Motion planning in dynamic environment using maneuverability maps

When a robot manipulator performs tasks in a dynamic unstructured environment, it not only should account for where and how it should move, but also ensure that the robot can effectively avoid collisions with static and moving obstacles. Planning in absolutely unstructured and dynamic environment is much more challenging, up-to-date there is no universal solution to this problem. In this paper, we believed the concept that for motion planning in real dynamic environments, usually there is only a part of a path invalidated by obstacles, and only this part needs replanning. Thus, in this paper a practical strategy was presented for motion planning in dynamic environments. First, a variant dynamic Rapidly-exploring Random Tree (RRT) planner was designed to combine global planning with local replanning. Then, the proposed dynamic RRT algorithm was implemented under Robotic Operation System (ROS) framework and applied to a 7-degree-of-freedom manipulator. Experimental results have shown that the proposed dynamic RRT planner can plan and execute movement of robot manipulator between a starting and target position while preserving task constraints and reacting to environment changes in real time.

The problem of collision-free motion planning in dynamic environments has received considerable attention in computational geometry. In this thesis, we study the point robot motion planning problems among dynamic obstacles. A growing disc is a disc obstacle that grows over time. A transient obstacle is an obstacle that appears at a particular time in the environment and may disappear at some later time.

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 show that it also efficiently 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.

Service robots have a great potential of improving human quality of life by aiding in everyday tasks. However, robots that share an environment and interact with humans still face some challenges that limits their acceptance. One of these challenges is how to move and behave among groups of people, which is a task performed seamlessly by humans and some animals. Motion planning in dynamic environments has been addressed mostly by predicting future position of humans and avoiding them. However, with the increase of the number of persons in such environments, techniques that are based only on the prediction of the movement of humans can fail, as they usually ignore the human's reaction to the presence of the robot. Instead of trying to model the complex human motion behavior, this work proposes to rely on humans to guide the robot through difficult situations, where modern approaches would fail to find a solution. This will be accomplished by a probabilistic approach for selecting a human leader, according to the robot's desired destination. In this way, the robot can take advantage of the humans' paths and behavior, effortlessly avoiding dynamic and static features together with the human leader, relieving the robot from the burden of having to generate its own path in difficult situations.

In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a “Hierarchical Deep Deterministic Policy Gradient (HDDPG)” has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. However, unlike others, the HDDPG enables sharing an identical environment and state among workers and managers, while a unique reward system is required for each Deep Deterministic Policy Gradient (DDPG) agent. Therefore, the HDDPG allows easy structural expansion with probabilistic action selection of a worker by the manager. Due to its innate structural advantage, the HDDPG has a merit in building a general AI to deal with a complex time-horizon tasks with various conflicting sub-goals. The experimental results demonstrated its usefulness with a manipulator motion planning problem in a dynamic environment, where path planning and collision avoidance conflict each other. The proposed HDDPG is compared with an HAM and a single DDPG for performance evaluation. The result shows that the HDDPG demonstrated more than 40% of reward gain and more than two times the reward improvement rate. Another important feature of the proposed HDDPG is the biased manager training capability. By adding a preference factor to each worker, the manager can be trained to prefer a certain worker to achieve better success rate for a specific objective if needed.

動的環境下におけるロボットの動作生成

A robot in a dynamic environment needs to avoid static obstacles and perform motion planning while considering dynamic obstacles. This paper presents a Configuration-time-space (C-time-space), in which time-series information is added to the configuration space (C-space). The C-time-space is represented by the mapping position and joint displacement of the robot at each time in the generalized coordinate space. Motion planning using the C-time-space can generate a trajectory that avoids dynamic obstacles. The experimental evaluation results indicate that compared to the conventional method, the trajectory distance of the robot arm can be shortened in environments where dynamic objects exist using the proposed method.

In a human–robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners —This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality.

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

Predictive motion planning is a key for achieving energy-efficient driving, which is one of the major visions of automated driving nowadays. Motion planning is a challenging task, especially in the presence of other dynamic traffic participants. Two main issues have to be addressed. First, for globally optimal driving, the entire trip has to be considered at once. Second, the movement of other traffic participants is usually not known in advance. Both issues lead to increased computational effort. The length of the prediction horizon is usually large and the problem of unknown future movement of other traffic participants usually requires frequent replanning. This work proposes a novel motion planning approach for vehicles operating in dynamic environments. The above-mentioned problems are addressed by splitting the planning into a strategic planning part and situation-dependent replanning part. Strategic planning is done without considering other dynamic participants and is reused later in order to lower the computational effort during replanning phase.

Trajectory planning in dynamic environments can be decomposed into two sub-problems: 1) planning a path to avoid static obstacles, 2) then planning a speed profile to avoid dynamic obstacles. This is also called path-speed decomposition. In this work, we present a novel approach to solve the first sub-problem, motion planning with static obstacles. From an optimization perspective, motion planning for autonomous vehicles can be viewed as non-convex constrained nonlinear optimization, which requires a good enough initial guess to start and is often sensitive to algorithm parameters. We formulate motion planning as convex spline optimization. The convexity of the formulated problem makes it able to be solved fast and reliably, while guaranteeing a global optimum. We then reorganize the constrained spline optimization into a recurrent formulation, which further reduces the computational time to be linear in the optimization horizon size. The proposed method can be applied to both trajectory generation and motion planning problems. Its effectiveness is demonstrated in challenging scenarios such as tight lane changes and sharp turns.

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

A central challenging problem in humanoid robotics is to plan and execute dynamic tasks in dynamic environments. Given that the environment is known, sampling-based online motion planners are best suited for handling changing environments. However, without learning strategies, each task still has to be planned from scratch, preventing these algorithms from getting closer to realtime performance. This paper proposes a novel learning-based motion planning algorithm for addressing this issue. Our algorithm, called the attractor guided planner (AGP), extends existing motion planners in two simple but important ways. First, it extracts significant attractor points from successful paths in order to reuse them as guiding landmarks during the planning of new similar tasks. Second, it relies on a task comparison metric for deciding when previous solutions should be reused for guiding the planning of new tasks. The task comparison metric takes into account the task specification and as well environment features which are relevant to the query. Several experiments are presented with different humanoid reaching examples in the presence of randomly moving obstacles. Our results show that the AGP greatly improves both the planning time and solution quality, when comparing to traditional sampling-based motion planners.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

A new fuzzy potential field is presented for autonomous mobile robot motion planning with dynamic environments. Two fuzzy models have been used to accomplish the total attractive and repulsive forces acting on the mobile robot. The proposed fuzzy potential field motion planning was investigated based on several conducted simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations the proposed approach was able to provide the robot with collision free path to softly land on the moving target and solved the local minimum problem within any stationary or dynamic environment compared to other potential field based approaches.