The concept of Artificial Intelligence has gained a lot of attention over the last decade. In particular, AI-based tools have been employed in several scenarios and are, by now, pervading our everyday life. Nonetheless, most of these systems lack many capabilities that we would naturally consider to be included in a notion of "intelligence". In this work, we present an architecture that, inspired by the cognitive theory known as Thinking Fast and Slow by D. Kahneman, is tasked with solving planning problems in different settings, specifically: classical and multi-agent epistemic. The system proposed is an instance of a more general AI paradigm, referred to as SOFAI (for Slow and Fast AI). SOFAI exploits multiple solving approaches, with different capabilities that characterize them as either fast or slow, and a metacognitive module to regulate them. This combination of components, which roughly reflects the human reasoning process according to D. Kahneman, allowed us to enhance the reasoning process that, in this case, is concerned with planning in two different settings. The behavior of this system is then compared to state-of-the-art solvers, showing that the newly introduced system presents better results in terms of generality, solving a wider set of problems with an acceptable trade-off between solving times and solution accuracy.

As autonomous agents enter complex environments, it becomes more difficult to adequately model the interactions between the two. Agents must therefore cope with greater ambiguity (e.g., unknown environments, underdefined models, and vague problem definitions). Despite the consequences of ignoring ambiguity, tools for decision making under ambiguity are understudied. The general approach has been to avoid ambiguity (exploit known information) using robust methods. This work contributes ambiguity attitude graph search (AAGS), generalizing robust methods with ambiguity attitudes--the ability to trade-off between seeking and avoiding ambiguity in the problem. AAGS solves online decision making problems with limited budget to learn about their environment. To evaluate this approach AAGS is tasked with path planning in static and dynamic environments. Results demonstrate that appropriate ambiguity attitudes are dependent on the quality of information from the environment. In relatively certain environments, AAGS can readily exploit information with robust policies. Conversely, model complexity reduces the information conveyed by individual samples; this allows the risks taken by optimistic policies to achieve better performance.

Online collision-free trajectory generation within a shared workspace is fundamental for most multi-robot applications. However, many widely-used methods based on model predictive control (MPC) lack theoretical guarantees on the feasibility of underlying optimization. Furthermore, when applied in a distributed manner without a central coordinator, deadlocks often occur where several robots block each other indefinitely. Whereas heuristic methods such as introducing random perturbations exist, no profound analyses are given to validate these measures. Towards this end, we propose a systematic method called infinite-horizon model predictive control with deadlock resolution. The MPC is formulated as a convex optimization over the proposed modified buffered Voronoi with warning band. Based on this formulation, the condition of deadlocks is formally analyzed and proven to be analogous to a force equilibrium. A detection-resolution scheme is proposed, which can effectively detect deadlocks online before they even happen. Once detected, it utilizes an adaptive resolution scheme to resolve deadlocks, under which no stable deadlocks can exist under minor conditions. In addition, the proposed planning algorithm ensures recursive feasibility of the underlying optimization at each time step under both input and model constraints, is concurrent for all robots and requires only local communication. Comprehensive simulation and experiment studies are conducted over large-scale multi-robot systems. Significant improvements on success rate are reported, in comparison with other state-of-the-art methods and especially in crowded and high-speed scenarios.

Non-prehensile manipulation methods usually use a simple end effector, e.g., a single rod, to manipulate the object. Compared to the grasping method, such an end effector is compact and flexible, and hence it can perform tasks in a constrained workspace; As a trade-off, it has relatively few degrees of freedom (DoFs), resulting in an under-actuation problem with complex constraints for planning and control. This paper proposes a new non-prehensile manipulation method for the task of object retrieval in cluttered environments, using a rod-like pusher. Specifically, a candidate trajectory in a cluttered environment is first generated with an improved Rapidly-Exploring Random Tree (RRT) planner; Then, a Model Predictive Control (MPC) scheme is applied to stabilize the slider's poses through necessary contact with obstacles. Different from existing methods, the proposed approach is with the contact-aware feature, which enables the synthesized effect of active removal of obstacles, avoidance behavior, and switching contact face for improved dexterity. Hence both the feasibility and efficiency of the task are greatly promoted. The performance of the proposed method is validated in a planar object retrieval task, where the target object, surrounded by many fixed or movable obstacles, is manipulated and isolated. Both simulation and experimental results are presented.

We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more plausible and robust to detector failures compared to state-of-the-art methods: (i) it is unsupervised as it does not need any training sessions. (ii) During the exploration, a probability distribution on the 2D floor map is updated according to an intuitive mechanism, while an improved belief update increases the effectiveness of the agent's exploration. (iii) We incorporate the awareness that an object detector may fail into the aforementioned probability modelling by exploiting the success statistics of a specific detector. Our solution is dubbed POMP-BE-PD (Pomcp-based Online Motion Planning with Belief by Exploration and Probabilistic Detection). It uses the current pose of an agent and an RGB-D observation to learn an optimal search policy, exploiting a POMDP solved by a Monte-Carlo planning approach. On the Active Vision Database benchmark, we increase the average success rate over all the environments by a significant 35% while decreasing the average path length by 4% with respect to competing methods. Thus, our results are state-of-the-art, even without using any training procedure.

In this paper, we deal with the problem of full-body path planning for walking robots. The state of walking robots is defined in multi-dimensional space. Path planning requires defining the path of the feet and the robot's body. Moreover, the planner should check multiple constraints like static stability, self-collisions, collisions with the terrain, and the legs' workspace. As a result, checking the feasibility of the potential path is time-consuming and influences the performance of a planning method. In this paper, we verify the feasibility of sampling-based planners in the path planning task of walking robots. We identify the strengths and weaknesses of the existing planners. Finally, we propose a new planning method that improves the performance of path planning of legged robots.

In large unknown environments, search operations can be much more time-efficient with the use of multi-robot fleets by parallelizing efforts. This means robots must efficiently perform collaborative mapping (exploration) while simultaneously searching an area for victims (coverage). Previous simultaneous mapping and planning techniques treat these problems as separate and do not take advantage of the possibility for a unified approach. We propose a novel exploration-coverage planner which bridges the mapping and search domains by growing sets of random trees rooted upon a pose graph produced through mapping to generate points of interest, or tasks. Furthermore, it is important for the robots to first prioritize high information tasks to locate the greatest number of victims in minimum time by balancing coverage and exploration, which current methods do not address. Towards this goal, we also present a new multi-robot task allocator that formulates a notion of a hierarchical information heuristic for time-critical collaborative search. Our results show that our algorithm produces 20% more coverage efficiency, defined as average covered area per second, compared to the existing state-of-the-art. Our algorithms and the rest of our multi-robot search stack is based in ROS and made open source

This paper addresses a new semantic multi-robot planning problem in uncertain and dynamic environments. Particularly, the environment is occupied with non-cooperative, mobile, uncertain labeled targets. These targets are governed by stochastic dynamics while their current and future positions as well as their semantic labels are uncertain. Our goal is to control mobile sensing robots so that they can accomplish collaborative semantic tasks defined over the uncertain current/future positions and labels of these targets. We express these tasks using Linear Temporal Logic (LTL). We propose a sampling-based approach that explores the robot motion space, the mission specification space, as well as the future configurations of the labeled targets to design optimal paths. These paths are revised online to adapt to uncertain perceptual feedback. To the best of our knowledge, this is the first work that addresses semantic mission planning problems in uncertain and dynamic semantic environments. We provide extensive experiments that demonstrate the efficiency of the proposed method

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The PGA Tour is making major changes to its schedule and how several of its events are played as LIV Golf's second season gets underway. The PGA Tour ratified a motion Tuesday that reduces fields for eight designated events in 2024 to between 70 and 80 golfers with no 36-hole cuts. The tour has not announced which events will be affected, but the majors, the FedEx Cup Playoffs and the Players Championship will not be included in the changes.

Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special considerations for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planning graph is assigned learned motion costs by a neural network trained in simulation to minimize traversal time and limit the risk of failure. Our method achieves real-time performance with a bounded computation time. We present extensive experimental results gathered during the Finals event of the DARPA Subterranean Challenge, where this method contributed to team CERBERUS winning the competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of autonomous operation without a single planning or locomotion failure.