Online Adaptation of Sampling-Based Motion Planning with Inaccurate Models

Robotic manipulation relies on analytical or learned models to simulate the system dynamics. These models are often inaccurate and based on offline information, so that the robot planner is unable to cope with mismatches between the expected and the actual behavior of the system (e.g., the presence of an unexpected obstacle). In these situations, the robot should use information gathered online to correct its planning strategy and adapt to the actual system response. We propose a sampling-based motion planning approach that uses an estimate of the model error and online observations to correct the planning strategy at each new replanning. Our approach adapts the cost function and the sampling bias of a kinodynamic motion planner when the outcome of the executed transitions is different from the expected one (e.g., when the robot unexpectedly collides with an obstacle) so that future trajectories will avoid unreliable motions. To infer the properties of a new transition, we introduce the notion of context-awareness, i.e., we store local environment information for each executed transition and avoid new transitions with context similar to previous unreliable ones. This is helpful for leveraging online information even if the simulated transitions are far (in the state-and-action space) from the executed ones. Simulation and experimental results show that the proposed approach increases the success rate in execution and reduces the number of replannings needed to reach the goal.

OpenMORE: an open-source tool for sampling-based path replanning in ROS

With the spread of robots in unstructured, dynamic environments, the topic of path replanning has gained importance in the robotics community. Although the number of replanning strategies has significantly increased, there is a lack of agreed-upon libraries and tools, making the use, development, and benchmarking of new algorithms arduous. This paper introduces OpenMORE, a new open-source ROS-based C++ library for sampling-based path replanning algorithms. The library builds a framework that allows for continuous replanning and collision checking of the traversed path during the execution of the robot trajectory. Users can solve replanning tasks exploiting the already available algorithms and can easily integrate new ones, leveraging the library to manage the entire execution.

Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning

Kinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This paper presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering transitions from sequential runs of kinodynamic RRT and a bandit algorithm decides what region to sample at each timestep. The paper demonstrates the approach on several simulated examples as well as a 7-degree-of-freedom manipulation task with dynamics uncertainty, suggesting that the approach finds better solutions faster and leads to a higher success rate in execution.

Spatio-Temporal Avoidance of Predicted Occupancy in Human-Robot Collaboration

This paper addresses human-robot collaboration (HRC) challenges of integrating predictions of human activity to provide a proactive-n-reactive response capability for the robot. Prior works that consider current or predicted human poses as static obstacles are too nearsighted or too conservative in planning, potentially causing delayed robot paths. Alternatively, time-varying prediction of human poses would enable robot paths that avoid anticipated human poses, synchronized dynamically in time and space. Herein, a proactive path planning method, denoted STAP, is presented that uses spatiotemporal human occupancy maps to find robot trajectories that anticipate human movements, allowing robot passage without stopping. In addition, STAP anticipates delays from robot speed restrictions required by ISO/TS 15066 speed and separation monitoring (SSM). STAP also proposes a sampling-based planning algorithm based on RRT* to solve the spatio-temporal motion planning problem and find paths of minimum expected duration. Experimental results show STAP generates paths of shorter duration and greater average robot-human separation distance throughout tasks. Additionally, STAP more accurately estimates robot trajectory durations in HRC, which are useful in arriving at proactive-n-reactive robot sequencing.

Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

Hiding task-oriented programming complexity: an industrial case study

The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROS. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users.

Accelerating sampling-based optimal path planning via adaptive informed sampling

This paper improves the performance of RRT*-like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy that accounts for the cost progression regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling). The paper proves that the resulting algorithm is asymptotically optimal. Furthermore, its convergence rate is superior to that of state-of-the-art path planners, such as Informed-RRT*, both in simulations and manufacturing case studies. An open-source ROS-compatible implementation is also released.

Anytime informed path re-planning and optimization for robots in changing environments

In this paper, we propose a path re-planning algorithm that makes robots able to work in scenarios with moving obstacles. The algorithm switches between a set of pre-computed paths to avoid collisions with moving obstacles. It also improves the current path in an anytime fashion. The use of informed sampling enhances the search speed. Numerical results show the effectiveness of the strategy in different simulation scenarios.