In urban environments like Amsterdam, where road congestion and stress on infrastructure are critical issues, the city’s waterways remain underutilized for transportation. Autonomous Surface Vessels (ASVs), making waterway transport cheaper and less labor intensive, present a potential solution by reducing transportation costs and alleviating road traffic. Although companies are developing ASVs, current solutions are mainly limited to larger, less congested waterways, with further innovation needed for denser urban areas.

The challenges in autonomous navigation for ASVs in urban canals stem from the complex nonlinear dynamics of vessels, which require long-term planning and rapid responses to environmental changes. Urban waterways are narrow and unstructured, with loosely defined navigation rules that can lead to discontinuities in the motion planner’s cost function. Typically, motion planners rely on predictions of other agents’ movements and plan around them, resulting in no interaction awareness. This approach can lead to the freezing robot problem in dense environments, where the ASV halts, deeming all the space unsafe. A local motion planner must address these challenges by ensuring long-horizon interaction-aware motions, rule adherence, and real-time planning.

An emerging approach in autonomous navigation is a sampling-based Model Predictive Control (MPC) strategy known as Model Predictive Path Integral control (MPPI). This algorithm approximates the optimal control sequence by sampling from a continuous input distribution. Thanks to its gradient-free nature, MPPI only requires collision checking to avoid collision and allows discontinuous cost functions. Additionally, its computation speed remains largely unaffected by the complexity of the robots’ nonlinear dynamics, enabling longer planning horizons. While this approach has grown in popularity in the motion planning community, its application in dynamic environments with multiple interacting agents remains relatively unexplored.

Building on the state-of-the-art in MPPI, this thesis first introduces an Interaction-Aware Model Predictive Path Integral (IA-MPPI) controller tailored for motion planning in crowded urban canals. While conventional planners passively react to other agents, IA-MPPI actively predicts and plans cooperatively in real-time, addressing the freezing robot problem and handling discontinuous navigation rules. The decentralized, communication-free architecture assumes agent cooperation and employs a two-stage sample evaluation to enhance efficiency. This approach manages nonlinear dynamics, exact obstacle shapes, and longer planning horizons, demonstrating robustness and efficiency compared to state-of-the-art MPC in multi-agent environments.

While IA-MPPI uses a constant velocity model for predicting other agents’ hidden goals, Chapter 4 of this thesis introduces a learning-based trajectory prediction model trained on realistic artificial data to improve accuracy in crowded environments. By extracting the agents’ goals from predicted trajectories and integrating this information into the IA-MPPI controller, the planner’s performance increases significantly in environments with tight interactions. Moreover, this approach outperforms treating the predicted trajectory as occupied space, leading to safer navigation in dense urban canals.

One of MPPI’s main drawbacks is that it struggles in highly dynamic environments because it usually only samples around a previous plan. When rapid changes occur, such as strong disturbances or an obstacle unexpectedly cutting off the path, the previous plan becomes invalid, and all the generated samples may lead to collisions. To address this, Chapter 5 introduces Biased-MPPI, incorporating multiple classic and learning-based ancillary controllers into the sampling distribution. While the mathematical derivations show that this introduces a bias, this significantly improves reactivity, safety, and robustness to local minima. Additionally, this approach reduces the required samples, making the controller more efficient regarding computational demand.

Lastly, Chapter 6 leverages the gradient-free nature of MPPI and applies it to a different domain involving contact-rich manipulation tasks, which are notoriously difficult for model-based planning. With a parallelizable GPU-based physics engine like IsaacGym, MPPI can efficiently roll out hundreds of input sequences in parallel, faster than in real-time, to approximate optimal control. This approach eliminates the need for explicit modeling of contact dynamics by exploiting the models embedded in the simulator, making it particularly suited for manipulation tasks like pushing and picking with both prehensile and non-prehensile manipulators. Experiments with real robots demonstrate the effectiveness of this method in handling discontinuous, contact-heavy environments.

Overall, this thesis explores key aspects of motion planning, particularly for ASVs, with extensions to ground robots and mobile manipulators. It advances the MPPI framework for autonomous navigation by adapting it for multi-agent systems and introducing biases through classic and learning-based ancillary controllers. Additionally, the thesis compares MPPI to MPC in navigation experiments and to optimization fabrics in manipulation tasks. The work presented promotes the use of MPPI, especially in domains where complex nonlinear dynamics and discontinuous costs complicate the use of real-time optimization-based MPC.

Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex motion planning problems. However, the performance of MPPI relies heavily on the choice of sampling distribution. Existing literature often uses the previously computed input sequence as the mean of a Gaussian distribution for sampling, leading to potential failures and local minima. We propose a novel derivation of MPPI that allows for arbitrary sampling distributions to enhance efficiency, robustness, and convergence while alleviating the problem of local minima. We present an efficient importance sampling scheme that combines classical and learning-based ancillary controllers simultaneously, resulting in more informative sampling and control fusion. Several simulated and real-world demonstrate the validity of our approach.

Motion planning for autonomous robots in tight, interaction-rich, and mixed human-robot environments is challenging. State-of-the-art methods typically separate prediction and planning, predicting other agents’ trajectories first and then planning the ego agent’s motion in the remaining free space. However, agents’ lack of awareness of their influence on others can lead to the freezing robot problem. We build upon Interaction-Aware Model Predictive Path Integral (IAMPPI) control and combine it with learning-based trajectory predictions, thereby relaxing its reliance on communicated short-term goals for other agents. We apply this framework to Autonomous Surface Vessels (ASVs) navigating urban canals. By generating an artificial dataset in real sections of Amsterdam’s canals, adapting and training a prediction model for our domain, and proposing heuristics to extract local goals, we enable effective cooperation in planning. Our approach improves autonomous robot navigation in complex, crowded environments, with potential implications for multi-agent systems and human-robot interaction. available at: autonomousrobots.nl/pubpage/IA_MPPI_LBM.html