. In this work, we report low-voltage stacked DEAs (LVSDEAs) with an operating voltage below 450 volts and used them to propel an insect-sized (40 millimeters long) soft untethered and autonomous legged robot. The DEAnsect body, with three LVSDEAs to drive its three legs, weighs 190 milligrams and can carry a 950-milligram payload (five times its body weight). The unloaded DEAnsect moves at 30 millimeters/second and is very robust by virtue of its compliance. The sub–500-volt operation voltage enabled us to develop 780-milligram drive electronics, including optical sensors, a microcontroller, and a battery, for two channels to output 450 volts with frequencies up to 1 kilohertz. By integrating this flexible printed circuit board with the DEAnsect, we developed a subgram robot capable of autonomous navigation, independently following printed paths. This work paves the way for new generations of resilient soft and fast untethered robots.
01 Aug 1997
TL;DR: The ARL Monopod is built, which is the fastest electrically actuated legged robot to date and adapted Raibert's control laws for the low power electric actuation necessary for autonomous locomotion.
Abstract: To study the design, control and energetics of autonomous dynamically stable legged machines we have built a planar one-legged robot, the ARL Monopod. Its top running speed of 4.3 km/h (1.2 m/s) makes it the fastest electrically actuated legged robot to date. We adapted Raibert's control laws for the low power electric actuation necessary for autonomous locomotion and performed a detailed energetic analysis of our experiments. A comparison shows that the ARL Monopod with its 125 W average power consumption is more energy efficient than previously built robots.
••03 May 2010
TL;DR: This paper equips BigDog with a laser scanner, stereo vision system, and perception and navigation algorithms, and uses these sensors and algorithms to perform autonomous navigation to goal positions in unstructured forest environments.
Abstract: BigDog is a four legged robot with exceptional rough-terrain mobility. In this paper, we equip BigDog with a laser scanner, stereo vision system, and perception and navigation algorithms. Using these sensors and algorithms, BigDog performs autonomous navigation to goal positions in unstructured forest environments. The robot perceives obstacles, such as trees, boulders, and ground features, and steers to avoid them on its way to the goal. We describe the hardware and software implementation of the navigation system and summarize performance. During field tests in unstructured wooded terrain, BigDog reached its goal position 23 of 26 runs and traveled over 130 meters at a time without operator involvement.