Search-based Planning for a Legged Robot over Rough Terrain

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains

X-RHex: A Highly Mobile Hexapedal Robot for Sensorimotor Tasks

We report on the design and development of X-RHex, a hexapedal robot with a single actuator per leg, intended for real-world mobile applications. X-RHex is an updated version of the RHex platform, designed to offer substantial improvements in power, run-time, payload size, durability, and terrain negotiation, with a smaller physical volume and a comparable footprint and weight. Furthermore, X-RHex is designed to be easier to build and maintain by using a variety of commercial off-the-shelf (COTS) components for a majority of its internals. This document describes the X-RHex architecture and design, with a particular focus on the new ability of this robot to carry modular payloads as a laboratory on legs. X-RHex supports a variety of sensor suites on a small, mobile robotic platform intended for broad, general use in research, defense, and search and rescue applications. Comparisons with previous RHex platforms are presented throughout, with preliminary tests indicating that the locomotive capabilities of X-RHex can meet or exceed the previous platforms. With the additional payload capabilities of X-RHex, we claim it to be the first robot of its size to carry a fully programmable GPU for fast, parallel sensor processing

Topological Learning for Robotic Exploration and Navigation in Uncertain Environments

The effectiveness of robot autonomy is governed by the ability to make decisions based on online sensor measurements and a prior belief of the environment. Uncertainty in the environment introduces challenges to robotic decision making. This thesis address two key robot decision making problems: exploration and navigation. The robotic exploration problem requires a robot to maximally observe an unknown environment. A key challenge of robotic exploration is dealing with prior map uncertainty that arises due to partial knowledge of the world at the beginning of the mission. Since the map is unknown, it is difficult to determine which action gains the most information. The robot navigation problem is to determine a sequence of states in a given environment to move the robot from a start to goal state while avoiding collisions. Sensors mounted on real robots have measurement noise which leads to uncertainty in the mapping of obstacles. This uncertainty in obstacle location makes it challenging to find collision free paths. Real-world environments consist of interesting topological structures such as rooms, corridors, connections, intersections, and loops. These topological structures can be exploited to address uncertainty in the environment. Computational topology methods can be utilized to robustly extract environment topology. In this thesis, we resolve robot decision-making challenges due to environment uncertainty by leveraging topological methods that are robust to noise. We first address prior map uncertainty by predicting unknown regions of subterranean environments. Our method involves a convolutional neural network and a novel loss that combines image inpainting features and topological features via persistent homology. Simulation results on four real mines and a dataset of procedurally-generated networks show our algorithm can exploit subtle topological structural cues in subterranean environments for efficient exploration. We then address sensor measurement uncertainty by constructing a topologically accurate roadmap from a probabilistic occupancy map.We propose an unsupervised topological learning technique with biased sampling in topologically important regions. Simulation results show a robot can search a collision-free and cost-effective path over the topological roadmap in low runtime. The proposed topological learning is a significant contribution to the two key problems of robot decision making. Our experimental results show a robot can successfully learn to exploit topological structures for online exploration of subterranean environments.Further we show that through topological biased sampling, a robot can construct a topologically accurate roadmap for robot navigation tasks

A Stability-Estimator to Unify Humanoid Locomotion: Walking, Stair-Climbing and Ladder-Climbing

The field of Humanoid robotics research has often struggled to find a unique niche that is not better served by other forms of robot. Unlike more traditional industrials robots with a specific purpose, a humanoid robot is not necessarily optimized for any particular task, due to the complexity and balance issues of being bipedal. However, the versatility of a humanoid robot may be ideal for applications such as search and rescue. Disaster sites with chemical, biological, or radiation contamination mean that human rescue workers may face untenable risk. Using a humanoid robot in these dangerous circumstances could make emergency response faster and save human lives. Despite the many successes of existing mobile robots in search and rescue, stair and ladder climbing remains a challenging task due to their form. To execute ladder climbing motions effectively, a humanoid robot requires a reliable estimate of stability. Traditional methods such as Zero Moment Point are not applicable to vertical climbing, and do not account for force limits imposed on end-effectors. This dissertation implements a simple contact wrench space method using a linear combination of contact wrenches. Experiments in simulation showed ZMP equivalence on flat ground. Furthermore, the estimator was able to predict stability with four point contact on a vertical ladder. Finally, an extension of the presented method is proposed based on these findings to address the limitations of the linear combination.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Evolutionary Legged Robotics

Due to the technological advance, robotic systems become more and more interesting for industrial and home applications. Popular examples are given by robotic lawn mower, robot vacuum cleaner, and package drones. Beside the toy industry, legged robots are not as popular, although they have some clear advantages compared to wheeled systems. With their flexibility concerning the locomotion, they are able to adapt their walking pattern to different environments. For instance they can walk over obstacles and gaps or climb over rubble and stairs. Another possible advantage could be a redundancy for locomotion. A faulty motor in one limb could be compensated by other motors in the kinematic chain. As well, multiple failing legs can be compensated by an adapted walking pattern. Compared to this, the more complex mechatronic systems represent a major challenge to the construction and the control. This thesis is dedicated to the control of complex walking robots. Genetic algorithms are applied to generate walking patterns for different robots. The evolutionary development of walking patterns is done in a simulation software. Results of various approaches are transferred and tested on existing systems which have been developed at RIC/DFKI. Different robotic systems are used to evaluate the generality of the applied methods. Eventually, a method is developed that can be utilized, with a few system specific modifications, for a variety of legged robots. As basis for the development and investigation of several methods, software tools are designed to generalize the application of applying genetic algorithms to legged locomotion. These tools include a simulation environment, a behavior representation, a genetic algorithm and a learning and benchmark framework. The simulation environment is adapted to the behavior of real robotic systems via reference experiments. In addition, the simulation is extended by a foot contact model for loose surfaces. The evaluation of the genetic algorithm is done on several benchmark problems and compared to three existing algorithms. This thesis contributes to the state of the art in many areas. The developed methodology can easily be applied to several complex robotic systems due to its transferability. The genetic algorithm and the hierarchical behavior representation provide a new opportunity to control the generation of the offspring in an evolutionary process. In addition, the developed software tools are an important contribution for their respective research fields