Modern applications of mobile robots require them to have the ability to safely and effectively navigate in human environments. New challenges arise when these robots must plan their motion in a human-aware fashion. Current methods addressing this problem have focused mainly on the activity forecasting aspect, aiming at improving predictions without considering the active nature of the interaction, i.e. the robot’s effect on the environment and consequent issues such as reciprocity. Furthermore, many methods rely on computationally expensive offline training of predictive models that may not be well suited to rapidly evolving dynamic environments. This thesis presents a novel approach for enabling autonomous robots to navigate socially in environments with humans. Following formulations of the inverse planning problem, agents reason about the intentions of other agents and make predictions about their future interactive motion. A technique is proposed to implement counterfactual reasoning over a parametrised set of light-weight reciprocal motion models, thus making it more tractable to maintain beliefs over the future trajectories of other agents towards plausible goals. The speed of inference and the effectiveness of the algorithms is demonstrated via physical robot experiments, where computationally constrained robots navigate amongst humans in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as 100ms after the first observation. While intention inference is a key aspect of successful human-robot interaction, executing any task requires planning that takes into account the predicted goals and trajectories of other agents, e.g., pedestrians. It is well known that robots demonstrate unwanted behaviours, such as freezing or becoming sluggishly responsive, when placed in dynamic and cluttered environments, due to the way in which safety margins according to simple heuristics end up covering the entire feasible space of motion. The presented approach makes more refined predictions about future movement, which enables robots to find collision-free paths quickly and efficiently. This thesis describes a novel technique for generating "interactive costmaps", a representation of the planner’s costs and rewards across time and space, providing an autonomous robot with the information required to navigate socially given the estimate of other agents’ intentions. This multi-layered costmap deters the robot from obstructing while encouraging social navigation respectful of other agents’ activity. Results show that this approach minimises collisions and near-collisions, minimises travel times for agents, and importantly offers the same computational cost as the most common costmap alternatives for navigation. A key part of the practical deployment of such technologies is their ease of implementation and configuration. Since every use case and environment is different and distinct, the presented methods use online adaptation to learn parameters of the navigating agents during runtime. Furthermore, this thesis includes a novel technique for allocating tasks in distributed robotics systems, where a tool is provided to maximise the performance on any distributed setup by automatic parameter tuning. All of these methods are implemented in ROS and distributed as open-source. The ultimate aim is to provide an accessible and efficient framework that may be seamlessly deployed on modern robots, enabling widespread use of intention prediction for interactive navigation in distributed robotic systems.

Multi-agent path planning is applied in a wide range of applications in robotics and autonomous vehicles, including aerial vehicles such as drones and other unmanned aerial vehicles (UAVs), to solve tasks in areas like surveillance, search and rescue, and transportation. In today's rapidly evolving technology in the fields of automation and artificial intelligence, multi-agent path planning is growing increasingly more relevant. The main problems encountered in multi-agent path planning are collision avoidance with other agents, obstacle evasion, and pathfinding from a starting point to an endpoint. In this project, the objectives were to create intelligent agents capable of navigating through two-dimensional eight-agent cost map environments to a static target, while avoiding collisions with other agents and simultaneously minimizing the path cost. The method of reinforcement learning was used by utilizing the development platform Unity and the open-source ML-Agents toolkit that enables the development of intelligent agents with reinforcement learning inside Unity. Perlin Noise was used to generate the cost maps. The reinforcement learning algorithm Proximal Policy Optimization was used to train the agents. The training was structured as a curriculum with two lessons, the first lesson was designed to teach the agents to reach the target, without colliding with other agents or moving out of bounds. The second lesson was designed to teach the agents to minimize the path cost. The project successfully achieved its objectives, which could be determined from visual inspection and by comparing the final model with a baseline model. The baseline model was trained only to reach the target while avoiding collisions, without minimizing the path cost. A comparison of the models showed that the final model outperformed the baseline model, reaching an average of $27.6\%$ lower path cost. / Multi-agent-vägsökning används inom en rad olika tillämpningar inom robotik och autonoma fordon, inklusive flygfarkoster såsom drönare och andra obemannade flygfarkoster (UAV), för att lösa uppgifter inom områden som övervakning, sök- och räddningsinsatser samt transport. I dagens snabbt utvecklande teknik inom automation och artificiell intelligens blir multi-agent-vägsökning allt mer relevant. De huvudsakliga problemen som stöts på inom multi-agent-vägsökning är kollisioner med andra agenter, undvikande av hinder och vägsökning från en startpunkt till en slutpunkt. I detta projekt var målen att skapa intelligenta agenter som kan navigera genom tvådimensionella åtta-agents kostnadskartmiljöer till ett statiskt mål, samtidigt som de undviker kollisioner med andra agenter och minimerar vägkostnaden. Metoden förstärkningsinlärning användes genom att utnyttja utvecklingsplattformen Unity och Unitys open-source ML-Agents toolkit, som möjliggör utveckling av intelligenta agenter med förstärkningsinlärning inuti Unity. Perlin Brus användes för att generera kostnadskartorna. Förstärkningsinlärningsalgoritmen Proximal Policy Optimization användes för att träna agenterna. Träningen strukturerades som en läroplan med två lektioner, den första lektionen var utformad för att lära agenterna att nå målet, utan att kollidera med andra agenter eller röra sig utanför gränserna. Den andra lektionen var utformad för att lära agenterna att minimera vägkostnaden. Projektet uppnådde framgångsrikt sina mål, vilket kunde fastställas genom visuell inspektion och genom att jämföra den slutliga modellen med en basmodell. Basmodellen tränades endast för att nå målet och undvika kollisioner, utan att minimera vägen kostnaden. En jämförelse av modellerna visade att den slutliga modellen överträffade baslinjemodellen, och uppnådde en genomsnittlig $27,6\%$ lägre vägkostnad.