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The problem of minimizing cost in nonlinear control systems with uncertainties or disturbances remains a major challenge. Model predictive control (MPC), and in particular sampling-based MPC has recently shown great success in complex domains such as aggressive driving with highly nonlinear dynamics. Sampling-based methods rely on a prior distribution to generate samples in the first place. Obviously, the choice of this distribution highly influences efficiency of the controller. Existing approaches such as sampling around the control trajectory of the previous time step perform suboptimally, especially in multi-modal or highly dynamic settings. In this work, we therefore propose to learn models that generate samples in low-cost areas of the state-space, conditioned on the environment and on contextual information of the task to solve. By using generative models as an informed sampling distribution, our approach exploits guidance from the learned models and at the same time maintains robustness properties of the MPC methods. We use Conditional Variational Autoencoders (CVAE) to learn distributions that imitate samples from a training dataset containing optimized controls. An extensive evaluation in the autonomous navigation domain suggests that replacing previous sampling schemes with our learned models considerably improves performance in terms of path quality and planning efficiency.

Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics., ILIAD (EC H2020: 732737)

Finding optimal paths for self-driving cars in cluttered environments is one of the major challenges in autonomous driving. The complexity stems from the nonlinearity of the system and the non-convexity of the configuration space. This paper introduces a novel extend function called Hybrid Curvature (HC) Steer for sampling-based nonholonomic motion planning in tight environments. HC Steer solves the two-point boundary value problem by computing continuous curvature paths while the vehicle is going in one direction, but allows curvature discontinuities at switches in the driving direction. The resulting paths approximate Reeds-Shepp's paths and are directly drivable by an autonomous vehicle. BiRRT∗, an optimal sampling-based motion planner, is used to evaluate and compare HC Steer's performance to state of the art steering functions, namely Reeds-Shepp (RS) and Continuous Curvature (CC) Steer. Extensive experiments in challenging environments show HC Steer's advantage of computing smoother paths than RS Steer in equally tight environments and finding solutions with less direction switches, higher success rates, and shorter planning time than CC Steer.

RRT and RRT* have become popular planning techniques, in particular for high-dimensional systems such as wheeled robots with complex nonholonomic constraints. Their planning times, however, can scale poorly for such robots, which has motivated researchers to study hierarchical techniques that grow the RRT trees in more focused ways. Along this line, we introduce Theta*-RRT that hierarchically combines (discrete) any-angle search with (continuous) RRT motion planning for nonholonomic wheeled robots. Theta*-RRT is a variant of RRT that generates a trajectory by expanding a tree of geodesics toward sampled states whose distribution summarizes geometric information of the any-angle path. We show experimentally, for both a differential drive system and a high-dimensional truck-and-trailer system, that Theta*-RRT finds shorter trajectories significantly faster than four baseline planners (RRT, A*-RRT, RRT*, A*-RRT*) without loss of smoothness, while A*-RRT* and RRT* (and thus also Informed RRT*) fail to generate a first trajectory sufficiently fast in environments with complex nonholonomic constraints. We also prove that Theta*-RRT retains the probabilistic completeness of RRT for all small-time controllable systems that use an analytical steer function.

The distance metric is a key component in RRT-based motion planning that deeply affects coverage of the state space, path quality and planning time. With the goal to speed up planning time, we introduce a learning approach to approximate the distance metric for RRT-based planners. By exploiting a novel steer function which solves the two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking performance. In an extensive analysis we compare our approach to an Euclidean distance baseline, consider four alternative regression models and study the impact of domain-specific feature expansion. The learning approach is shown to be faster in planning time by several factors at negligible loss of path quality.

The ability to perceive and predict future positions of dynamic objects is essential for mobile robots and intelligent vehicles in dynamic environments. In this paper, we present a novel planning-based approach for long-term human motion prediction that accounts for local interactions and can accurately predict joint motion of multiple agents. Long-term predictions are handled using an MDP formulation that computes a set of stochastic motion policies. To obtain distributions over future motion trajectories, we sample the policies with a weighted random walk algorithm in which each person is locally influenced by social forces from other nearby agents. Unlike related work, the algorithm is environment-aware, can account for individual agent velocities, requires no training phase and makes joint predictions for multiple agents. Experiments in simulation and with real data show that our method makes more accurate predictions than two state-of-the-art methods in terms of probabilistic and geometrical performance measures.

Planning smooth trajectories is important for the safe, efficient and comfortable operation of mobile robots, such as wheeled robots moving in crowded environments or cars moving at high speed. Asymptotically optimal sampling-based motion planners can be used to generate such trajectories. However, to achieve the necessary efficiency for the realtime operation of robots, one often uses their initial feasible trajectories or the trajectories of non-optimal motion planners instead, typically after a post-smoothing step. We propose a gradient-informed post-smoothing algorithm, called GRIPS, that deforms given trajectories by locally optimizing the placement of vertices while satisfying the system's kinodynamic constraints. We show experimentally that GRIPS typically produces trajectories of significantly smaller length and higher smoothness than several existing post-smoothing algorithms.

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments on a robot with 12 states show that it also scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments., Comment: Accepted by ICRA2020. This version is extended for readability and completeness. Additional content would have exceeded page limit