A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration

We are interested in the problem of time-optimal control of omnidirectional robots with bounded acceleration (TOC-ORBA). While there exist approximate solutions for such robots, and exact solutions with unbounded acceleration, exact solvers to the TOC-ORBA problem have remained elusive until now. In this paper, we present a real-time solver for true time-optimal control of omnidirectional robots with bounded acceleration. We first derive the general parameterized form of the solution to the TOC-ORBA problem by application of Pontryagin's maximum principle. We then frame the boundary value problem of TOC-ORBA as an optimization problem over the parametrized control space. To overcome local minima and poor initial guesses to the optimization problem, we introduce a two-stage optimal control solver (TSOCS): The first stage computes an upper bound to the total time for the TOC-ORBA problem and holds the time constant while optimizing the parameters of the trajectory to approach the boundary value conditions. The second stage uses the parameters found by the first stage, and relaxes the constraint on the total time to solve for the parameters of the complete TOC-ORBA problem. We further implement TSOCS as a closed loop controller to overcome actuation errors on real robots in real-time. We empirically demonstrate the effectiveness of TSOCS in simulation and on real robots, showing that 1) it runs in real time, generating solutions in less than 0.5ms on average; 2) it generates faster trajectories compared to an approximate solver; and 3) it is able to solve TOC-ORBA problems with non-zero final velocities that were previously unsolvable in real-time

Comparison Between Genetic Fuzzy Methodology and Q-Learning for Collaborative Control Design

A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method

Effective Task Transfer Through Indirect Encoding

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain

Collaborative decision making in uncertain environments

Two major issues in the design of multi-robot systems are those of communication and co-ordination. Communication ithin real world environments cannot always be guaranteed. A multi-robot system must, therefore, be able to continue with its task in the absence of communication between team members. Co-ordination of multiple robots to perform a specific task involves team members being able to make decisions as a single entity and as a member of a team. The co-ordination needs to be robust enough to handle failures within the system and unknown phenomena within the environment. In this thesis, the problems of communication and co-ordination are discussed and a new type of multi-robot system is introduced in an effort to solve the inherent difficulties within communication and co-ordination of multi-robot systems. The co-ordination and communication strategy is based upon the concept of sharing potential field information within dynamic local groups. Each member of the multi-robot system creates their own potential field based upon individual sensor readings. Team members that are dynamically assigned to local groups share their individual potential fields, in order to create a combined potential field which reduces the effect of sensor noise. It is because of this, that team members are able to make better decisions. A number of experiments, both in simulation and in laboratory environments, are presented. These experiments compare the performance of the system against a nonsharing control and a hybrid system made up of a global path planner and a reactive motor controller. It is demonstrated that the new system significantly outperforms these other methods in a search type problem. From this, it is concluded that the novel system proposed in this thesis successfully tackled the search problem, and that it should also be possible for the system to be applied to a number of other common multi-robot problems

Collaborative decision making in uncertain environments

Two major issues in the design of multi-robot systems are those of communication and co-ordination. Communication ithin real world environments cannot always be guaranteed. A multi-robot system must, therefore, be able to continue with its task in the absence of communication between team members. Co-ordination of multiple robots to perform a specific task involves team members being able to make decisions as a single entity and as a member of a team. The co-ordination needs to be robust enough to handle failures within the system and unknown phenomena within the environment. In this thesis, the problems of communication and co-ordination are discussed and a new type of multi-robot system is introduced in an effort to solve the inherent difficulties within communication and co-ordination of multi-robot systems. The co-ordination and communication strategy is based upon the concept of sharing potential field information within dynamic local groups. Each member of the multi-robot system creates their own potential field based upon individual sensor readings. Team members that are dynamically assigned to local groups share their individual potential fields, in order to create a combined potential field which reduces the effect of sensor noise. It is because of this, that team members are able to make better decisions. A number of experiments, both in simulation and in laboratory environments, are presented. These experiments compare the performance of the system against a nonsharing control and a hybrid system made up of a global path planner and a reactive motor controller. It is demonstrated that the new system significantly outperforms these other methods in a search type problem. From this, it is concluded that the novel system proposed in this thesis successfully tackled the search problem, and that it should also be possible for the system to be applied to a number of other common multi-robot problems

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Facing homelessness

In facing homelessness we face the other, and in facing the other, we face ourselves. This book contributes to an emerging body of knowledge on street homelessness in the South African context. It is meant for researchers and scholars who are committed to finding solutions for street homelessness. It offers conceptual frameworks and practical guidelines for a liberative and transformative response to homelessness. It brings together authors from a wide range of disciplines, fusing the rigour of researchers, the vision of activists and the lived experience of practitioners. In this volume, the causes of street homelessness in South Africa today, and its different faces, are traced. It critiques singular solutions, and interrogates the political, institutional and moral failures that contribute to the systemic exclusion of homeless persons and other vulnerable populations from society. It proposes rights-based interventions as part of a radical re-imagination of how street homelessness can be ended, one person and one neighbourhood at a time. The analysis by the authors steer in the direction of new ways of doing and being that could demonstrate concrete, viable and sustainable alternatives to the exclusionary realities faced by homeless persons. It argues for solution-based approaches, aimed at radical forms of social inclusion and achieved through broad-based and creative collaborations by all spheres of society. In the face and presence of street homelessness – as one expression of urban vulnerability and deep socio-economic inequality – society is confronted with a clear political, institutional, moral and personal obligation. This volume calls for a reclamation of community in its most inclusionary, life-affirming and interdependent sense, asserting that we truly are well because of others, and we are unwell if others are. It is a call to reclaim our common humanity in the context of inclusive communities where all are equally welcome and bestowed with dignity and honour