Actively Mapping Industrial Structures In the context of robotics, active perceptual planning refers to exploration by a mobile robot equipped with sensors to conduct a survey of an object or environment of interest. It can be of assistance for the regular inspection and monitoring of remote or dangerous facilities such as offshore platforms. Active mapping has been investigated for many applications, such as inspection and virtual modelling, and on many platforms, such as aerial, wheeled and underwater robots. However, the online deployment of such a system on a real robot still remains a challenge. In recent years, there have been significant advances in quadruped mobility and hardware reliability, and the first industrial prototypes are being tested on live industrial facilities.  Quadrupeds can cover the same terrain as wheeled or tracked robots but can also cross mobility hazards and climb stairs. While UAVs are being actively deployed for these kinds of missions, [...]
Visual-Inertial-Kinematic Odometry for Legged Robots (VILENS) This blog post provides an overview of our recent ICRA 2020 paper Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry: [bibtex key="2020ICRA_wisth"] This is one paper in a series of works on state estimation described here. Introduction Many algorithms for mobile robotics rely on one crucial piece of information - Where is the robot? Mobile robots rely on having an accurate location estimate for control, motion planning, navigation, and many other tasks. For example, when legged robots attempt to walk down stairs very precise foot placement is required - a error of just a few centimetres can send the robot tumbling down to the bottom! (click image for animation) The process of using sensor inputs to determine the robot's location is known as state estimation. Legged robots, in particular, have relatively strict requirements for state [...]
Planning for Multiple Robots in Congested Environments When most of us plan journeys, chances are at some point we open Google Maps to find an array of colours telling us that we will probably experience traffic at certain points on our journey. This allows us to plan our journeys accordingly, possibly choosing to take a longer route with less traffic. This begs the question: If we can plan our journeys to avoid congested areas, why can’t robots? This blog post provides an overview of our recent paper ‘Multi-Robot Planning Under Uncertainty with Congestion-Aware Models’ which addresses this problem: [bibtex key="2020AAMAS_street"] What do we want to do? Multi-robot systems are now deployed widely in warehouse logistics, agriculture and on our roads. Commonly, we wish to solve multi-robot path planning problems in these environments, where each robot has a goal location to [...]
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques Path planning is the problem of finding a continuous sequence of valid states from a start to a goal specification. Popular approaches in robotics include graph-based searches, such as A* , and sampling-based planners, such as Rapidly-exploring Random Trees (RRT) . Both graph- and sampling-based approaches have characteristic strengths and weaknesses. Advanced BIT* (ABIT*) continues previous work  to combine these strengths and mitigate these weaknesses using a unified planning paradigm. ABIT* achieves this by viewing the planning problem as the two subproblems of approximation and search. This perspective allows ABIT* to use advanced graph-search techniques on an anytime sampling-based approximation to quickly find initial solutions and almost-surely asymptotically converge to the global optimum. https://www.youtube.com/watch?v=VFdihv8Lq2A Full details of ABIT* can be found in the paper (https://arxiv.org/abs/2002.06589). Approximation ABIT* approximates the state space of a planning problem by sampling multiple batches of [...]
Learning from demonstration and reinforcement learning have been applied to many difficult problems in sequential decision making and control. In most settings, it is assumed that the demonstrations available are fixed. In this work, we consider learning from demonstration in the context of shared autonomy...