Disturbance Detection, Identification, and Recovery by Gait Transition in Legged Robots

We present a framework for detecting, identifying, and recovering within stride from faults and other leg contact disturbances encountered by a walking hexapedal robot. Detection is achieved by means of a software contactevent sensor with no additional sensing hardware beyond the commercial actuators’ standard shaft encoders. A simple finite state machine identifies disturbances as due either to an expected ground contact, a missing ground contact indicating leg fault, or an unexpected “wall” contact. Recovery proceeds as necessary by means of a recently developed topological gait transition coordinator. We demonstrate the efficacy of this system by presenting preliminary data arising from two reactive behaviors — wall avoidance and leg-break recovery. We believe that extensions of this framework will enable reactive behaviors allowing the robot to function with guarded autonomy under widely varying terrain and self-health conditions

A fixed-parameter tractable algorithm for combinatorial filter reduction

What is the minimal information that a robot must retain to achieve its task? To design economical robots, the literature dealing with reduction of combinatorial filters approaches this problem algorithmically. As lossless state compression is NP-hard, prior work has examined, along with minimization algorithms, a variety of special cases in which specific properties enable efficient solution. Complementing those findings, this paper refines the present understanding from the perspective of parameterized complexity. We give a fixed-parameter tractable algorithm for the general reduction problem by exploiting a transformation into minimal clique covering. The transformation introduces new constraints that arise from sequential dependencies encoded within the input filter -- some of these constraints can be repaired, others are treated through enumeration. Through this approach, we identify parameters affecting filter reduction that are based upon inter-constraint couplings (expressed as a notion of their height and width), which add to the structural parameters present in the unconstrained problem of minimal clique covering.Comment: 8 pages, 4 figure

A Tendon-Driven Origami Hopper Triggered by Proprioceptive Contact Detection

We report on experiments with a laptop-sized (0.23m, 2.53kg), paper origami robot that exhibits highly dynamic and stable two degree-of-freedom (circular boom) hopping at speeds in excess of 1.5 bl/s (body-lengths per second) at a specific resistance O(1) while achieving aerial phase apex states 25% above the stance height over thousands of cycles. Three conventional brushless DC motors load energy into the folded paper springs through pulley-borne cables whose sudden loss of tension upon touchdown triggers the release of spring potential that accelerates the body back through liftoff to flight with a 20W powerstroke, whereupon the toe angle is adjusted to regulate fore-aft speed. We also demonstrate in the vertical hopping mode the transparency of this actuation scheme by using proprioceptive contact detection with only motor encoder sensing. The combination of actuation and sensing shows potential to lower system complexity for tendon-driven robots. For more information: Kod*lab (link to kodlab.seas.upenn.edu

Orienting Deformable Polygonal Parts without Sensors

Parts orienting is an important part of automated manufacturing. Sensorless manipulation has proven to be a useful paradigm in addressing parts orienting, and the manipulation of deformable objects is a growing area of interest. Until now, these areas have remained separate because existing orienting approaches utilize forces that if applied to deformable parts violate the assumptions used by existing algorithms, and could potentially break the part. We introduce a new algorithm and manipulator actions that, when provided with the geometric description and a deformation model of choice for the part, exploits the deformation and generates a Plan that consists of the shortest sequence of manipulator actions guaranteed to orient the part up to symmetry from any unknown initial orientation and pose. Additionally, the algorithm estimates whether a given manipulator is sufficiently precise to perform the actions which guarantee the final orientation. This is dictated by the particular part geometry, deformation model, and the manipulator action path planner which contains simple end-effector constraints and any standard motion planner. We illustrate the success of the algorithm with multiple parts through 192 trials of experiments that were performed with low-precision robot manipulators and six parts made of four types of materials. The experimental trials resulted in 154 successes, which show the feasibility of deformable parts orienting. The analysis of the failures showed that for success the assumptions of zero friction are essential for this work, increased manipulator precision would be beneficial but not necessary, and a simple deformation model can be sufficient. Finally, we note that the algorithm has applications to truly sensorless manipulation of non-deformable parts

What is Robotics: Why Do We Need It and How Can We Get It?

Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Learning in behavioural robotics

The research described in this thesis examines how machine learning mechanisms can be used in an assembly robot system to improve the reliability of the system and reduce the development workload, without reducing the flexibility of the system. The justification foi' this is that for a robot to be performing effectively it is frequently necessary to have gained experience of its performance under a particular configuration before that configuration can be altered to produce a performance improvement. Machine learning mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings. • The establishment of the action-sensor couplings which are necessary for the effective handling of uncertainty. • Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the coupling or the interaction of parameters and results, and from that model derive an appropriate learning mechanism that will find a parametrisation for that model that will enable good performance to be obtained. From this starting point it has been possible to show how equal, or better performance can be obtained by using iearning mechanisms which are neither derived from nor require a model of the task being learned. Instead, by combining iteration and a task specific profit function it is possible to use a generic behavioural module based on a learning mechanism to achieve the task.Iteration and a task specific profit function can also be used to learn which behavioural module from a pool of equally competent modules is the best at any one time to use to achieve a particular goal. Like the other two kinds of learning, this successfully automates an otherwise difficult test and evaluation process that would have to be performed by a developer. In doing so effectively, it, like the other learning that has been used here, shows that instead of being a peripheral issue to be introduced to a working system, learning, carried out in the right way, can be instrumental in the production of that working system

Autonomy in the real real-world: A behaviour based view of autonomous systems control in an industrial product inspection system

The thesis presented in this dissertation appears in two sequential parts that arose from an exploration of the use of Behaviour Based Artificial Intelligence (BBAI) techniques in a domain outside that of robotics, where BBAI is most frequently used. The work details a real-world physical implementation of the control and interactions of an industrial product inspection system from a BBAI perspective. It concentrates particularly on the control of a number of active laser scanning sensor systems (each a subsystem of a larger main inspection system), using a subsumption architecture. This industrial implementation is in itself a new direction for BBAI control and an important aspect of this thesis. However, the work has also led on to the development of a number of key ideas which contribute to the field of BBAI in general. The second part of the thesis concerns the nature of physical and temporal constraints on a distributed control system and the desirability of utilising mechanisms to provide continuous, low-level learning and adaptation of domain knowledge on a sub-behavioural basis. Techniques used include artificial neural networks and hill-climbing state-space search algorithms. Discussion is supported with examples from experiments with the laser scanning inspection system. Encouraging results suggest that concerted design effort at this low level of activity will benefit the whole system in terms of behavioural robustness and reliability. Relevant aspects of the design process that should be of value in similar real-world projects are identified and emphasised. These issues are particularly important in providing a firm foundation for artificial intelligence based control systems