Category Archives: Robotics

A ROS module that improves real-time aspects of network communication among distributed ROS machines, and a nice analysis of wireless network characteristics and limitations

Danilo Tardioli, Ramviyas Parasuraman, Petter Ögren, Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks, Robotics and Autonomous Systems, Volume 111, 2019, Pages 73-87, DOI: 10.1016/j.robot.2018.10.009.

The Robot Operating System (ROS) is a popular and widely used software framework for building robotics systems. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes presents several issues regarding limited-bandwidth, delays, and jitter, when used in wireless multi-hop networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance by reducing delay and jitter in data exchanges, especially in multi-hop networks. Pound allows the use of multiple ROS masters (roscores), features data compression, and importantly, introduces a priority scheme that allows favoring more important flows over less important ones. We compare Pound to the state-of-the-art solutions through extensive experiments and show that it performs equally well, or better in all the test cases, including a control-over-network example.

A novel paradigm for motion planning based on probabilistic inference

Mukadam, M., Dong, J., Yan, X., Dellaert, F., & Boots, B. , Continuous-time Gaussian process motion planning via probabilistic inference, The International Journal of Robotics Research, 37(11), 1319–1340, DOI: 10.1177/0278364918790369.

We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and GP interpolation. We call this algorithm the Gaussian Process Motion Planner (GPMP). We then detail how motion planning problems can be formulated as probabilistic inference on a factor graph. This forms the basis for GPMP2, a very efficient algorithm that combines GP representations of trajectories with fast, structure-exploiting inference via numerical optimization. Finally, we extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan when conditions change. We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments. Our evaluation reveals that GPMP2 is several times faster than previous algorithms while retaining robustness. We also benchmark iGPMP2 on replanning problems, and show that it can find successful solutions in a fraction of the time required by GPMP2 to replan from scratch.

Interesting close-loop detection for robot SLAM that only uses odometry and topology

Rohou, S., Franek, P., Aubry, C., & Jaulin, L. , Proving the existence of loops in robot trajectories, The International Journal of Robotics Research, DOI: 10.1177/0278364918808367.

In this paper we present a reliable method to verify the existence of loops along the uncertain trajectory of a robot, based on proprioceptive measurements only, within a bounded-error context. The loop closure detection is one of the key points in simultaneous localization and mapping (SLAM) methods, especially in homogeneous environments with difficult scenes recognitions. The proposed approach is generic and could be coupled with conventional SLAM algorithms to reliably reduce their computing burden, thus improving the localization and mapping processes in the most challenging environments such as unexplored underwater extents. To prove that a robot performed a loop whatever the uncertainties in its evolution, we employ the notion of topological degree that originates in the field of differential topology. We show that a verification tool based on the topological degree is an optimal method for proving robot loops. This is demonstrated both on datasets from real missions involving autonomous underwater vehicles and by a mathematical discussion.

Perpetual power for small robots in a swarm

Farshad Arvin, Simon Watson, Ali Emre Turgut, Jose Espinosa, Tomáš Krajník, Barry Lennox, Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging, Journal of Intelligent & Robotic Systems, December 2018, Volume 92, Issue 3–4, pp 395–412, DOI: 10.1007/s10846-017-0673-8.

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.

A new model of reinforcement learning based on the human brain that copes with continuous spaces through continuous rewards, with a short but nice state-of-the-art of RL applied to large, continuous spaces

Feifei Zhao, Yi Zeng, Guixiang Wang, Jun Bai, Bo Xu, A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations, Cognitive Computation, Volume 10, Issue 2, pp 296–306, DOI: 10.1007/s12559-017-9511-3.

Decision making is a fundamental ability for intelligent agents (e.g., humanoid robots and unmanned aerial vehicles). During decision making process, agents can improve the strategy for interacting with the dynamic environment through reinforcement learning. Many state-of-the-art reinforcement learning models deal with relatively smaller number of state-action pairs, and the states are preferably discrete, such as Q-learning and Actor-Critic algorithms. While in practice, in many scenario, the states are continuous and hard to be properly discretized. Better autonomous decision making methods need to be proposed to handle these problems. Inspired by the mechanism of decision making in human brain, we propose a general computational model, named as prefrontal cortex-basal ganglia (PFC-BG) algorithm. The proposed model is inspired by the biological reinforcement learning pathway and mechanisms from the following perspectives: (1) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in prefrontal cortex. (2) We maintain the contextual reward information in working memory. This has a top-down biasing effect on reinforcement learning in basal ganglia. The proposed model separates the continuous states into smaller distinguishable states, and introduces continuous reward function for each state to obtain reward information at different time. To verify the performance of our model, we apply it to many UAV decision making experiments, such as avoiding obstacles and flying through window and door, and the experiments support the effectiveness of the model. Compared with traditional Q-learning and Actor-Critic algorithms, the proposed model is more biologically inspired, and more accurate and faster to make decision.

SLAM as a sampling problem, with some references to the signal sampling state-of-the-art

Golnoosh Elhami, et. al Sampling at Unknown Locations: Uniqueness and Reconstruction Under Constraints, IEEE Transactions on Signal Processing, Vol 66 no. 22, DOI: 10.1109/TSP.2018.2872019.

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is often hopeless. For example, we recently showed that, for polynomial and bandlimited signals, it is possible to find two signals, arbitrarily far from each other, that fit the measurements. However, we also showed that this can be overcome by adding constraints to the sample positions. In this paper, we show that these constraints lead to a uniform sampling of a composite of functions. Furthermore, the formulation retains the key aspects of the SLAM and SfM problems, whilst providing uniqueness, in many cases. We demonstrate this by studying two simple examples of constrained sampling at unknown locations. In the first, we consider sampling a periodic bandlimited signal composite with an unknown linear function. We derive the sampling requirements for uniqueness and present an algorithm that recovers both the bandlimited signal and the linear warping. Furthermore, we prove that, when the requirements for uniqueness are not met, the cases of multiple solutions have measure zero. For our second example, we consider polynomials sampled such that the sampling positions are constrained by a rational function. We previously proved that, if a specific sampling requirement is met, uniqueness is achieved. In addition, we present an alternate minimization scheme for solving the resulting non-convex optimization problem. Finally, fully reproducible simulation results are provided to support our theoretical analysis.

Sharing beliefs (pdfs) between human and robot

Rina Tse, Mark Campbell, Human–Robot Communications of Probabilistic Beliefs via a Dirichlet Process Mixture of Statements, IEEE Transactions on Robotics, vol. 34, no. 5, DOI: 10.1109/TRO.2018.2830360.

This paper presents a natural framework for information sharing in cooperative tasks involving humans and robots. In this framework, all information gathered over time by a human–robot team is exchanged and summarized in the form of a fused probability density function (pdf). An approach for an intelligent system to describe its belief pdfs in English expressions is presented. This belief expression generation is achieved through two goodness measures: semantic correctness and information preservation. In order to describe complex, multimodal belief pdfs, a Mixture of Statements (MoS) model is proposed such that optimal expressions can be generated through compositions of multiple statements. The model is further extended to a nonparametric Dirichlet process MoS generation, such that the optimal number of statements required for describing a given pdf is automatically determined. Results based on information loss, human collaborative task performances, and correctness rating scores suggest that the proposed method for generating belief expressions is an effective approach for communicating probabilistic information between robots and humans.

Automatic design of a robot to perform given tasks with an optimal configuration

Sehoon Ha et al., Computational Design of Robotic Devices From High-Level Motion Specifications, IEEE Transactions on Robotics, vol. 34, no. 5, DOI: 10.1109/TRO.2018.2830419.

We present a novel computational approach to design the robotic devices from high-level motion specifications. Our computational system uses a library of modular components—actuators, mounting brackets, and connectors—to define the space of possible robot designs. The process of creating a new robot begins with a set of input trajectories that specify how its end effectors and/or body should move. By searching through the combinatorial set of possible arrangements of modular components, our method generates a functional, as-simple-as-possible robotic device that is capable of tracking the input motion trajectories. To significantly improve the efficiency of this discrete optimization process, we propose a novel heuristic that guides the search for appropriate designs. Briefly, our heuristic function estimates how much an intermediate robot design needs to change before it becomes able to execute the target motion trajectories. We demonstrate the effectiveness of our computational design method by automatically creating a variety of robotic manipulators and legged robots. To generate these results, we define our own robotic kit that includes off-the-shelf actuators and 3-D printable connectors. We validate our results by fabricating two robotic devices designed with our method.

On the need to replanning in POMDPs when applied to real systems, due to imperfect sensing and computational cost of online planning

Ali-akbar Agha-mohammadi et al., SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space, IEEE Transactions on Robotics, vol. 34, no. 5, DOI: 10.1109/TRO.2018.2838556.

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Interesting study of the number of optimal points in SLAM, considering it as a non-linear, non-convex optimization problem

Heng Wang, Shoudong Huang, Guanghong Yang, Gamini Dissanayake, Comparison of two different objective functions in 2D point feature SLAM, Automatica,
Volume 97, 2018, Pages 172-181, DOI: 10.1016/j.automatica.2018.08.009.

This paper compares two different objective functions in 2D point feature Simultaneous Localization and Mapping (SLAM). It is shown that the objective function can have a significant impact on the convergence of the iterative optimization techniques used in SLAM. When Frobenius norm is adopted for the error term of the orientation part of odometry, the SLAM problem has much better convergence properties, as compared with that using the angle difference as the error term. For one-step case, we have proved that there is one and only one minimum to the SLAM problem, and strong duality always holds. For two-step case, strong duality always holds except when three very special conditions hold simultaneously (which happens with probability zero), thus the global optimal solution to primal SLAM problem can be obtained by solving the corresponding Lagrangian dual problem in most cases. Further, for arbitrary m-step cases, we also show using examples that much better convergence results can be obtained. Simulation examples are given to demonstrate the different convergence properties using two different objective functions.