This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

We present a new method for planning footstep placements for a robot walking on uneven terrain with obstacles, using a mixed-integer quadratically-constrained quadratic program (MIQCQP). Our approach is unique in that it handles obstacle avoidance, kinematic reachability, and rotation of footstep placements, which typically have required non-convex constraints, in a single mixed-integer optimization that can be efficiently solved to its global optimum. Reachability is enforced through a convex inner approximation of the reachable space for the robot's feet. Rotation of the footsteps is handled by a piecewise linear approximation of sine and cosine, designed to ensure that the approximation never overestimates the robot's reachability. Obstacle avoidance is ensured by decomposing the environment into convex regions of obstacle-free configuration space and assigning each footstep to one such safe region. We demonstrate this technique in simple 2D and 3D environments and with real environments sensed by a humanoid robot. We also discuss computational performance of the algorithm, which is currently capable of planning short sequences of a few steps in under one second or longer sequences of 10–30 footsteps in tens of seconds to minutes on common laptop computer hardware. Our implementation is available within the Drake MATLAB toolbox [1].

/pdf/footstep-planning-on-uneven-terrain-with-mixed-integer-56svn7so7g.pdf

Footstep planning on uneven terrain with mixed-integer convex optimization

The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

/pdf/robotics-cyber-security-vulnerabilities-attacks-40d9b2dkuj.pdf

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations.

International Conference on Control, Automation, Robotics and Vision

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

/pdf/a-review-of-subsequence-time-series-clustering-80itis2vph.pdf

A review of subsequence time series clustering.

Algorithms on sequences of symbols have been studied for a long time and now form a fundamental part of computer science. One of the very important problems in analysis of sequences is the longest common subsequence problem. For the general case of an arbitrary number of input sequences, the problem is NP-hard. We describe an approach to solve this problem. This approach is based on constructing a logical model for the longest common subsequence problem.

/pdf/the-longest-common-subsequence-problem-9q8wajenuk.pdf

The Longest Common Subsequence Problem

In recent years among developers of robotic software formed a direction of the development of individual solvers. Such solvers are designed for specific hard problems. In this paper we consider an approach to design of an efficient solver for the problem of placement of visual landmarks.

/pdf/on-the-problem-of-placement-of-visual-landmarks-3qk1n28fxv.pdf

On the problem of placement of visual landmarks

In this paper we describe an approach to solve the problem of sensor placement. Our approach is based on constructing logical models for considered problem.

/pdf/the-problem-of-sensor-placement-xvrmt75tn3.pdf

The Problem of Sensor Placement

In this paper we consider an approach to solve the problem of selection of a minimal set of landmarks. Our approach is based on constructing intelligent algorithms to solve logical models for the problem.

Computational Experiments for the Problem of Selection of a Minimal Set of Visual Landmarks

Robots with anticipatory behavior has the possibility of dealing with novel situations more effectively than a system without such ability. In this paper we consider an anticipation system for simple robot navigation. This system based on artificial physics optimization. PACS: 07.05.Mh

/pdf/the-force-law-design-of-artificial-physics-optimization-for-5vng6zp671.pdf

The Force Law Design of Artificial Physics Optimization for Robot Anticipation of Motion

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