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1. Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms

   https://doi.org/10.3390/app11073115  

   Albani, Dario ; Hönig, Wolfgang ; Nardi, Daniele ; Ayanian, Nora ; Trianni, Vito ( April 2021 , Applied Sciences)

   null (Ed.)

   Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures. 

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2. Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

   https://doi.org/10.1049/itr2.12327  

   Valiente, Rodolfo ; Raftari, Arash ; Mahjoub, Hossein Nourkhiz ; Razzaghpour, Mahdi ; Mahmud, Syed K. ; Fallah, Yaser P. ( January 2023 , IET Intelligent Transport Systems)

   Vehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

    

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3. Aerial Flight Paths for Communication

   https://doi.org/10.3389/frobt.2021.719154  

   Bevins, Alisha ; Duncan, Brittany A. ( December 2021 , Frontiers in Robotics and AI)

   This article presents an understanding of naive users’ perception of the communicative nature of unmanned aerial vehicle (UAV) motions refined through an iterative series of studies. This includes both what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area prioritized gestures from participants to the vehicle or augmenting the vehicle with additional communication modalities, rather than communicating without clear definitions of the states attempting to be conveyed. In an attempt to elicit more concrete states and better understand specific motion perception, this work includes multiple iterations of state creation, flight path refinement, and label assignment. The lessons learned in this work will be applicable broadly to those interested in defining flight paths, and within the human-robot interaction community as a whole, as it provides a base for those seeking to communicate using non-anthropomorphic robots. We found that the Negative Attitudes towards Robots Scale (NARS) can be an indicator of how a person is likely to react to a UAV, the emotional content they are likely to perceive from a message being conveyed, and it is an indicator for the personality characteristics they are likely to project upon the UAV. We also see that people commonly associate motions from other non-verbal communication situations onto UAVs. Flight specific recommendations are to use a dynamic retreating motion from a person to encourage following, use a perpendicular motion to their field of view for blocking, simple descending motion for landing, and to use either no motion or large altitude changes to encourage watching. Overall, this research explores the communication from the UAV to the bystander through its motion, to see how people respond physically and emotionally. 

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4. Safe Bipedal Path Planning via Control Barrier Functions for Polynomial Shape Obstacles Estimated Using Logistic Regression

   https://doi.org/10.1109/ICRA48891.2023.10160671  

   Peng, Chengyang ; Donca, Octavian ; Castillo, Guillermo ; Hereid, Ayonga ( May 2023 , IEEE)

   Safe path planning is critical for bipedal robots to operate in safety-critical environments. Common path planning algorithms, such as RRT or RRT*, typically use geometric or kinematic collision check algorithms to ensure collision-free paths toward the target position. However, such approaches may generate non-smooth paths that do not comply with the dynamics constraints of walking robots. It has been shown that the control barrier function (CBF) can be integrated with RRT/RRT* to synthesize dynamically feasible collision-free paths. Yet, existing work has been limited to simple circular or elliptical shape obstacles due to the challenging nature of constructing appropriate barrier functions to represent irregularly shaped obstacles. In this paper, we present a CBF-based RRT* algorithm for bipedal robots to generate a collision-free path through space with multiple polynomial-shaped obstacles. In particular, we used logistic regression to construct polynomial barrier functions from a grid map of the environment to represent irregularly shaped obstacles. Moreover, we developed a multi-step CBF steering controller to ensure the efficiency of free space exploration. The proposed approach was first validated in simulation for a differential drive model, and then experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with randomly placed obstacles. 

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5. Optimization-Based Robot Team Exploration Considering Attrition and Communication Constraints

   https://doi.org/10.1109/IROS51168.2021.9636029  

   Schack, Matthew ; Rogers, John ; Han, Qi ; Dantam, Neil ( January 2021 , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Exploring robots may fail due to environmental hazards. Thus, robots need to account for the possibility of failure to plan the best exploration paths. Optimizing expected utility enables robots to find plans that balance achievable reward with the inherent risks of exploration. Moreover, when robots rendezvous and communicate to exchange observations, they increase the probability that at least one robot is able to return with the map. Optimal exploration is NP-hard, so we apply a constraint-based approach to enable highly-engineered solution techniques. We model exploration under the possibility of robot failure and communication constraints as an integer, linear program and a generalization of the Vehicle Routing Problem. Empirically, we show that for several scenarios, this formulation produces paths within 50% of a theoretical optimum and achieves twice as much reward as a baseline greedy approach. 

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